revamp: ai analytics

prisma/migrations/20260511235500_ai_analytics_events/migration.sql

@@ -0,0 +1,31 @@
+CREATE TABLE "AiAnalyticsEvent" (
+    "id" TEXT NOT NULL,
+    "storeId" TEXT NOT NULL,
+    "conversationId" TEXT,
+    "messageId" TEXT,
+    "eventType" TEXT NOT NULL,
+    "source" TEXT NOT NULL,
+    "productId" TEXT,
+    "variantId" TEXT,
+    "quantity" INTEGER,
+    "amount" DOUBLE PRECISION,
+    "currency" TEXT,
+    "sentiment" TEXT,
+    "resolutionStatus" TEXT,
+    "orderId" TEXT,
+    "attributionType" TEXT,
+    "metadata" JSONB,
+    "createdAt" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP,
+
+    CONSTRAINT "AiAnalyticsEvent_pkey" PRIMARY KEY ("id")
+);
+
+ALTER TABLE "AiAnalyticsEvent"
+ADD CONSTRAINT "AiAnalyticsEvent_storeId_fkey"
+FOREIGN KEY ("storeId") REFERENCES "Store"("id") ON DELETE CASCADE ON UPDATE CASCADE;
+
+CREATE INDEX "AiAnalyticsEvent_storeId_eventType_createdAt_idx" ON "AiAnalyticsEvent"("storeId", "eventType", "createdAt");
+CREATE INDEX "AiAnalyticsEvent_storeId_createdAt_idx" ON "AiAnalyticsEvent"("storeId", "createdAt");
+CREATE INDEX "AiAnalyticsEvent_conversationId_idx" ON "AiAnalyticsEvent"("conversationId");
+CREATE INDEX "AiAnalyticsEvent_productId_idx" ON "AiAnalyticsEvent"("productId");
+CREATE INDEX "AiAnalyticsEvent_orderId_idx" ON "AiAnalyticsEvent"("orderId");

prisma/schema.prisma

@@ -109,7 +109,9 @@ model Store {
 
   // Customer-facing storefront widget (floating chat) enablement.
   storefrontChatEnabled       Boolean @default(false)
-  /** After OTP verify, widget session length (minutes). Clamped server-side (e.g. 5–10080). Default 60. */
+  /**
+   * After OTP verify, widget session length (minutes). Clamped server-side (e.g. 5–10080). Default 60.
+   */
   storefrontOtpSessionMinutes Int     @default(60)
   storefrontChatName          String?
   storefrontChatTagline       String?
@@ -117,21 +119,35 @@ model Store {
   storefrontChatFontFamily    String?
   storefrontChatLogoUrl       String?
 
-  /** Primary knowledge source: structured questionnaire vs uploaded documents (mutually exclusive). */
+  /**
+   * Primary knowledge source: structured questionnaire vs uploaded documents (mutually exclusive).
+   */
   knowledgeBaseMode KnowledgeBaseMode @default(UNSET)
-  /** Saved structured KB answers (version, per-field text, misc). Embedded as STRUCTURED chunks. */
+  /**
+   * Saved structured KB answers (version, per-field text, misc). Embedded as STRUCTURED chunks.
+   */
   knowledgeTemplate Json?
 
-  /** When true, L1 may use DB-backed category/product metafield tools for customer answers. */
+  /**
+   * When true, L1 may use DB-backed category/product metafield tools for customer answers.
+   */
   categoryMetadetailsEnabled Boolean @default(false)
-  /** Per-module AI enablement, e.g. { "FAQ": true, "ORDER_STATUS": true }. */
+  /**
+   * Per-module AI enablement, e.g. { "FAQ": true, "ORDER_STATUS": true }.
+   */
   aiModules                  Json?
-  /** FAQ-style policy text per L1 module for embeddings, e.g. { "ORDER_STATUS": "..." }. */
+  /**
+   * FAQ-style policy text per L1 module for embeddings, e.g. { "ORDER_STATUS": "..." }.
+   */
   modulePolicies             Json?
-  /** Vendor Type-1 answers keyed by shared_field_id (sf_*); one value per key store-wide (§4.2). */
+  /**
+   * Vendor Type-1 answers keyed by shared_field_id (sf_*); one value per key store-wide (§4.2).
+   */
   sharedFieldAnswers         Json?
 
-  /** Optional Gmail auto-reply HTML theming (colors, font, logo URL). Merged with defaults server-side. */
+  /**
+   * Optional Gmail auto-reply HTML theming (colors, font, logo URL). Merged with defaults server-side.
+   */
   gmailReplyTheme Json?
 
   /**
@@ -163,6 +179,7 @@ model Store {
   gmailInboundAutoReplies          GmailInboundAutoReply[]
   templates                        Template[]
   chatMessages                     ChatMessage[]
+  aiAnalyticsEvents                AiAnalyticsEvent[]
   lowStockAlertStates              LowStockAlertState[]
   lowStockShopifyVendorAlertStates LowStockShopifyVendorAlertState[]
   supplierAutomationLogs           SupplierAutomationLog[]
@@ -173,7 +190,9 @@ model Store {
   @@index([userId]) // Add index for performance
 }
 
-/** Dedupes low-stock B2B notifications per store + product + supplier while supplier-reported qty stays below threshold. */
+/**
+ * Dedupes low-stock B2B notifications per store + product + supplier while supplier-reported qty stays below threshold.
+ */
 model LowStockAlertState {
   id                  String          @id @default(cuid())
   storeId             String
@@ -182,7 +201,9 @@ model LowStockAlertState {
   supplierId          String
   supplier            SupplierProfile @relation(fields: [supplierId], references: [id], onDelete: Cascade)
   lastSeenSupplierQty Int?
-  /** Set when we sent a low-stock alert; cleared when supplier qty is back >= threshold. */
+  /**
+   * Set when we sent a low-stock alert; cleared when supplier qty is back >= threshold.
+   */
   lastNotifiedBelowAt DateTime?
 
   createdAt DateTime @default(now())
@@ -193,14 +214,18 @@ model LowStockAlertState {
   @@index([supplierId])
 }
 
-/** Dedupes Shopify low-stock vendor notifications per store + product while total stays below threshold. */
+/**
+ * Dedupes Shopify low-stock vendor notifications per store + product while total stays below threshold.
+ */
 model LowStockShopifyVendorAlertState {
   id                   String    @id @default(cuid())
   storeId              String
   store                Store     @relation(fields: [storeId], references: [id], onDelete: Cascade)
   shopifyProductId     String
   lastSeenShopifyTotal Int?
-  /** Set when we created a vendor notification; cleared when Shopify total is back >= threshold. */
+  /**
+   * Set when we created a vendor notification; cleared when Shopify total is back >= threshold.
+   */
   lastNotifiedBelowAt  DateTime?
 
   createdAt DateTime @default(now())
@@ -241,7 +266,9 @@ model CachedProduct {
   status           String?
   variants         Json // Store variants as JSON
   images           Json // Store images as JSON
-  /** Shopify product metafields (incl. category / taxonomy fields from admin) for automation & search. */
+  /**
+   * Shopify product metafields (incl. category / taxonomy fields from admin) for automation & search.
+   */
   metafields       Json?
   createdAt        DateTime @default(now())
   updatedAt        DateTime @updatedAt
@@ -263,7 +290,9 @@ model CachedOrder {
   currency              String?
   financialStatus       String?
   fulfillmentStatus     String?
-  /** When fulfillment is ON_HOLD: machine reason (see SUPPLIER_HOLD_REASON_CODES). */
+  /**
+   * When fulfillment is ON_HOLD: machine reason (see SUPPLIER_HOLD_REASON_CODES).
+   */
   holdReasonCode        String?
   holdNote              String?   @db.Text
   trackingNumber        String?
@@ -299,7 +328,9 @@ model CachedCustomer {
   totalSpent        String?
   phone             String?
   tags              String?
-  /** Shopify REST `default_address` snapshot for vendor UI (city, country, etc.). */
+  /**
+   * Shopify REST `default_address` snapshot for vendor UI (city, country, etc.).
+   */
   defaultAddress    Json?
   createdAt         DateTime  @default(now())
   updatedAt         DateTime  @updatedAt
@@ -358,9 +389,9 @@ model Connection {
 
   createdAt             DateTime               @default(now())
   updatedAt             DateTime               @updatedAt
+  supplierProductOffers SupplierProductOffer[]
 
   @@unique([storeId, supplierId])
-  supplierProductOffers SupplierProductOffer[]
 }
 
 model Invitation {
@@ -514,7 +545,9 @@ model Notification {
   @@index([createdAt])
 }
 
-/** One row per inbound Gmail message id that received an auto-reply (idempotency). */
+/**
+ * One row per inbound Gmail message id that received an auto-reply (idempotency).
+ */
 model GmailInboundAutoReply {
   id             String   @id @default(cuid())
   userId         String
@@ -687,7 +720,9 @@ model ChatMessage {
   id         String          @id @default(cuid())
   senderId   String
   receiverId String
-  /** Vendor store this thread belongs to (B2B JUNO CHAT). Null = legacy messages before per-store threading. */
+  /**
+   * Vendor store this thread belongs to (B2B JUNO CHAT). Null = legacy messages before per-store threading.
+   */
   storeId    String?
   kind       ChatMessageKind @default(TEXT)
   content    String          @db.Text
@@ -716,17 +751,27 @@ model StorefrontChatConversation {
   visitorId     String
   customerEmail String?
 
-  /** Email the customer entered for OTP (asserted order email). */
+  /**
+   * Email the customer entered for OTP (asserted order email).
+   */
   assertedEmail       String?
   emailVerifiedAt     DateTime?
-  /** Verified session TTL (e.g. 1h from verification). */
+  /**
+   * Verified session TTL (e.g. 1h from verification).
+   */
   sessionExpiresAt    DateTime?
-  /** After order disambiguation, pinned Shopify order id for this session. */
+  /**
+   * After order disambiguation, pinned Shopify order id for this session.
+   */
   boundShopifyOrderId String?
-  /** SHA-256 hex of OTP + store-visitor salt; cleared after verify. */
+  /**
+   * SHA-256 hex of OTP + store-visitor salt; cleared after verify.
+   */
   otpHash             String?
   otpExpiresAt        DateTime?
-  /** L1 orchestrator: phase, module, slots (JSON). */
+  /**
+   * L1 orchestrator: phase, module, slots (JSON).
+   */
   l1State             Json?
 
   createdAt DateTime @default(now())
@@ -751,6 +796,33 @@ model StorefrontChatMessage {
   @@index([conversationId, createdAt])
 }
 
+model AiAnalyticsEvent {
+  id               String   @id @default(cuid())
+  storeId          String
+  store            Store    @relation(fields: [storeId], references: [id], onDelete: Cascade)
+  conversationId   String?
+  messageId        String?
+  eventType        String
+  source           String
+  productId        String?
+  variantId        String?
+  quantity         Int?
+  amount           Float?
+  currency         String?
+  sentiment        String?
+  resolutionStatus String?
+  orderId          String?
+  attributionType  String?
+  metadata         Json?
+  createdAt        DateTime @default(now())
+
+  @@index([storeId, eventType, createdAt])
+  @@index([storeId, createdAt])
+  @@index([conversationId])
+  @@index([productId])
+  @@index([orderId])
+}
+
 model PushSubscription {
   id     String @id @default(cuid())
   userId String

src/app/api/dashboard/ai-analytics/route.test.ts

@@ -0,0 +1,112 @@
+import { beforeEach, describe, expect, it, vi } from "vitest"
+import { GET } from "./route"
+
+vi.mock("@/auth", () => ({
+    auth: vi.fn(),
+}))
+
+vi.mock("@/lib/dashboard/vendorActiveStore", () => ({
+    getVendorActiveStore: vi.fn(),
+}))
+
+vi.mock("@/lib/prisma", () => ({
+    prisma: {
+        storefrontChatMessage: { findMany: vi.fn() },
+        aiAnalyticsEvent: { findMany: vi.fn() },
+        storefrontChatConversation: { findMany: vi.fn() },
+        cachedOrder: { findMany: vi.fn() },
+        chatMessage: { findMany: vi.fn() },
+    },
+}))
+
+beforeEach(() => {
+    vi.clearAllMocks()
+})
+
+describe("GET /api/dashboard/ai-analytics", () => {
+    it("returns business outcome analytics instead of technical model KPIs", async () => {
+        const { auth } = await import("@/auth")
+        const { getVendorActiveStore } = await import("@/lib/dashboard/vendorActiveStore")
+        const { prisma } = await import("@/lib/prisma")
+
+        vi.mocked(auth).mockResolvedValue({ user: { id: "user-1", role: "VENDOR" } } as never)
+        vi.mocked(getVendorActiveStore).mockResolvedValue({ id: "store-1", userId: "user-1" } as never)
+        vi.mocked(prisma.storefrontChatMessage.findMany).mockResolvedValue([
+            {
+                senderType: "CUSTOMER",
+                createdAt: new Date("2026-05-01T10:00:00Z"),
+                content: "Do you have school shoes?",
+                conversationId: "conv-1",
+            },
+            {
+                senderType: "AI",
+                createdAt: new Date("2026-05-01T10:00:03Z"),
+                content: "Yes, we have school shoes.",
+                conversationId: "conv-1",
+            },
+        ] as never)
+        vi.mocked(prisma.aiAnalyticsEvent.findMany).mockResolvedValue([
+            {
+                id: "event-1",
+                conversationId: "conv-1",
+                messageId: "m1",
+                eventType: "customer_question",
+                source: "storefront_chat",
+                productId: null,
+                variantId: null,
+                quantity: null,
+                amount: null,
+                currency: null,
+                sentiment: "neutral",
+                resolutionStatus: null,
+                orderId: null,
+                attributionType: null,
+                metadata: null,
+                createdAt: new Date("2026-05-01T10:00:00Z"),
+            },
+            {
+                id: "event-2",
+                conversationId: "conv-1",
+                messageId: "m2",
+                eventType: "resolved_by_ai",
+                source: "storefront_chat",
+                productId: null,
+                variantId: null,
+                quantity: null,
+                amount: null,
+                currency: null,
+                sentiment: null,
+                resolutionStatus: "resolved_by_ai",
+                orderId: null,
+                attributionType: null,
+                metadata: null,
+                createdAt: new Date("2026-05-01T10:00:03Z"),
+            },
+        ] as never)
+        vi.mocked(prisma.storefrontChatConversation.findMany).mockResolvedValue([
+            {
+                id: "conv-1",
+                visitorId: "visitor-1",
+                customerEmail: null,
+                boundShopifyOrderId: null,
+                createdAt: new Date("2026-05-01T09:59:00Z"),
+                updatedAt: new Date("2026-05-01T10:00:03Z"),
+            },
+        ] as never)
+        vi.mocked(prisma.cachedOrder.findMany).mockResolvedValue([] as never)
+        vi.mocked(prisma.chatMessage.findMany).mockResolvedValue([] as never)
+
+        const res = await GET(new Request("http://localhost/api/dashboard/ai-analytics?range=30d"))
+        const json = await res.json()
+
+        expect(res.status).toBe(200)
+        expect(json.modelEvaluation).toBeUndefined()
+        expect(json.revenueImpact).toMatchObject({
+            directUpsellRevenue: 0,
+            aiAssistedRevenue: 0,
+        })
+        expect(json.customerExperience.sentimentMix.totals.neutral).toBe(1)
+        expect(json.automationOutcomes.questionsAnswered).toBe(1)
+        expect(Array.isArray(json.timeline)).toBe(true)
+    })
+})

src/app/api/dashboard/ai-analytics/route.ts

@@ -1,10 +1,75 @@
 import { NextResponse } from "next/server"
-import { format, startOfMonth, subMonths } from "date-fns"
 import { auth } from "@/auth"
 import { prisma } from "@/lib/prisma"
 import { getVendorActiveStore } from "@/lib/dashboard/vendorActiveStore"
 import { classifyStorefrontCustomerQuery } from "@/lib/dashboard/storefrontQueryClassification"
-import { buildModelEvaluationSnapshot } from "@/lib/dashboard/aiModelEvaluationSnapshot"
+import { bucketKey, emptyTimeline, getAnalyticsWindow, type AnalyticsGrain } from "@/lib/dashboard/analyticsTimeline"
+import { computeAiRevenueAttribution } from "@/lib/analytics/revenueAttribution"
+
+type StorefrontMessageRow = {
+    senderType: "CUSTOMER" | "AI"
+    createdAt: Date
+    content: string
+    conversationId: string
+}
+
+type AnalyticsEventRow = {
+    id: string
+    conversationId: string | null
+    messageId: string | null
+    eventType: string
+    source: string
+    productId: string | null
+    variantId: string | null
+    quantity: number | null
+    amount: number | null
+    currency: string | null
+    sentiment: string | null
+    resolutionStatus: string | null
+    orderId: string | null
+    attributionType: string | null
+    metadata: unknown
+    createdAt: Date
+}
+
+type ConversationRow = {
+    id: string
+    visitorId: string
+    customerEmail: string | null
+    boundShopifyOrderId: string | null
+    createdAt: Date
+    updatedAt: Date
+}
+
+type CachedOrderRow = {
+    shopifyOrderId: string
+    orderNumber: string
+    email: string | null
+    totalPrice: string
+    currency: string | null
+    lineItems: unknown
+    shopifyCreatedAt: Date | null
+    createdAt: Date
+}
+
+type ChatMessageRow = {
+    senderId: string
+    receiverId: string
+    createdAt: Date
+}
+
+type PrismaWithAnalytics = typeof prisma & {
+    aiAnalyticsEvent: {
+        findMany: (args: {
+            where: {
+                storeId: string
+                createdAt: { gte: Date; lte: Date }
+            }
+            select: Record<string, boolean>
+            orderBy: { createdAt: "asc" }
+        }) => Promise<AnalyticsEventRow[]>
+    }
+}
 
 function median(nums: number[]): number {
     if (nums.length === 0) return 0
@@ -13,20 +78,37 @@ function median(nums: number[]): number {
     return s.length % 2 ? s[m]! : (s[m - 1]! + s[m]!) / 2
 }
 
-function monthKeys(monthCount: number): string[] {
+function pct(part: number, total: number): number {
-    const keys: string[] = []
+    if (total <= 0) return 0
-    for (let i = monthCount - 1; i >= 0; i--) {
+    return Math.round((part / total) * 1000) / 10
-        keys.push(format(startOfMonth(subMonths(new Date(), i)), "yyyy-MM"))
-    }
-    return keys
 }
 
-function monthLabel(isoMonth: string): string {
+function money(amount: number): number {
-    const [y, m] = isoMonth.split("-").map(Number)
+    return Math.round(amount * 100) / 100
-    return format(new Date(y!, (m ?? 1) - 1, 1), "MMM")
 }
 
-export async function GET() {
+function addToTimeline<T extends { key: string }>(
+    timeline: T[],
+    date: Date,
+    grain: AnalyticsGrain,
+    update: (row: T) => void,
+) {
+    const key = bucketKey(date, grain)
+    const row = timeline.find((item) => item.key === key)
+    if (row) update(row)
+}
+
+function productQuestionLabel(content: string): string {
+    return (
+        content
+            .replace(/[^\w\s-]/g, " ")
+            .replace(/\s+/g, " ")
+            .trim()
+            .slice(0, 70) || "Product question"
+    )
+}
+
+export async function GET(req: Request) {
     try {
         const session = await auth()
         if (!session?.user?.id) {
@@ -41,19 +123,26 @@ export async function GET() {
             return NextResponse.json({ message: "No active store" }, { status: 400 })
         }
 
+        const { searchParams } = new URL(req.url)
+        const window = getAnalyticsWindow({
+            range: searchParams.get("range"),
+            grain: searchParams.get("grain"),
+        })
         const vendorUserId = store.userId
-        const since = startOfMonth(subMonths(new Date(), 5))
+        const prismaWithAnalytics = prisma as PrismaWithAnalytics
 
-        const [
+        const [storefrontMessages, analyticsEvents, conversations, cachedOrders, chatMessages]: [
-            storefrontMessages,
+            StorefrontMessageRow[],
-            gmailAutoReplies,
+            AnalyticsEventRow[],
-            attributedConversations,
+            ConversationRow[],
-            chatMessages,
+            CachedOrderRow[],
-        ] = await Promise.all([
+            ChatMessageRow[],
+        ] =
+            await Promise.all([
                 prisma.storefrontChatMessage.findMany({
                     where: {
                         conversation: { storeId: store.id },
-                    createdAt: { gte: since },
+                        createdAt: { gte: window.since, lte: window.until },
                     },
                     select: {
                         senderType: true,
@@ -63,43 +152,120 @@ export async function GET() {
                     },
                     orderBy: [{ conversationId: "asc" }, { createdAt: "asc" }],
                 }),
-            prisma.gmailInboundAutoReply.findMany({
+                prismaWithAnalytics.aiAnalyticsEvent.findMany({
-                where: { storeId: store.id, createdAt: { gte: since } },
+                    where: { storeId: store.id, createdAt: { gte: window.since, lte: window.until } },
-                select: { createdAt: true },
+                    select: {
+                        id: true,
+                        conversationId: true,
+                        messageId: true,
+                        eventType: true,
+                        source: true,
+                        productId: true,
+                        variantId: true,
+                        quantity: true,
+                        amount: true,
+                        currency: true,
+                        sentiment: true,
+                        resolutionStatus: true,
+                        orderId: true,
+                        attributionType: true,
+                        metadata: true,
+                        createdAt: true,
+                    },
+                    orderBy: { createdAt: "asc" },
                 }),
                 prisma.storefrontChatConversation.findMany({
-                where: {
+                    where: { storeId: store.id, updatedAt: { gte: window.since } },
-                    storeId: store.id,
-                    boundShopifyOrderId: { not: null },
-                    updatedAt: { gte: subMonths(new Date(), 1) },
-                },
                     select: {
                         id: true,
                         visitorId: true,
                         customerEmail: true,
                         boundShopifyOrderId: true,
+                        createdAt: true,
                         updatedAt: true,
                     },
-                orderBy: { updatedAt: "desc" },
+                    orderBy: { updatedAt: "asc" },
-                take: 25,
+                }),
+                prisma.cachedOrder.findMany({
+                    where: {
+                        storeId: store.id,
+                        OR: [
+                            { shopifyCreatedAt: { gte: window.since, lte: window.until } },
+                            { createdAt: { gte: window.since, lte: window.until } },
+                        ],
+                    },
+                    select: {
+                        shopifyOrderId: true,
+                        orderNumber: true,
+                        email: true,
+                        totalPrice: true,
+                        currency: true,
+                        lineItems: true,
+                        shopifyCreatedAt: true,
+                        createdAt: true,
+                    },
                 }),
                 prisma.chatMessage.findMany({
-                where: { storeId: store.id, createdAt: { gte: since } },
+                    where: { storeId: store.id, createdAt: { gte: window.since, lte: window.until } },
                     select: { senderId: true, receiverId: true, createdAt: true },
                     orderBy: { createdAt: "asc" },
                     take: 8000,
                 }),
             ])
 
-        const keys = monthKeys(6)
+        const revenueAttribution = computeAiRevenueAttribution({
-        const aiShareByMonth: Record<string, { ai: number; cust: number }> = Object.fromEntries(
+            events: analyticsEvents,
-            keys.map((k) => [k, { ai: 0, cust: 0 }]),
+            conversations,
-        )
+            orders: cachedOrders,
-        const gmailByMonth: Record<string, number> = Object.fromEntries(keys.map((k) => [k, 0]))
+        })
 
-        for (const g of gmailAutoReplies) {
+        const timeline = emptyTimeline(window.labels, window.grain, {
-            const k = format(startOfMonth(g.createdAt), "yyyy-MM")
+            directUpsellRevenue: 0,
-            if (k in gmailByMonth) gmailByMonth[k] += 1
+            directCrossSellRevenue: 0,
+            aiAssistedRevenue: 0,
+            questions: 0,
+            resolvedByAi: 0,
+            unresolvedQuestions: 0,
+            ticketsCreated: 0,
+            upsellShown: 0,
+            upsellClicked: 0,
+            cartActions: 0,
+            positiveSentiment: 0,
+            neutralSentiment: 0,
+            negativeSentiment: 0,
+            urgentSentiment: 0,
+        })
+
+        for (const event of analyticsEvents) {
+            addToTimeline(timeline, event.createdAt, window.grain, (row) => {
+                if (event.eventType === "customer_question") row.questions += 1
+                if (event.eventType === "resolved_by_ai") row.resolvedByAi += 1
+                if (event.eventType === "unresolved_question") row.unresolvedQuestions += 1
+                if (event.eventType === "ticket_created") row.ticketsCreated += 1
+                if (event.eventType === "upsell_shown") row.upsellShown += 1
+                if (event.eventType === "upsell_clicked") row.upsellClicked += 1
+                if (event.eventType === "add_to_cart_clicked" || event.eventType === "cart_link_opened") row.cartActions += 1
+                if (event.sentiment === "positive") row.positiveSentiment += 1
+                if (event.sentiment === "neutral") row.neutralSentiment += 1
+                if (event.sentiment === "negative") row.negativeSentiment += 1
+                if (event.sentiment === "urgent") row.urgentSentiment += 1
+            })
+        }
+        for (const row of revenueAttribution.directRows) {
+            addToTimeline(timeline, row.createdAt, window.grain, (bucket) => {
+                if (row.attributionType === "direct_upsell") bucket.directUpsellRevenue += row.amount
+                if (row.attributionType === "direct_cross_sell") bucket.directCrossSellRevenue += row.amount
+            })
+        }
+        for (const row of revenueAttribution.assistedRows) {
+            addToTimeline(timeline, row.createdAt, window.grain, (bucket) => {
+                bucket.aiAssistedRevenue += row.amount
+            })
+        }
+        for (const row of timeline) {
+            row.directUpsellRevenue = money(row.directUpsellRevenue)
+            row.directCrossSellRevenue = money(row.directCrossSellRevenue)
+            row.aiAssistedRevenue = money(row.aiAssistedRevenue)
         }
 
         const intentCounts: Record<string, number> = {
@@ -109,144 +275,157 @@ export async function GET() {
             Shipping: 0,
             General: 0,
         }
-
+        const productQuestionCounts = new Map<string, { name: string; count: number }>()
+        const pendingByConversation = new Map<string, Date>()
         const aiLatencySeconds: number[] = []
-        const aiLatencyByMonth: Record<string, number[]> = Object.fromEntries(keys.map((k) => [k, []]))
-        let pendingCustomer: { conversationId: string; createdAt: Date } | null = null
 
-        for (const m of storefrontMessages) {
+        for (const message of storefrontMessages) {
-            const monthKey = format(startOfMonth(m.createdAt), "yyyy-MM")
+            if (message.senderType === "CUSTOMER") {
-            if (monthKey in aiShareByMonth) {
+                const bucket = classifyStorefrontCustomerQuery(message.content)
-                if (m.senderType === "AI") aiShareByMonth[monthKey]!.ai += 1
+                intentCounts[bucket] += 1
-                if (m.senderType === "CUSTOMER") aiShareByMonth[monthKey]!.cust += 1
+                if (bucket === "Product Qs") {
-            }
+                    const name = productQuestionLabel(message.content)
-
+                    productQuestionCounts.set(name, {
-            if (m.senderType === "CUSTOMER") {
+                        name,
-                intentCounts[classifyStorefrontCustomerQuery(m.content)] += 1
+                        count: (productQuestionCounts.get(name)?.count ?? 0) + 1,
-                pendingCustomer = { conversationId: m.conversationId, createdAt: m.createdAt }
-            } else if (m.senderType === "AI" && pendingCustomer?.conversationId === m.conversationId) {
-                const sec = (m.createdAt.getTime() - pendingCustomer.createdAt.getTime()) / 1000
-                aiLatencySeconds.push(sec)
-                const mk = format(startOfMonth(m.createdAt), "yyyy-MM")
-                if (mk in aiLatencyByMonth) aiLatencyByMonth[mk]!.push(sec)
-                pendingCustomer = null
-            }
-        }
-
-        const automationSeries = keys.map((key) => {
-            const { ai, cust } = aiShareByMonth[key] ?? { ai: 0, cust: 0 }
-            const vol = ai + cust
-            const storefrontAiSharePct = vol > 0 ? Math.round((ai / vol) * 1000) / 10 : 0
-            return {
-                monthKey: key,
-                month: monthLabel(key),
-                storefrontAiSharePct,
-                gmailAutoReplies: gmailByMonth[key] ?? 0,
-                volumes: { aiMessages: ai, customerMessages: cust },
-            }
                     })
+                }
+                pendingByConversation.set(message.conversationId, message.createdAt)
+            } else if (message.senderType === "AI") {
+                const pendingAt = pendingByConversation.get(message.conversationId)
+                if (!pendingAt) continue
+                const sec = (message.createdAt.getTime() - pendingAt.getTime()) / 1000
+                if (sec >= 0 && sec <= 3600) aiLatencySeconds.push(sec)
+                pendingByConversation.delete(message.conversationId)
+            }
+        }
 
         const manualLatencyMinutes: number[] = []
-        const manualLatencyByMonth: Record<string, number[]> = Object.fromEntries(keys.map((k) => [k, []]))
         for (let i = 0; i < chatMessages.length - 1; i++) {
             const a = chatMessages[i]!
             const b = chatMessages[i + 1]!
-            if (
+            if (a.receiverId === vendorUserId && a.senderId !== vendorUserId && b.senderId === vendorUserId) {
-                a.receiverId === vendorUserId &&
-                a.senderId !== vendorUserId &&
-                b.senderId === vendorUserId
-            ) {
                 const min = (b.createdAt.getTime() - a.createdAt.getTime()) / 60_000
-                manualLatencyMinutes.push(min)
+                if (min >= 0 && min <= 24 * 60) manualLatencyMinutes.push(min)
-                const mk = format(startOfMonth(b.createdAt), "yyyy-MM")
-                if (mk in manualLatencyByMonth) manualLatencyByMonth[mk]!.push(min)
             }
         }
 
-        const responseTimeByMonth = keys.map((k) => ({
+        const questions =
-            month: monthLabel(k),
+            analyticsEvents.filter((event) => event.eventType === "customer_question").length ||
-            aiMinutes: Math.round((median(aiLatencyByMonth[k] ?? []) / 60) * 10) / 10,
+            storefrontMessages.filter((m) => m.senderType === "CUSTOMER").length
-            manualMinutes: Math.round(median(manualLatencyByMonth[k] ?? []) * 10) / 10,
+        const aiAnswers =
-        }))
+            analyticsEvents.filter((event) => event.eventType === "ai_answered").length ||
+            storefrontMessages.filter((m) => m.senderType === "AI").length
+        const resolvedByAi = analyticsEvents.filter((event) => event.eventType === "resolved_by_ai").length
+        const unresolvedQuestions = analyticsEvents.filter((event) => event.eventType === "unresolved_question").length
+        const ticketsCreated = analyticsEvents.filter((event) => event.eventType === "ticket_created").length
+        const cartClicks = analyticsEvents.filter((event) =>
+            ["add_to_cart_clicked", "upsell_clicked", "cross_sell_clicked"].includes(event.eventType),
+        ).length
+        const cartOpenCount = analyticsEvents.filter((event) => event.eventType === "cart_link_opened").length
+        const conversionBase = cartClicks || cartOpenCount
+        const directOrderCount = new Set(revenueAttribution.directRows.map((row) => row.orderId)).size
+        const currency = cachedOrders[0]?.currency ?? revenueAttribution.directRows[0]?.currency ?? "GBP"
 
-        const orderIds = attributedConversations
+        const sentimentTotals = {
-            .map((c) => c.boundShopifyOrderId)
+            positive: analyticsEvents.filter((event) => event.sentiment === "positive").length,
-            .filter(Boolean) as string[]
+            neutral: analyticsEvents.filter((event) => event.sentiment === "neutral").length,
-
+            negative: analyticsEvents.filter((event) => event.sentiment === "negative").length,
-        const cachedOrders =
+            urgent: analyticsEvents.filter((event) => event.sentiment === "urgent").length,
-            orderIds.length > 0
+            unknown: analyticsEvents.filter((event) => event.sentiment === "unknown").length,
-                ? await prisma.cachedOrder.findMany({
-                      where: {
-                          storeId: store.id,
-                          shopifyOrderId: { in: orderIds },
-                      },
-                      select: {
-                          shopifyOrderId: true,
-                          totalPrice: true,
-                          currency: true,
-                          orderNumber: true,
-                      },
-                  })
-                : []
-
-        const orderByShopifyId = new Map(cachedOrders.map((o) => [o.shopifyOrderId, o]))
-
-        const attributedRows = attributedConversations
-            .map((conv) => {
-                const oid = conv.boundShopifyOrderId
-                if (!oid) return null
-                const o = orderByShopifyId.get(oid)
-                const amt = o ? parseFloat(o.totalPrice || "0") : null
-                const currency = o?.currency ?? "GBP"
-                const hint =
-                    conv.customerEmail?.split("@")[0]?.slice(0, 2).toUpperCase() ??
-                    conv.visitorId.slice(0, 2).toUpperCase()
-                return {
-                    id: conv.id,
-                    initials: hint.padEnd(2, "?"),
-                    label:
-                        conv.customerEmail?.replace(/(@.).+$/, "$1***") ??
-                        `Visitor ${conv.visitorId.slice(-6)}`,
-                    summary: o?.orderNumber ? `Order ${o.orderNumber}` : "Linked storefront session → order",
-                    amount: amt,
-                    currency,
-                    channel: "AI" as const,
-                    updatedAt: conv.updatedAt.toISOString(),
         }
-            })
+        const sentimentTotal = Object.values(sentimentTotals).reduce((sum, count) => sum + count, 0)
-            .filter(Boolean)
+        const aiMedianSeconds = median(aiLatencySeconds)
-
+        const manualMedianMinutes = median(manualLatencyMinutes)
-        const attributedTotal = attributedRows.reduce((sum, r) => sum + (r?.amount ?? 0), 0)
+        const minutesSavedPerAnswer = Math.max(0, manualMedianMinutes - aiMedianSeconds / 60)
-
+        const hoursSavedEstimate = Math.round((minutesSavedPerAnswer * aiAnswers / 60) * 10) / 10
-        const signalEvents = storefrontMessages.length + gmailAutoReplies.length
+        const topProductQuestions = Array.from(productQuestionCounts.values())
-        const modelEvaluation = buildModelEvaluationSnapshot(store.id, {
+            .sort((a, b) => b.count - a.count)
-            signalEvents,
+            .slice(0, 8)
-            monthLabels: automationSeries.map((r) => r.month),
+        const convertedProductIds = new Set(revenueAttribution.directRows.map((row) => row.productId).filter(Boolean))
-        })
+        const highInterestProducts = topProductQuestions
+            .filter((row) => !convertedProductIds.has(row.name))
+            .slice(0, 5)
 
         return NextResponse.json({
             storeId: store.id,
             generatedAt: new Date().toISOString(),
-            modelEvaluation,
+            range: window.range,
-            automationSeries,
+            grain: window.grain,
-            messageVolumeByType: Object.entries(intentCounts).map(([name, count]) => ({ name, count })),
+            window: {
-            responseTime: {
+                since: window.since.toISOString(),
-                aiMedianSeconds: Math.round(median(aiLatencySeconds)),
+                until: window.until.toISOString(),
-                manualMedianMinutes: Math.round(median(manualLatencyMinutes) * 10) / 10,
+            },
+            revenueImpact: {
+                currency,
+                directUpsellRevenue: money(revenueAttribution.directUpsellRevenue),
+                directCrossSellRevenue: money(revenueAttribution.directCrossSellRevenue),
+                aiAssistedRevenue: money(revenueAttribution.aiAssistedRevenue),
+                aiCartConversionRate: pct(directOrderCount, conversionBase),
+                averageAiInfluencedOrderValue:
+                    revenueAttribution.assistedRows.length > 0
+                        ? money(revenueAttribution.aiAssistedRevenue / revenueAttribution.assistedRows.length)
+                        : 0,
+                pendingAttributionCount: Math.max(0, cartClicks - directOrderCount),
+                directRows: revenueAttribution.directRows.slice(0, 12).map((row) => ({
+                    ...row,
+                    createdAt: row.createdAt.toISOString(),
+                })),
+                assistedRows: revenueAttribution.assistedRows.slice(0, 12).map((row) => ({
+                    ...row,
+                    createdAt: row.createdAt.toISOString(),
+                })),
+                methodology:
+                    "Direct revenue only counts AI-clicked products later found in a matching order. Assisted revenue counts full matching orders after AI engagement.",
+            },
+            customerExperience: {
+                sentimentMix: {
+                    totals: sentimentTotals,
+                    percentages: {
+                        positive: pct(sentimentTotals.positive, sentimentTotal),
+                        neutral: pct(sentimentTotals.neutral, sentimentTotal),
+                        negative: pct(sentimentTotals.negative, sentimentTotal),
+                        urgent: pct(sentimentTotals.urgent, sentimentTotal),
+                        unknown: pct(sentimentTotals.unknown, sentimentTotal),
+                    },
+                },
+                unresolvedQuestionRate: pct(unresolvedQuestions, questions),
+                escalationRate: pct(ticketsCreated, questions),
+                unresolvedQuestions,
+                ticketsCreated,
+                negativeOrUrgent: sentimentTotals.negative + sentimentTotals.urgent,
+            },
+            automationOutcomes: {
+                questionsAnswered: questions,
+                aiAnswers,
+                resolvedByAi,
+                ticketsCreated,
+                aiMedianSeconds: Math.round(aiMedianSeconds),
+                manualMedianMinutes: Math.round(manualMedianMinutes * 10) / 10,
                 sampleAiPairs: aiLatencySeconds.length,
                 sampleManualPairs: manualLatencyMinutes.length,
-                byMonth: responseTimeByMonth,
+                hoursSavedEstimate,
+                resolutionRate: pct(resolvedByAi, questions),
             },
-            attributedSupportSales: {
+            demandSignals: {
-                totalAmount: Math.round(attributedTotal * 100) / 100,
+                curiousProductQuestionCount: intentCounts["Product Qs"],
-                currency: cachedOrders[0]?.currency ?? "GBP",
+                topProductQuestions,
-                rows: attributedRows.slice(0, 12),
+                highInterestProducts,
+                questionMix: Object.entries(intentCounts).map(([name, count]) => ({ name, count })),
+            },
+            timeline,
+            technicalDiagnostics: {
+                hiddenByDefault: true,
+                storefrontAiMessages: storefrontMessages.filter((m) => m.senderType === "AI").length,
+                storefrontCustomerMessages: storefrontMessages.filter((m) => m.senderType === "CUSTOMER").length,
+                analyticsEvents: analyticsEvents.length,
+                cachedOrders: cachedOrders.length,
             },
             totals: {
-                storefrontAiMessages: storefrontMessages.filter((m) => m.senderType === "AI").length,
+                questions,
-                storefrontCustomerMessages: storefrontMessages.filter((m) => m.senderType === "CUSTOMER")
+                aiAnswers,
-                    .length,
+                cartClicks,
-                gmailAutoReplies: gmailAutoReplies.length,
+                cartOpenCount,
             },
         })
     } catch (e) {

src/app/api/storefront-chat/events/route.ts

@@ -0,0 +1,61 @@
+import { NextResponse } from "next/server"
+import { prisma } from "@/lib/prisma"
+import { storefrontChatEventSchema } from "@/lib/api-schemas/public"
+import { validationErrorResponse } from "@/lib/api-schemas/validate"
+import { logAiAnalyticsEvent } from "@/lib/analytics/aiEvents"
+
+function normalizeShopDomain(input: string) {
+    return input.trim().toLowerCase().replace(/^https?:\/\//i, "").replace(/\/$/, "")
+}
+
+function shopVariants(cleanDomain: string) {
+    if (!cleanDomain) return []
+    const v = new Set<string>()
+    v.add(cleanDomain)
+    v.add(`${cleanDomain}/`)
+    v.add(`https://${cleanDomain}`)
+    v.add(`http://${cleanDomain}`)
+    return Array.from(v)
+}
+
+export async function POST(req: Request) {
+    try {
+        const raw = await req.json().catch(() => ({}))
+        const parsed = storefrontChatEventSchema.safeParse(raw)
+        if (!parsed.success) {
+            return validationErrorResponse(parsed.error)
+        }
+
+        const { shop, visitorId, type, productId, variantId, sourceMessageId } = parsed.data
+        const stores = await prisma.store.findMany({
+            where: { shopifyDomain: { in: shopVariants(normalizeShopDomain(shop)) } },
+            select: { id: true, storefrontChatEnabled: true },
+        })
+        const store = stores.find((s) => Boolean(s.storefrontChatEnabled)) || stores[0]
+        if (!store || !store.storefrontChatEnabled) {
+            return NextResponse.json({ success: true })
+        }
+
+        const conversation = await prisma.storefrontChatConversation.findFirst({
+            where: { storeId: store.id, visitorId },
+            select: { id: true },
+        })
+
+        if (conversation) {
+            void logAiAnalyticsEvent({
+                storeId: store.id,
+                conversationId: conversation.id,
+                messageId: sourceMessageId ?? null,
+                eventType: type,
+                productId: productId ?? null,
+                variantId: variantId ?? null,
+                metadata: { source: "widget_action" },
+            })
+        }
+
+        return NextResponse.json({ success: true })
+    } catch (error) {
+        console.error("[STOREFRONT_CHAT_EVENTS_POST]", error)
+        return NextResponse.json({ success: true })
+    }
+}

src/app/api/storefront-chat/messages/route.ts

@@ -25,6 +25,12 @@ import {
     selectVariantForCart,
 } from "@/lib/storefront-chat/cartLinks"
 import { productSearchHitToActionProduct, searchProductsForStore } from "@/lib/ai/tools/productTools"
+import {
+    classifyAiResolution,
+    classifyCustomerSentiment,
+    logAiAnalyticsEvent,
+    logAiAnalyticsEvents,
+} from "@/lib/analytics/aiEvents"
 
 function normalizeShopDomain(input: string) {
     return input
@@ -423,6 +429,39 @@ export async function POST(req: Request) {
             select: { id: true, createdAt: true, senderType: true, content: true, actions: true }
         })
 
+        void logAiAnalyticsEvents([
+            {
+                storeId: store.id,
+                conversationId: conversation.id,
+                messageId: customerMessage.id,
+                eventType: "customer_question",
+                sentiment: classifyCustomerSentiment(customerContent),
+                metadata: {
+                    actionType: action?.type ?? null,
+                    contentPreview: customerContent.slice(0, 500),
+                },
+                createdAt: customerMessage.createdAt,
+            },
+            ...(action
+                ? [
+                      {
+                          storeId: store.id,
+                          conversationId: conversation.id,
+                          messageId: customerMessage.id,
+                          eventType:
+                              action.type === "upsell_suggestion"
+                                  ? ("upsell_clicked" as const)
+                                  : ("add_to_cart_clicked" as const),
+                          productId: action.productId,
+                          variantId: action.variantId ?? null,
+                          quantity: action.quantity,
+                          metadata: { sourceMessageId: action.sourceMessageId ?? null },
+                          createdAt: customerMessage.createdAt,
+                      },
+                  ]
+                : []),
+        ])
+
         const pendingCartProduct = parsePendingCartProduct(conversation.l1State)
         const existingCartLines = parseCartLines(conversation.l1State)
         const cartResponse = action
@@ -463,6 +502,50 @@ export async function POST(req: Request) {
                 select: { id: true, createdAt: true, senderType: true, content: true, actions: true },
             })
 
+            const openCartAction = cartResponse.actions.find((a) => a.type === "open_cart")
+            const openCartProduct = openCartAction?.product
+            void logAiAnalyticsEvents([
+                {
+                    storeId: store.id,
+                    conversationId: conversation.id,
+                    messageId: aiMessage.id,
+                    eventType: "ai_answered",
+                    resolutionStatus: "resolved_by_ai",
+                    productId: openCartProduct?.productId ?? null,
+                    variantId: openCartProduct?.variantId ?? null,
+                    quantity: openCartProduct?.quantity ?? null,
+                    metadata: {
+                        contentPreview: cartResponse.content.slice(0, 500),
+                        cartLines: cartResponse.cartLines,
+                    },
+                    createdAt: aiMessage.createdAt,
+                },
+                {
+                    storeId: store.id,
+                    conversationId: conversation.id,
+                    messageId: aiMessage.id,
+                    eventType: "cart_link_generated",
+                    productId: openCartProduct?.productId ?? null,
+                    variantId: openCartProduct?.variantId ?? null,
+                    quantity: openCartProduct?.quantity ?? null,
+                    metadata: {
+                        cartUrl: openCartAction?.cartUrl ?? null,
+                        cartLines: cartResponse.cartLines,
+                    },
+                    createdAt: aiMessage.createdAt,
+                },
+                {
+                    storeId: store.id,
+                    conversationId: conversation.id,
+                    messageId: aiMessage.id,
+                    eventType: "resolved_by_ai",
+                    productId: openCartProduct?.productId ?? null,
+                    variantId: openCartProduct?.variantId ?? null,
+                    metadata: { reason: "cart_permalink_generated" },
+                    createdAt: aiMessage.createdAt,
+                },
+            ])
+
             await prisma.storefrontChatConversation.update({
                 where: { id: conversation.id },
                 data: {
@@ -581,6 +664,68 @@ export async function POST(req: Request) {
             select: { id: true, createdAt: true, senderType: true, content: true, actions: true }
         })
 
+        const resolutionStatus = classifyAiResolution({
+            content: aiContent,
+            usedTools: ai.meta?.usedTools,
+            module: ai.meta?.module,
+        })
+        const upsellActions = aiActions.filter((a) => a.type === "upsell_suggestion")
+        void logAiAnalyticsEvents([
+            {
+                storeId: store.id,
+                conversationId: conversation.id,
+                messageId: aiMessage.id,
+                eventType: "ai_answered",
+                resolutionStatus,
+                metadata: {
+                    module: ai.meta?.module ?? null,
+                    usedTools: ai.meta?.usedTools ?? [],
+                    contentPreview: aiContent.slice(0, 500),
+                },
+                createdAt: aiMessage.createdAt,
+            },
+            ...(resolutionStatus === "resolved_by_ai"
+                ? [
+                      {
+                          storeId: store.id,
+                          conversationId: conversation.id,
+                          messageId: aiMessage.id,
+                          eventType: "resolved_by_ai" as const,
+                          resolutionStatus,
+                          metadata: { module: ai.meta?.module ?? null },
+                          createdAt: aiMessage.createdAt,
+                      },
+                  ]
+                : resolutionStatus === "needs_follow_up"
+                  ? [
+                        {
+                            storeId: store.id,
+                            conversationId: conversation.id,
+                            messageId: aiMessage.id,
+                            eventType: "unresolved_question" as const,
+                            resolutionStatus,
+                            metadata: { module: ai.meta?.module ?? null },
+                            createdAt: aiMessage.createdAt,
+                        },
+                    ]
+                  : []),
+            ...upsellActions.map((upsell) => ({
+                storeId: store.id,
+                conversationId: conversation.id,
+                messageId: aiMessage.id,
+                eventType: "upsell_shown" as const,
+                productId: upsell.product.productId,
+                variantId: upsell.product.variantId ?? null,
+                quantity: upsell.product.quantity ?? 1,
+                amount: upsell.product.price ? Number(upsell.product.price) || null : null,
+                metadata: {
+                    title: upsell.product.title,
+                    productType: upsell.product.productType ?? null,
+                },
+                createdAt: aiMessage.createdAt,
+            })),
+        ])
+
         if (aiContent === ai.content) {
             await prisma.storefrontChatConversation.update({
                 where: { id: conversation.id },
@@ -612,6 +757,18 @@ export async function POST(req: Request) {
                             status: "SENT",
                         },
                     })
+                    void logAiAnalyticsEvent({
+                        storeId: store.id,
+                        conversationId: conversation.id,
+                        messageId: aiMessage.id,
+                        eventType: "ticket_created",
+                        resolutionStatus: "escalated",
+                        metadata: {
+                            to: ticketTo,
+                            subject: ticketSubject,
+                            module: ai.meta?.module ?? null,
+                        },
+                    })
                 }
             } catch (ticketErr) {
                 console.error("[STOREFRONT_TICKET_CREATE_ERR]", ticketErr)

src/app/dashboard/ai-analytics/page.tsx

@@ -7,82 +7,107 @@ import {
     Bar,
     BarChart,
     CartesianGrid,
+    Cell,
     Legend,
     Line,
     LineChart,
-    ReferenceLine,
     ResponsiveContainer,
     Tooltip,
     XAxis,
     YAxis,
-    Cell,
 } from "recharts"
 import { Loader2, Sparkles } from "lucide-react"
 import { useTheme } from "@/components/ThemeProvider"
 import { chartTooltipProps } from "@/lib/charts/rechartsTooltip"
 import { cn } from "@/lib/utils"
 
-type ModelEvaluationPayload = {
+type TimelineRow = {
-    modelName: string
+    key: string
-    pipelineVersion: string
+    label: string
-    evaluationWindow: string
+    directUpsellRevenue: number
-    sampleSize: number
+    directCrossSellRevenue: number
-    accuracyPct: number
+    aiAssistedRevenue: number
-    precisionPct: number
+    questions: number
-    recallPct: number
+    resolvedByAi: number
-    f1Pct: number
+    unresolvedQuestions: number
-    confusionMatrix: {
+    ticketsCreated: number
-        truePositive: number
+    upsellShown: number
-        falsePositive: number
+    upsellClicked: number
-        falseNegative: number
+    cartActions: number
-        trueNegative: number
+    positiveSentiment: number
-    }
+    neutralSentiment: number
-    monthlyF1Pct: { month: string; f1: number }[]
+    negativeSentiment: number
+    urgentSentiment: number
 }
 
 type AiAnalyticsPayload = {
-    modelEvaluation: ModelEvaluationPayload
+    generatedAt: string
-    automationSeries: {
+    range: string
-        month: string
+    grain: string
-        storefrontAiSharePct: number
+    revenueImpact: {
-        gmailAutoReplies: number
+        currency: string
-        volumes: { aiMessages: number; customerMessages: number }
+        directUpsellRevenue: number
-    }[]
+        directCrossSellRevenue: number
-    messageVolumeByType: { name: string; count: number }[]
+        aiAssistedRevenue: number
-    responseTime: {
+        aiCartConversionRate: number
+        averageAiInfluencedOrderValue: number
+        pendingAttributionCount: number
+        methodology: string
+    }
+    customerExperience: {
+        sentimentMix: {
+            totals: Record<"positive" | "neutral" | "negative" | "urgent" | "unknown", number>
+            percentages: Record<"positive" | "neutral" | "negative" | "urgent" | "unknown", number>
+        }
+        unresolvedQuestionRate: number
+        escalationRate: number
+        unresolvedQuestions: number
+        ticketsCreated: number
+        negativeOrUrgent: number
+    }
+    automationOutcomes: {
+        questionsAnswered: number
+        aiAnswers: number
+        resolvedByAi: number
+        ticketsCreated: number
         aiMedianSeconds: number
         manualMedianMinutes: number
         sampleAiPairs: number
         sampleManualPairs: number
-        byMonth: { month: string; aiMinutes: number; manualMinutes: number }[]
+        hoursSavedEstimate: number
+        resolutionRate: number
     }
-    attributedSupportSales: {
+    demandSignals: {
-        totalAmount: number
+        curiousProductQuestionCount: number
-        currency: string
+        topProductQuestions: { name: string; count: number }[]
-        rows: {
+        highInterestProducts: { name: string; count: number }[]
-            id: string
+        questionMix: { name: string; count: number }[]
-            initials: string
-            label: string
-            summary: string
-            amount: number | null
-            currency: string
-            channel: "AI"
-            updatedAt: string
-        }[]
     }
-    totals: {
+    timeline: TimelineRow[]
+    technicalDiagnostics: {
+        hiddenByDefault: boolean
         storefrontAiMessages: number
         storefrontCustomerMessages: number
-        gmailAutoReplies: number
+        analyticsEvents: number
+        cachedOrders: number
     }
 }
 
-const INTENT_COLORS: Record<string, string> = {
+const RANGE_OPTIONS = [
-    WISMO: "#3b82f6",
+    { value: "today", label: "Today" },
-    Returns: "#f97316",
+    { value: "7d", label: "7 days" },
-    "Product Qs": "#a855f7",
+    { value: "30d", label: "30 days" },
-    Shipping: "#22c55e",
+    { value: "90d", label: "90 days" },
-    General: "#94a3b8",
+    { value: "12m", label: "12 months" },
+    { value: "year", label: "Year" },
+    { value: "all", label: "All" },
+] as const
+
+const SENTIMENT_COLORS = {
+    positive: "#22c55e",
+    neutral: "#94a3b8",
+    negative: "#f97316",
+    urgent: "#ef4444",
 }
 
 function formatMoney(amount: number, currency: string) {
@@ -93,23 +118,62 @@ function formatMoney(amount: number, currency: string) {
     }
 }
 
+function formatPercent(value: number) {
+    return `${value.toFixed(value % 1 === 0 ? 0 : 1)}%`
+}
+
+function EmptyState({ children, isLight }: { children: string; isLight: boolean }) {
+    return (
+        <div className={cn("rounded-xl border p-6 text-sm text-center", isLight ? "border-slate-100 bg-slate-50 text-slate-500" : "border-white/10 bg-white/[0.04] text-slate-400")}>
+            {children}
+        </div>
+    )
+}
+
+function MetricCard({
+    title,
+    value,
+    helper,
+    accent,
+    isLight,
+}: {
+    title: string
+    value: string
+    helper: string
+    accent: string
+    isLight: boolean
+}) {
+    return (
+        <div className={cn("rounded-2xl border p-4 md:p-5", isLight ? "bg-white border-slate-100 shadow-sm" : "bg-white/5 border-white/10")}>
+            <p className={cn("text-[11px] uppercase tracking-widest font-black", isLight ? "text-slate-500" : "text-slate-400")}>
+                {title}
+            </p>
+            <p className={cn("text-2xl md:text-3xl font-black mt-2 tabular-nums", accent)}>{value}</p>
+            <p className={cn("text-xs mt-2 leading-relaxed", isLight ? "text-slate-500" : "text-slate-400")}>{helper}</p>
+        </div>
+    )
+}
+
 export default function AiAnalyticsPage() {
     const { theme } = useTheme()
     const isLight = theme === "light"
+    const [range, setRange] = useState("30d")
     const [data, setData] = useState<AiAnalyticsPayload | null>(null)
     const [error, setError] = useState<string | null>(null)
     const [loading, setLoading] = useState(true)
 
     useEffect(() => {
         let cancelled = false
+        setLoading(true)
+        setError(null)
         ;(async () => {
             try {
-                const res = await fetch("/api/dashboard/ai-analytics")
+                const res = await fetch(`/api/dashboard/ai-analytics?range=${encodeURIComponent(range)}`)
                 const json = await res.json().catch(() => null)
                 if (!res.ok) throw new Error(json?.message || "Failed to load analytics")
                 if (!cancelled) setData(json)
             } catch (e) {
-                if (!cancelled) setError(e instanceof Error ? e.message : "Failed to load")
+                if (!cancelled) setError(e instanceof Error ? e.message : "Failed to load analytics")
             } finally {
                 if (!cancelled) setLoading(false)
             }
@@ -117,63 +181,58 @@ export default function AiAnalyticsPage() {
         return () => {
             cancelled = true
         }
-    }, [])
+    }, [range])
 
     const chartMuted = isLight ? "#64748b" : "#94a3b8"
     const gridStroke = isLight ? "rgba(0,0,0,0.06)" : "rgba(255,255,255,0.08)"
     const tt = useMemo(() => chartTooltipProps(isLight), [isLight])
-
+    const timeline = data?.timeline ?? []
-    const barData = useMemo(
+    const sentimentBars = useMemo(() => {
-        () =>
+        const totals = data?.customerExperience.sentimentMix.totals
-            (data?.messageVolumeByType ?? []).map((r) => ({
+        if (!totals) return []
-                ...r,
+        return [
-                fill: INTENT_COLORS[r.name] ?? "#6366f1",
+            { name: "Positive", count: totals.positive, fill: SENTIMENT_COLORS.positive },
-            })),
+            { name: "Neutral", count: totals.neutral, fill: SENTIMENT_COLORS.neutral },
-        [data?.messageVolumeByType],
+            { name: "Negative", count: totals.negative, fill: SENTIMENT_COLORS.negative },
-    )
+            { name: "Urgent", count: totals.urgent, fill: SENTIMENT_COLORS.urgent },
-
+        ]
-    const responseBars = useMemo(() => {
+    }, [data?.customerExperience.sentimentMix.totals])
-        const rows = data?.responseTime.byMonth ?? []
-        return rows.map((r) => ({
-            month: r.month,
-            AI: r.aiMinutes > 0 ? r.aiMinutes : 0,
-            Manual: r.manualMinutes > 0 ? r.manualMinutes : 0,
-        }))
-    }, [data?.responseTime.byMonth])
 
     return (
         <div className="space-y-6 md:space-y-8 max-w-[1400px] mx-auto">
-            <div className="flex flex-col sm:flex-row sm:items-end sm:justify-between gap-4">
+            <div className="flex flex-col xl:flex-row xl:items-end xl:justify-between gap-4">
                 <div>
                     <div className="flex items-center gap-2 mb-1">
                         <Sparkles className={cn("w-7 h-7", isLight ? "text-indigo-600" : "text-indigo-400")} />
-                        <h1
+                        <h1 className={cn("text-2xl md:text-3xl font-extrabold tracking-tight", isLight ? "text-slate-900" : "text-white")}>
-                            className={cn(
+                            AI Business Analytics
-                                "text-2xl md:text-3xl font-extrabold tracking-tight",
-                                isLight ? "text-slate-900" : "text-white",
-                            )}
-                        >
-                            AI Analytics
                         </h1>
                     </div>
-                    <p className={cn("text-sm md:text-base max-w-2xl", isLight ? "text-slate-500" : "text-slate-400")}>
+                    <p className={cn("text-sm md:text-base max-w-3xl", isLight ? "text-slate-500" : "text-slate-400")}>
-                        Live metrics from storefront AI replies, Gmail automation, JUNO Chat response times, and
+                        Outcome metrics for revenue, customer experience, automation, and demand signals from AI-assisted storefront journeys.
-                        storefront sessions linked to Shopify orders.
                     </p>
                 </div>
-                {data && (
+                <div className="flex flex-wrap gap-2">
-                    <div
+                    {RANGE_OPTIONS.map((option) => (
+                        <button
+                            key={option.value}
+                            type="button"
+                            onClick={() => setRange(option.value)}
                             className={cn(
-                            "text-xs font-semibold rounded-xl px-4 py-2 border",
+                                "rounded-xl border px-3 py-2 text-xs font-black transition",
-                            isLight ? "bg-white border-slate-200 text-slate-600" : "bg-white/5 border-white/10 text-slate-300",
+                                range === option.value
+                                    ? isLight
+                                        ? "bg-indigo-600 border-indigo-600 text-white"
+                                        : "bg-indigo-500 border-indigo-400 text-white"
+                                    : isLight
+                                      ? "bg-white border-slate-200 text-slate-600 hover:bg-slate-50"
+                                      : "bg-white/5 border-white/10 text-slate-300 hover:bg-white/10",
                             )}
                         >
-                        <span className="opacity-70">Last 6 months · AI msgs </span>
+                            {option.label}
-                        <span className="text-emerald-500">{data.totals.storefrontAiMessages}</span>
+                        </button>
-                        <span className="opacity-70"> · Gmail auto </span>
+                    ))}
-                        <span className="text-sky-400">{data.totals.gmailAutoReplies}</span>
                 </div>
-                )}
             </div>
 
             {loading && (
@@ -181,418 +240,186 @@ export default function AiAnalyticsPage() {
                     <Loader2 className="w-10 h-10 animate-spin text-indigo-400" />
                 </div>
             )}
+
             {error && (
-                <div
+                <div className={cn("rounded-2xl border p-6 text-sm font-medium", isLight ? "bg-rose-50 border-rose-100 text-rose-800" : "bg-rose-950/40 border-rose-900/50 text-rose-100")}>
-                    className={cn(
-                        "rounded-2xl border p-6 text-sm font-medium",
-                        isLight ? "bg-rose-50 border-rose-100 text-rose-800" : "bg-rose-950/40 border-rose-900/50 text-rose-100",
-                    )}
-                >
                     {error}
                 </div>
             )}
 
             {!loading && data && (
                 <>
-                    <div
+                    <section className="grid grid-cols-1 md:grid-cols-2 xl:grid-cols-4 gap-4">
-                        className={cn(
+                        <MetricCard
-                            "p-4 md:p-6 rounded-xl md:rounded-2xl border",
+                            title="Direct AI upsell revenue"
-                            isLight ? "bg-white border-slate-100 shadow-sm" : "bg-white/5 border-white/10 backdrop-blur-xl",
+                            value={formatMoney(data.revenueImpact.directUpsellRevenue, data.revenueImpact.currency)}
-                        )}
+                            helper="Only counts AI-suggested products later found in matching Shopify orders."
-                    >
+                            accent={isLight ? "text-emerald-700" : "text-emerald-400"}
-                        <div className="flex flex-col lg:flex-row lg:items-start lg:justify-between gap-4 mb-6">
+                            isLight={isLight}
+                        />
+                        <MetricCard
+                            title="AI-assisted revenue"
+                            value={formatMoney(data.revenueImpact.aiAssistedRevenue, data.revenueImpact.currency)}
+                            helper="Full order revenue after prior AI engagement in the same customer/order window."
+                            accent={isLight ? "text-indigo-700" : "text-indigo-300"}
+                            isLight={isLight}
+                        />
+                        <MetricCard
+                            title="Unresolved questions"
+                            value={data.customerExperience.unresolvedQuestions.toLocaleString()}
+                            helper={`${formatPercent(data.customerExperience.unresolvedQuestionRate)} unresolved rate across customer questions.`}
+                            accent={isLight ? "text-orange-700" : "text-orange-300"}
+                            isLight={isLight}
+                        />
+                        <MetricCard
+                            title="Hours saved estimate"
+                            value={`${data.automationOutcomes.hoursSavedEstimate.toLocaleString()}h`}
+                            helper="Estimated from AI answer volume and median manual response time where samples exist."
+                            accent={isLight ? "text-sky-700" : "text-sky-300"}
+                            isLight={isLight}
+                        />
+                    </section>
+
+                    <section className="grid grid-cols-1 xl:grid-cols-3 gap-4 md:gap-6">
+                        <div className={cn("xl:col-span-2 rounded-2xl border p-4 md:p-6", isLight ? "bg-white border-slate-100 shadow-sm" : "bg-white/5 border-white/10")}>
+                            <div className="flex flex-col md:flex-row md:items-start md:justify-between gap-2 mb-5">
                                 <div>
-                                <h3 className={cn("text-lg font-bold", isLight ? "text-slate-900" : "text-white")}>
+                                    <h2 className={cn("text-lg font-black", isLight ? "text-slate-900" : "text-white")}>Revenue Impact Timeline</h2>
-                                    Model performance
+                                    <p className={cn("text-xs mt-1", isLight ? "text-slate-500" : "text-slate-400")}>{data.revenueImpact.methodology}</p>
-                                </h3>
-                                <p className={cn("text-xs mt-1 max-w-xl", isLight ? "text-slate-500" : "text-slate-400")}>
-                                    Intent routing and reply-quality classification tracked against assistant-handled
-                                    conversations on your storefront and Gmail automation pipeline.
-                                </p>
                                 </div>
-                            <div
+                                <span className={cn("text-[11px] font-black rounded-lg px-2 py-1", isLight ? "bg-slate-100 text-slate-600" : "bg-white/10 text-slate-300")}>
-                                className={cn(
+                                    {data.grain} view
-                                    "text-[11px] font-semibold space-y-1 rounded-xl px-4 py-3 border shrink-0",
-                                    isLight ? "bg-slate-50 border-slate-100 text-slate-600" : "bg-white/[0.06] border-white/10 text-slate-300",
-                                )}
-                            >
-                                <p>
-                                    <span className={cn("opacity-70 font-bold uppercase tracking-wide", isLight ? "" : "")}>Model </span>
-                                    <span className={isLight ? "text-slate-900" : "text-white"}>{data.modelEvaluation.modelName}</span>
-                                </p>
-                                <p>
-                                    <span className="opacity-70 font-bold uppercase tracking-wide">Release </span>
-                                    <span className={isLight ? "text-slate-900" : "text-white"}>{data.modelEvaluation.pipelineVersion}</span>
-                                </p>
-                                <p className="opacity-80">{data.modelEvaluation.evaluationWindow}</p>
-                                <p>
-                                    <span className="opacity-70">Weighted eval samples </span>
-                                    <span className={isLight ? "text-indigo-600" : "text-indigo-300"}>
-                                        {data.modelEvaluation.sampleSize.toLocaleString()}
                                 </span>
-                                </p>
                             </div>
-                        </div>
+                            {timeline.length === 0 ? (
-
+                                <EmptyState isLight={isLight}>Not enough data yet to draw a revenue timeline.</EmptyState>
-                        <div className="grid grid-cols-2 lg:grid-cols-4 gap-3 md:gap-4 mb-6">
+                            ) : (
-                            {(
+                                <div className="h-[300px] w-full min-w-0">
-                                [
-                                    { label: "Accuracy", value: data.modelEvaluation.accuracyPct, accent: "text-emerald-500" },
-                                    { label: "Precision", value: data.modelEvaluation.precisionPct, accent: "text-sky-500" },
-                                    { label: "Recall", value: data.modelEvaluation.recallPct, accent: "text-violet-500" },
-                                    { label: "F1 score", value: data.modelEvaluation.f1Pct, accent: "text-indigo-500" },
-                                ] as const
-                            ).map((m) => (
-                                <div
-                                    key={m.label}
-                                    className={cn(
-                                        "rounded-xl border p-4",
-                                        isLight ? "bg-slate-50/90 border-slate-100" : "bg-white/[0.04] border-white/10",
-                                    )}
-                                >
-                                    <p className={cn("text-[10px] uppercase font-bold tracking-widest", isLight ? "text-slate-500" : "text-slate-400")}>
-                                        {m.label}
-                                    </p>
-                                    <p className={cn("text-2xl md:text-3xl font-black mt-1 tabular-nums", isLight ? "text-slate-900" : "text-white")}>
-                                        {m.value}
-                                        <span className={cn("text-lg align-top ml-0.5", m.accent)}>%</span>
-                                    </p>
-                                </div>
-                            ))}
-                        </div>
-
-                        <div className="grid grid-cols-1 lg:grid-cols-2 gap-6">
-                            <div className="min-h-[220px]">
-                                <h4 className={cn("text-sm font-bold mb-3", isLight ? "text-slate-800" : "text-slate-200")}>
-                                    F1 score trend
-                                </h4>
-                                <div className="h-[200px] w-full min-w-0">
                                     <ResponsiveContainer width="100%" height="100%">
-                                        <LineChart data={data.modelEvaluation.monthlyF1Pct} margin={{ top: 8, right: 8, left: -18, bottom: 0 }}>
+                                        <AreaChart data={timeline} margin={{ top: 8, right: 8, left: -12, bottom: 0 }}>
                                             <CartesianGrid strokeDasharray="3 3" vertical={false} stroke={gridStroke} />
-                                            <XAxis
+                                            <XAxis dataKey="label" tick={{ fill: chartMuted, fontSize: 11, fontWeight: 600 }} axisLine={false} tickLine={false} dy={8} />
-                                                dataKey="month"
+                                            <YAxis tick={{ fill: chartMuted, fontSize: 10, fontWeight: 600 }} axisLine={false} tickLine={false} />
-                                                tick={{ fill: chartMuted, fontSize: 11, fontWeight: 600 }}
+                                            <Tooltip {...tt} formatter={(value, name) => [formatMoney(Number(value ?? 0), data.revenueImpact.currency), String(name)]} />
-                                                axisLine={false}
+                                            <Legend wrapperStyle={{ fontSize: 11, fontWeight: 700, color: chartMuted }} />
-                                                tickLine={false}
+                                            <Area type="monotone" dataKey="aiAssistedRevenue" name="AI-assisted" stackId="1" stroke="#6366f1" fill="#6366f1" fillOpacity={0.22} />
-                                                dy={8}
+                                            <Area type="monotone" dataKey="directUpsellRevenue" name="Direct upsell" stackId="1" stroke="#22c55e" fill="#22c55e" fillOpacity={0.35} />
-                                            />
+                                            <Area type="monotone" dataKey="directCrossSellRevenue" name="Direct cross-sell" stackId="1" stroke="#06b6d4" fill="#06b6d4" fillOpacity={0.3} />
-                                            <YAxis
-                                                domain={[78, 99]}
-                                                tick={{ fill: chartMuted, fontSize: 10, fontWeight: 600 }}
-                                                axisLine={false}
-                                                tickLine={false}
-                                                tickFormatter={(v) => `${v}%`}
-                                            />
-                                            <Tooltip {...tt} formatter={(v) => [`${Number(v ?? 0)}%`, "F1"]} />
-                                            <Line type="monotone" dataKey="f1" stroke="#6366f1" strokeWidth={3} dot={{ r: 4 }} activeDot={{ r: 6 }} />
-                                        </LineChart>
-                                    </ResponsiveContainer>
-                                </div>
-                            </div>
-
-                            <div>
-                                <h4 className={cn("text-sm font-bold mb-3", isLight ? "text-slate-800" : "text-slate-200")}>
-                                    Confusion matrix (held-out)
-                                </h4>
-                                <div className="flex justify-end gap-3 text-[10px] font-bold uppercase tracking-wide mb-2 pr-1">
-                                    <span className={cn("w-[calc(50%-6px)] text-center", isLight ? "text-slate-500" : "text-slate-400")}>
-                                        Predicted relevant
-                                    </span>
-                                    <span className={cn("w-[calc(50%-6px)] text-center", isLight ? "text-slate-500" : "text-slate-400")}>
-                                        Predicted not relevant
-                                    </span>
-                                </div>
-                                <div className="flex gap-3">
-                                    <div
-                                        className={cn(
-                                            "flex flex-col justify-center gap-10 py-2 w-24 shrink-0 text-[10px] font-bold uppercase tracking-wide leading-tight",
-                                            isLight ? "text-slate-500" : "text-slate-400",
-                                        )}
-                                    >
-                                        <span>Actual relevant</span>
-                                        <span>Actual not relevant</span>
-                                    </div>
-                                    <div className="flex-1 grid grid-cols-2 gap-3">
-                                        <div
-                                            className={cn(
-                                                "rounded-xl border p-4 text-center",
-                                                isLight ? "bg-emerald-50 border-emerald-100 text-emerald-900" : "bg-emerald-500/10 border-emerald-500/25 text-emerald-100",
-                                            )}
-                                        >
-                                            <p className="text-[10px] uppercase opacity-80 mb-1">True positive</p>
-                                            <p className="text-2xl font-black tabular-nums">{data.modelEvaluation.confusionMatrix.truePositive}</p>
-                                        </div>
-                                        <div
-                                            className={cn(
-                                                "rounded-xl border p-4 text-center",
-                                                isLight ? "bg-amber-50 border-amber-100 text-amber-950" : "bg-amber-500/10 border-amber-500/25 text-amber-100",
-                                            )}
-                                        >
-                                            <p className="text-[10px] uppercase opacity-80 mb-1">False negative</p>
-                                            <p className="text-2xl font-black tabular-nums">{data.modelEvaluation.confusionMatrix.falseNegative}</p>
-                                        </div>
-                                        <div
-                                            className={cn(
-                                                "rounded-xl border p-4 text-center",
-                                                isLight ? "bg-rose-50 border-rose-100 text-rose-950" : "bg-rose-500/10 border-rose-500/25 text-rose-100",
-                                            )}
-                                        >
-                                            <p className="text-[10px] uppercase opacity-80 mb-1">False positive</p>
-                                            <p className="text-2xl font-black tabular-nums">{data.modelEvaluation.confusionMatrix.falsePositive}</p>
-                                        </div>
-                                        <div
-                                            className={cn(
-                                                "rounded-xl border p-4 text-center",
-                                                isLight ? "bg-slate-50 border-slate-200 text-slate-800" : "bg-white/[0.06] border-white/10 text-slate-200",
-                                            )}
-                                        >
-                                            <p className="text-[10px] uppercase opacity-80 mb-1">True negative</p>
-                                            <p className="text-2xl font-black tabular-nums">{data.modelEvaluation.confusionMatrix.trueNegative}</p>
-                                        </div>
-                                    </div>
-                                </div>
-                            </div>
-                        </div>
-                    </div>
-
-                    <div className="grid grid-cols-1 lg:grid-cols-2 gap-4 md:gap-6">
-                        <div
-                            className={cn(
-                                "p-4 md:p-6 rounded-xl md:rounded-2xl border min-h-[320px]",
-                                isLight ? "bg-white border-slate-100 shadow-sm" : "bg-white/5 border-white/10 backdrop-blur-xl",
-                            )}
-                        >
-                            <h3 className={cn("text-lg font-bold", isLight ? "text-slate-900" : "text-white")}>
-                                AI share of storefront traffic
-                            </h3>
-                            <p className={cn("text-xs mt-1 mb-4", isLight ? "text-slate-500" : "text-slate-400")}>
-                                AI messages ÷ (AI + customer messages) per month. Gmail auto-replies shown in tooltip.
-                            </p>
-                            <div className="h-[240px] w-full min-w-0">
-                                <ResponsiveContainer width="100%" height="100%">
-                                    <AreaChart data={data.automationSeries} margin={{ top: 8, right: 8, left: -18, bottom: 0 }}>
-                                        <defs>
-                                            <linearGradient id="aiShareFill" x1="0" y1="0" x2="0" y2="1">
-                                                <stop offset="5%" stopColor="#22c55e" stopOpacity={isLight ? 0.35 : 0.25} />
-                                                <stop offset="95%" stopColor="#22c55e" stopOpacity={0} />
-                                            </linearGradient>
-                                        </defs>
-                                        <CartesianGrid strokeDasharray="3 3" vertical={false} stroke={gridStroke} />
-                                        <XAxis
-                                            dataKey="month"
-                                            tick={{ fill: chartMuted, fontSize: 11, fontWeight: 600 }}
-                                            axisLine={false}
-                                            tickLine={false}
-                                            dy={8}
-                                        />
-                                        <YAxis
-                                            tickFormatter={(v) => `${v}%`}
-                                            domain={[0, 100]}
-                                            tick={{ fill: chartMuted, fontSize: 10, fontWeight: 600 }}
-                                            axisLine={false}
-                                            tickLine={false}
-                                        />
-                                        <Tooltip
-                                            {...tt}
-                                            formatter={(value, _name, item) => {
-                                                const g = (item as { payload?: { gmailAutoReplies?: number } })?.payload
-                                                    ?.gmailAutoReplies
-                                                return [`${Number(value ?? 0)}% (Gmail auto: ${g ?? 0})`, "AI share"]
-                                            }}
-                                            labelFormatter={(l) => String(l)}
-                                        />
-                                        <ReferenceLine
-                                            y={80}
-                                            stroke={isLight ? "#cbd5e1" : "rgba(148,163,184,0.5)"}
-                                            strokeDasharray="4 4"
-                                        />
-                                        <Area
-                                            type="monotone"
-                                            dataKey="storefrontAiSharePct"
-                                            stroke="#16a34a"
-                                            strokeWidth={2}
-                                            fillOpacity={1}
-                                            fill="url(#aiShareFill)"
-                                            name="storefrontAiSharePct"
-                                        />
                                         </AreaChart>
                                     </ResponsiveContainer>
                                 </div>
+                            )}
                         </div>
 
-                        <div
+                        <div className={cn("rounded-2xl border p-4 md:p-6", isLight ? "bg-white border-slate-100 shadow-sm" : "bg-white/5 border-white/10")}>
-                            className={cn(
+                            <h2 className={cn("text-lg font-black", isLight ? "text-slate-900" : "text-white")}>Customer Sentiment</h2>
-                                "p-4 md:p-6 rounded-xl md:rounded-2xl border min-h-[320px]",
+                            <p className={cn("text-xs mt-1 mb-5", isLight ? "text-slate-500" : "text-slate-400")}>
-                                isLight ? "bg-white border-slate-100 shadow-sm" : "bg-white/5 border-white/10 backdrop-blur-xl",
+                                Conservative sentiment from storefront customer messages. Unknown is kept out of the chart.
-                            )}
-                        >
-                            <h3 className={cn("text-lg font-bold", isLight ? "text-slate-900" : "text-white")}>
-                                Customer topics (storefront)
-                            </h3>
-                            <p className={cn("text-xs mt-1 mb-4", isLight ? "text-slate-500" : "text-slate-400")}>
-                                Classified from customer messages via keyword rules (WISMO, returns, products, shipping).
                             </p>
+                            {sentimentBars.every((row) => row.count === 0) ? (
+                                <EmptyState isLight={isLight}>Not enough sentiment data yet.</EmptyState>
+                            ) : (
                                 <div className="h-[240px] w-full min-w-0">
                                     <ResponsiveContainer width="100%" height="100%">
-                                    <BarChart layout="vertical" data={barData} margin={{ left: 4, right: 16, top: 4, bottom: 4 }}>
+                                        <BarChart data={sentimentBars} margin={{ top: 8, right: 8, left: -18, bottom: 0 }}>
-                                        <CartesianGrid strokeDasharray="3 3" horizontal stroke={gridStroke} />
+                                            <CartesianGrid strokeDasharray="3 3" vertical={false} stroke={gridStroke} />
-                                        <XAxis type="number" tick={{ fill: chartMuted, fontSize: 10 }} axisLine={false} tickLine={false} />
+                                            <XAxis dataKey="name" tick={{ fill: chartMuted, fontSize: 11, fontWeight: 600 }} axisLine={false} tickLine={false} dy={8} />
-                                        <YAxis
+                                            <YAxis tick={{ fill: chartMuted, fontSize: 10, fontWeight: 600 }} axisLine={false} tickLine={false} />
-                                            type="category"
+                                            <Tooltip {...tt} />
-                                            dataKey="name"
+                                            <Bar dataKey="count" radius={[6, 6, 0, 0]}>
-                                            width={88}
+                                                {sentimentBars.map((row) => (
-                                            tick={{ fill: chartMuted, fontSize: 11, fontWeight: 600 }}
+                                                    <Cell key={row.name} fill={row.fill} />
-                                            axisLine={false}
-                                            tickLine={false}
-                                        />
-                                        <Tooltip
-                                            {...tt}
-                                            cursor={{ fill: isLight ? "rgba(99,102,241,0.06)" : "rgba(255,255,255,0.05)" }}
-                                        />
-                                        <Bar dataKey="count" radius={[0, 6, 6, 0]} barSize={18}>
-                                            {barData.map((entry, i) => (
-                                                <Cell key={i} fill={entry.fill} />
                                                 ))}
                                             </Bar>
                                         </BarChart>
                                     </ResponsiveContainer>
                                 </div>
+                            )}
+                        </div>
+                    </section>
+
+                    <section className="grid grid-cols-1 lg:grid-cols-2 gap-4 md:gap-6">
+                        <div className={cn("rounded-2xl border p-4 md:p-6", isLight ? "bg-white border-slate-100 shadow-sm" : "bg-white/5 border-white/10")}>
+                            <h2 className={cn("text-lg font-black", isLight ? "text-slate-900" : "text-white")}>Automation Outcomes</h2>
+                            <p className={cn("text-xs mt-1 mb-5", isLight ? "text-slate-500" : "text-slate-400")}>
+                                Shows the operational outcomes owners care about: handled questions, AI resolution, tickets, and response speed.
+                            </p>
+                            <div className="grid grid-cols-2 gap-3">
+                                <MetricCard title="Questions handled" value={data.automationOutcomes.questionsAnswered.toLocaleString()} helper="Customer questions in the selected window." accent={isLight ? "text-slate-900" : "text-white"} isLight={isLight} />
+                                <MetricCard title="Resolved by AI" value={formatPercent(data.automationOutcomes.resolutionRate)} helper={`${data.automationOutcomes.resolvedByAi.toLocaleString()} resolved events.`} accent={isLight ? "text-emerald-700" : "text-emerald-400"} isLight={isLight} />
+                                <MetricCard title="Tickets created" value={data.automationOutcomes.ticketsCreated.toLocaleString()} helper="Escalations sent to human support." accent={isLight ? "text-rose-700" : "text-rose-300"} isLight={isLight} />
+                                <MetricCard title="AI response median" value={`${data.automationOutcomes.aiMedianSeconds}s`} helper={`Manual median: ${data.automationOutcomes.manualMedianMinutes.toFixed(1)} min.`} accent={isLight ? "text-sky-700" : "text-sky-300"} isLight={isLight} />
+                            </div>
                         </div>
 
-                        <div
+                        <div className={cn("rounded-2xl border p-4 md:p-6", isLight ? "bg-white border-slate-100 shadow-sm" : "bg-white/5 border-white/10")}>
-                            className={cn(
+                            <h2 className={cn("text-lg font-black", isLight ? "text-slate-900" : "text-white")}>Questions And Resolutions</h2>
-                                "p-4 md:p-6 rounded-xl md:rounded-2xl border min-h-[320px]",
+                            <p className={cn("text-xs mt-1 mb-5", isLight ? "text-slate-500" : "text-slate-400")}>
-                                isLight ? "bg-white border-slate-100 shadow-sm" : "bg-white/5 border-white/10 backdrop-blur-xl",
+                                Timeline of demand, AI resolution, unresolved questions, and support tickets.
-                            )}
-                        >
-                            <h3 className={cn("text-lg font-bold", isLight ? "text-slate-900" : "text-white")}>
-                                Response time: AI vs manual
-                            </h3>
-                            <p className={cn("text-xs mt-1 mb-4", isLight ? "text-slate-500" : "text-slate-400")}>
-                                AI = median seconds from customer message to AI reply (storefront). Manual = median
-                                minutes from inbound supplier message to your reply (JUNO Chat), when sample exists.
                             </p>
-                            <div className="h-[240px] w-full min-w-0">
+                            <div className="h-[260px] w-full min-w-0">
                                 <ResponsiveContainer width="100%" height="100%">
-                                    <BarChart data={responseBars} margin={{ top: 8, right: 8, left: -18, bottom: 0 }}>
+                                    <LineChart data={timeline} margin={{ top: 8, right: 8, left: -18, bottom: 0 }}>
                                         <CartesianGrid strokeDasharray="3 3" vertical={false} stroke={gridStroke} />
-                                        <XAxis
+                                        <XAxis dataKey="label" tick={{ fill: chartMuted, fontSize: 11, fontWeight: 600 }} axisLine={false} tickLine={false} dy={8} />
-                                            dataKey="month"
+                                        <YAxis tick={{ fill: chartMuted, fontSize: 10, fontWeight: 600 }} axisLine={false} tickLine={false} />
-                                            tick={{ fill: chartMuted, fontSize: 11, fontWeight: 600 }}
+                                        <Tooltip {...tt} />
-                                            axisLine={false}
+                                        <Legend wrapperStyle={{ fontSize: 11, fontWeight: 700, color: chartMuted }} />
-                                            tickLine={false}
+                                        <Line type="monotone" dataKey="questions" name="Questions" stroke="#6366f1" strokeWidth={2} dot={false} />
-                                            dy={8}
+                                        <Line type="monotone" dataKey="resolvedByAi" name="Resolved" stroke="#22c55e" strokeWidth={2} dot={false} />
-                                        />
+                                        <Line type="monotone" dataKey="unresolvedQuestions" name="Unresolved" stroke="#f97316" strokeWidth={2} dot={false} />
-                                        <YAxis
+                                        <Line type="monotone" dataKey="ticketsCreated" name="Tickets" stroke="#ef4444" strokeWidth={2} dot={false} />
-                                            tick={{ fill: chartMuted, fontSize: 10, fontWeight: 600 }}
+                                    </LineChart>
-                                            axisLine={false}
-                                            tickLine={false}
-                                            tickFormatter={(v) => `${v}m`}
-                                        />
-                                        <Legend wrapperStyle={{ fontSize: 11, fontWeight: 600, color: chartMuted }} />
-                                        <Tooltip
-                                            {...tt}
-                                            formatter={(value, name) =>
-                                                name === "AI"
-                                                    ? [`${Number(value ?? 0)} min (median)`, "AI"]
-                                                    : [`${Number(value ?? 0)} min (median)`, "Manual"]
-                                            }
-                                        />
-                                        <Bar dataKey="AI" fill="#22c55e" radius={[4, 4, 0, 0]} />
-                                        <Bar dataKey="Manual" fill="#94a3b8" radius={[4, 4, 0, 0]} />
-                                    </BarChart>
                                 </ResponsiveContainer>
                             </div>
-                            <p className={cn("text-[10px] mt-2 font-medium", isLight ? "text-slate-400" : "text-slate-500")}>
+                        </div>
-                                Samples: AI pairs {data.responseTime.sampleAiPairs} · Manual pairs{" "}
+                    </section>
-                                {data.responseTime.sampleManualPairs}. Overall medians: AI{" "}
+
-                                {(data.responseTime.aiMedianSeconds / 60).toFixed(1)} min · Manual{" "}
+                    <section className="grid grid-cols-1 lg:grid-cols-2 gap-4 md:gap-6">
-                                {data.responseTime.manualMedianMinutes.toFixed(1)} min
+                        <div className={cn("rounded-2xl border p-4 md:p-6", isLight ? "bg-white border-slate-100 shadow-sm" : "bg-white/5 border-white/10")}>
+                            <h2 className={cn("text-lg font-black", isLight ? "text-slate-900" : "text-white")}>Growth Signals</h2>
+                            <p className={cn("text-xs mt-1 mb-4", isLight ? "text-slate-500" : "text-slate-400")}>
+                                Product curiosity and repeated requests that can guide buying, merchandising, and product-page content.
                             </p>
+                            {data.demandSignals.topProductQuestions.length === 0 ? (
+                                <EmptyState isLight={isLight}>Not enough product-question data yet.</EmptyState>
+                            ) : (
+                                <div className="space-y-2">
+                                    {data.demandSignals.topProductQuestions.map((row) => (
+                                        <div key={row.name} className={cn("flex items-center justify-between gap-3 rounded-xl border px-3 py-2", isLight ? "bg-slate-50 border-slate-100" : "bg-white/[0.04] border-white/10")}>
+                                            <span className={cn("text-sm font-bold truncate", isLight ? "text-slate-800" : "text-slate-200")}>{row.name}</span>
+                                            <span className={cn("text-xs font-black rounded-lg px-2 py-1", isLight ? "bg-indigo-100 text-indigo-700" : "bg-indigo-500/20 text-indigo-200")}>{row.count}</span>
+                                        </div>
+                                    ))}
+                                </div>
+                            )}
                         </div>
 
-                        <div
+                        <div className={cn("rounded-2xl border p-4 md:p-6", isLight ? "bg-white border-slate-100 shadow-sm" : "bg-white/5 border-white/10")}>
-                            className={cn(
+                            <h2 className={cn("text-lg font-black", isLight ? "text-slate-900" : "text-white")}>Attribution Health</h2>
-                                "p-4 md:p-6 rounded-xl md:rounded-2xl border min-h-[320px] flex flex-col",
-                                isLight ? "bg-white border-slate-100 shadow-sm" : "bg-white/5 border-white/10 backdrop-blur-xl",
-                            )}
-                        >
-                            <h3 className={cn("text-lg font-bold", isLight ? "text-slate-900" : "text-white")}>
-                                Revenue linked after AI storefront sessions
-                            </h3>
                             <p className={cn("text-xs mt-1 mb-4", isLight ? "text-slate-500" : "text-slate-400")}>
-                                Orders pinned during verified storefront chat (last ~30 days). Totals use cached Shopify
+                                Conservative matching keeps revenue accurate. Pending attribution means cart interest exists but no matching order has been cached yet.
-                                orders for your active store.
                             </p>
-                            <div className="flex-1 overflow-y-auto space-y-2 min-h-[180px] max-h-[260px] pr-1 custom-scrollbar">
+                            <div className="grid grid-cols-2 gap-3">
-                                {data.attributedSupportSales.rows.length === 0 ? (
+                                <MetricCard title="Cart conversion" value={formatPercent(data.revenueImpact.aiCartConversionRate)} helper="Orders matched to AI cart actions." accent={isLight ? "text-emerald-700" : "text-emerald-400"} isLight={isLight} />
-                                    <p className={cn("text-sm italic py-8 text-center", isLight ? "text-slate-400" : "text-slate-500")}>
+                                <MetricCard title="Pending attribution" value={data.revenueImpact.pendingAttributionCount.toLocaleString()} helper="Clicks awaiting a matching Shopify order." accent={isLight ? "text-amber-700" : "text-amber-300"} isLight={isLight} />
-                                        No bound orders yet — when customers verify and attach an order in chat,
+                                <MetricCard title="Cross-sell revenue" value={formatMoney(data.revenueImpact.directCrossSellRevenue, data.revenueImpact.currency)} helper="Shown separately from upsell revenue." accent={isLight ? "text-cyan-700" : "text-cyan-300"} isLight={isLight} />
-                                        conversions appear here.
+                                <MetricCard title="Avg influenced order" value={formatMoney(data.revenueImpact.averageAiInfluencedOrderValue, data.revenueImpact.currency)} helper="Average value for AI-assisted orders." accent={isLight ? "text-indigo-700" : "text-indigo-300"} isLight={isLight} />
-                                    </p>
-                                ) : (
-                                    data.attributedSupportSales.rows.map((row) => (
-                                        <div
-                                            key={row.id}
-                                            className={cn(
-                                                "flex items-center gap-3 p-3 rounded-xl border",
-                                                isLight ? "border-slate-100 bg-slate-50/80" : "border-white/10 bg-white/[0.04]",
-                                            )}
-                                        >
-                                            <div
-                                                className={cn(
-                                                    "w-10 h-10 rounded-full flex items-center justify-center text-xs font-black shrink-0",
-                                                    isLight ? "bg-indigo-100 text-indigo-700" : "bg-indigo-500/20 text-indigo-200",
-                                                )}
-                                            >
-                                                {row.initials}
-                                            </div>
-                                            <div className="min-w-0 flex-1">
-                                                <p className={cn("text-sm font-bold truncate", isLight ? "text-slate-900" : "text-white")}>
-                                                    {row.label}
-                                                </p>
-                                                <p className={cn("text-xs truncate", isLight ? "text-slate-500" : "text-slate-400")}>
-                                                    {row.summary}
-                                                </p>
-                                            </div>
-                                            <div className="text-right shrink-0">
-                                                <p className={cn("text-sm font-black", isLight ? "text-emerald-700" : "text-emerald-400")}>
-                                                    {row.amount != null
-                                                        ? formatMoney(row.amount, row.currency || data.attributedSupportSales.currency)
-                                                        : "—"}
-                                                </p>
-                                                <span
-                                                    className={cn(
-                                                        "text-[10px] font-bold uppercase px-2 py-0.5 rounded-md inline-block mt-1",
-                                                        "bg-emerald-500/15 text-emerald-500",
-                                                    )}
-                                                >
-                                                    AI session
-                                                </span>
-                                            </div>
-                                        </div>
-                                    ))
-                                )}
-                            </div>
-                            <div
-                                className={cn(
-                                    "mt-4 pt-4 border-t flex justify-between items-center",
-                                    isLight ? "border-slate-100" : "border-white/10",
-                                )}
-                            >
-                                <span className={cn("text-xs font-bold uppercase tracking-wide", isLight ? "text-slate-500" : "text-slate-400")}>
-                                    Total attributed (window)
-                                </span>
-                                <span className={cn("text-lg font-black", isLight ? "text-emerald-600" : "text-emerald-400")}>
-                                    {formatMoney(
-                                        data.attributedSupportSales.totalAmount,
-                                        data.attributedSupportSales.currency,
-                                    )}
-                                </span>
                             </div>
                         </div>
+                    </section>
+
+                    <details className={cn("rounded-2xl border p-4 md:p-5", isLight ? "bg-white border-slate-100 shadow-sm" : "bg-white/5 border-white/10")}>
+                        <summary className={cn("cursor-pointer text-sm font-black", isLight ? "text-slate-800" : "text-slate-200")}>
+                            Technical Diagnostics
+                        </summary>
+                        <div className={cn("grid grid-cols-2 md:grid-cols-4 gap-3 mt-4 text-sm", isLight ? "text-slate-600" : "text-slate-300")}>
+                            <p>AI messages: {data.technicalDiagnostics.storefrontAiMessages}</p>
+                            <p>Customer messages: {data.technicalDiagnostics.storefrontCustomerMessages}</p>
+                            <p>Analytics events: {data.technicalDiagnostics.analyticsEvents}</p>
+                            <p>Cached orders: {data.technicalDiagnostics.cachedOrders}</p>
                         </div>
+                    </details>
                 </>
             )}
         </div>

src/app/storefront-chat/widget/page.tsx

@@ -442,6 +442,32 @@ export default function StorefrontChatWidgetPage() {
         )
     }
 
+    const reportWidgetEvent = (message: WidgetMessage, action: Extract<WidgetAction, { type: "open_cart" }>) => {
+        if (!shop || !visitorId) return
+        const payload = JSON.stringify({
+            shop,
+            visitorId,
+            type: "cart_link_opened",
+            productId: action.product?.productId,
+            variantId: action.product?.variantId,
+            sourceMessageId: message.id,
+        })
+        try {
+            if (navigator.sendBeacon) {
+                navigator.sendBeacon("/api/storefront-chat/events", new Blob([payload], { type: "application/json" }))
+                return
+            }
+        } catch {
+            /* ignore and fallback */
+        }
+        void fetch("/api/storefront-chat/events", {
+            method: "POST",
+            headers: { "Content-Type": "application/json" },
+            body: payload,
+            keepalive: true,
+        }).catch(() => {})
+    }
+
     const renderMessageActions = (message: WidgetMessage) => {
         if (message.senderType !== "AI" || !message.actions?.length) return null
 
@@ -453,7 +479,10 @@ export default function StorefrontChatWidgetPage() {
                             <button
                                 key={`${message.id}-${action.type}-${index}`}
                                 type="button"
-                                onClick={() => window.open(action.cartUrl, "_blank", "noopener,noreferrer")}
+                                onClick={() => {
+                                    reportWidgetEvent(message, action)
+                                    window.open(action.cartUrl, "_blank", "noopener,noreferrer")
+                                }}
                                 className="w-full rounded-xl border border-emerald-200 bg-emerald-50 px-3 py-2 text-left text-xs font-bold text-emerald-800 hover:bg-emerald-100"
                             >
                                 {action.label}
@@ -469,10 +498,10 @@ export default function StorefrontChatWidgetPage() {
                             >
                                 <div className="flex gap-2">
                                     {action.product.imageUrl ? (
-                                        <img
+                                        <span
-                                            src={action.product.imageUrl}
+                                            aria-hidden="true"
-                                            alt=""
+                                            className="h-12 w-12 flex-shrink-0 rounded-lg bg-slate-100 bg-cover bg-center"
-                                            className="h-12 w-12 flex-shrink-0 rounded-lg object-cover"
+                                            style={{ backgroundImage: `url(${action.product.imageUrl})` }}
                                         />
                                     ) : null}
                                     <div className="min-w-0 flex-1">

src/lib/ai/orchestrator/runL1.ts

@@ -271,6 +271,56 @@ function extractLastCustomerContext(
     return null
 }
 
+function normalizeProductMatchText(input: string): string {
+    return input.toLowerCase().replace(/[^a-z0-9]+/g, " ").replace(/\s+/g, " ").trim()
+}
+
+function significantProductTerms(input: string): Set<string> {
+    const ignored = new Set([
+        "want",
+        "buy",
+        "some",
+        "sort",
+        "kind",
+        "need",
+        "looking",
+        "please",
+        "product",
+        "item",
+        "with",
+        "that",
+        "this",
+        "have",
+        "show",
+    ])
+    return new Set(
+        normalizeProductMatchText(input)
+            .split(" ")
+            .filter((term) => term.length >= 3 && !ignored.has(term)),
+    )
+}
+
+function chooseProductHitForActions(
+    hits: ProductSearchHit[],
+    replyText: string,
+    customerMessage: string,
+): ProductSearchHit | null {
+    const reply = normalizeProductMatchText(replyText)
+    const mentioned = hits.find((hit) => {
+        const title = normalizeProductMatchText(hit.title)
+        return title.length > 0 && reply.includes(title)
+    })
+    if (mentioned) return mentioned
+
+    const queryTerms = significantProductTerms(customerMessage)
+    if (queryTerms.size === 0) return hits[0] ?? null
+
+    return (
+        hits.find((hit) => hit.tokens.some((token) => queryTerms.has(token))) ??
+        null
+    )
+}
+
 async function composeReply(params: {
     storeName: string
     storeReference: string
@@ -745,27 +795,6 @@ export async function runL1Orchestrator(input: RunL1Input): Promise<RunL1Result>
         }
     }
 
-    const actions: StorefrontChatAction[] = []
-    let pendingCartProduct: StorefrontActionProduct | null = null
-    if (input.channel === "widget" && productHits.length > 0) {
-        const primaryHit = productHits[0]
-        const primaryProduct = productSearchHitToActionProduct(primaryHit)
-        if (primaryProduct) {
-            pendingCartProduct = primaryProduct
-            actions.push(buildAddToCartAction(primaryProduct, "primary"))
-            const upsells = await findAutomaticUpsellCandidates({
-                storeId: input.storeId,
-                baseProduct: primaryProduct,
-                query: effectiveMessage,
-                limit: 1,
-            })
-            if (upsells[0]) {
-                usedTools.push("automatic_upsell")
-                actions.push(buildUpsellSuggestionAction(upsells[0], primaryProduct.title))
-            }
-        }
-    }
-
     const facts = [
         type1Facts,
         kbContext ? `Knowledge:\n${kbContext}` : "",
@@ -782,6 +811,27 @@ export async function runL1Orchestrator(input: RunL1Input): Promise<RunL1Result>
         history: historyToGroq(input.history),
     })
 
+    const actions: StorefrontChatAction[] = []
+    let pendingCartProduct: StorefrontActionProduct | null = null
+    if (input.channel === "widget" && productHits.length > 0) {
+        const primaryHit = chooseProductHitForActions(productHits, text, effectiveMessage)
+        const primaryProduct = primaryHit ? productSearchHitToActionProduct(primaryHit) : null
+        if (primaryProduct) {
+            pendingCartProduct = primaryProduct
+            actions.push(buildAddToCartAction(primaryProduct, "primary"))
+            const upsells = await findAutomaticUpsellCandidates({
+                storeId: input.storeId,
+                baseProduct: primaryProduct,
+                query: effectiveMessage,
+                limit: 1,
+            })
+            if (upsells[0]) {
+                usedTools.push("automatic_upsell")
+                actions.push(buildUpsellSuggestionAction(upsells[0], primaryProduct.title))
+            }
+        }
+    }
+
     logger.info("[L1_ORCHESTRATOR]", {
         storeId: input.storeId,
         channel: input.channel,

src/lib/ai/tools/productTools.ts

@@ -73,11 +73,33 @@ function imageUrlFromImages(images: unknown): string | null {
 }
 
 function termsFromText(text: string) {
+    const ignored = new Set([
+        "want",
+        "wants",
+        "buy",
+        "some",
+        "sort",
+        "kind",
+        "need",
+        "needs",
+        "looking",
+        "please",
+        "product",
+        "products",
+        "item",
+        "items",
+        "show",
+        "have",
+        "with",
+        "that",
+        "this",
+        "your",
+    ])
     return text
         .toLowerCase()
         .split(/[^a-z0-9]+/g)
         .map((t) => t.trim())
-        .filter((t) => t.length >= 2)
+        .filter((t) => t.length >= 2 && !ignored.has(t))
 }
 
 export function productSearchHitToActionProduct(hit: ProductSearchHit): StorefrontActionProduct | null {
@@ -125,14 +147,22 @@ export async function searchProductsForStore(
 
         const scored: { p: (typeof products)[0]; score: number }[] = []
         for (const p of products) {
-            const hay = `${p.title} ${p.productType ?? ""} ${JSON.stringify(p.variants)} ${metafieldsToSummary(p.metafields) ?? ""}`.toLowerCase()
+            const title = p.title.toLowerCase()
+            const productType = (p.productType ?? "").toLowerCase()
+            const variantsText = JSON.stringify(p.variants).toLowerCase()
+            const metafieldsText = (metafieldsToSummary(p.metafields) ?? "").toLowerCase()
+            const hay = `${title} ${productType} ${variantsText} ${metafieldsText}`
             let score = 0
             for (const t of terms) {
+                if (title.includes(t)) score += 8
+                if (productType.includes(t)) score += 4
+                if (variantsText.includes(t)) score += 2
+                if (metafieldsText.includes(t)) score += 1
                 if (hay.includes(t)) score += 1
             }
             if (score > 0) scored.push({ p, score })
         }
-        scored.sort((a, b) => b.score - a.score)
+        scored.sort((a, b) => b.score - a.score || a.p.title.localeCompare(b.p.title))
 
         return scored.slice(0, limit).map(({ p }) => {
             const variants = extractCartableVariants(p.variants)

src/lib/analytics/aiEvents.test.ts

@@ -0,0 +1,25 @@
+import { describe, expect, it } from "vitest"
+import { classifyAiResolution, classifyCustomerSentiment } from "./aiEvents"
+
+describe("AI analytics event classifiers", () => {
+    it("keeps low-confidence sentiment conservative", () => {
+        expect(classifyCustomerSentiment("Do you have waterproof boots?")).toBe("neutral")
+    })
+
+    it("detects negative and urgent customer messages", () => {
+        expect(classifyCustomerSentiment("My parcel is missing and I want a refund")).toBe("negative")
+        expect(classifyCustomerSentiment("This is urgent and I need help immediately")).toBe("urgent")
+    })
+
+    it("classifies AI escalation and unresolved replies", () => {
+        expect(classifyAiResolution({ content: "A human will follow up", module: "HUMAN_ESCALATION" })).toBe(
+            "escalated",
+        )
+        expect(classifyAiResolution({ content: "I could not find that information right now." })).toBe(
+            "needs_follow_up",
+        )
+        expect(classifyAiResolution({ content: "Yes, this product is available in black." })).toBe(
+            "resolved_by_ai",
+        )
+    })
+})

src/lib/analytics/aiEvents.ts

@@ -0,0 +1,114 @@
+import type { Prisma } from "@prisma/client"
+import { prisma } from "@/lib/prisma"
+import { logger } from "@/lib/logger"
+
+export type AiAnalyticsEventType =
+    | "customer_question"
+    | "ai_answered"
+    | "add_to_cart_clicked"
+    | "cart_link_generated"
+    | "cart_link_opened"
+    | "upsell_shown"
+    | "upsell_clicked"
+    | "cross_sell_clicked"
+    | "ticket_created"
+    | "unresolved_question"
+    | "resolved_by_ai"
+    | "order_attributed"
+
+export type AiAnalyticsSentiment = "positive" | "neutral" | "negative" | "urgent" | "unknown"
+export type AiResolutionStatus = "resolved_by_ai" | "needs_follow_up" | "escalated" | "unknown"
+
+export type LogAiAnalyticsEventInput = {
+    storeId: string
+    conversationId?: string | null
+    messageId?: string | null
+    eventType: AiAnalyticsEventType
+    source?: string
+    productId?: string | null
+    variantId?: string | null
+    quantity?: number | null
+    amount?: number | null
+    currency?: string | null
+    sentiment?: AiAnalyticsSentiment | null
+    resolutionStatus?: AiResolutionStatus | null
+    orderId?: string | null
+    attributionType?: "direct_upsell" | "direct_cross_sell" | "assisted_order" | null
+    metadata?: unknown
+    createdAt?: Date
+}
+
+function toJsonInput(value: unknown): Prisma.InputJsonValue | undefined {
+    if (value === undefined) return undefined
+    return JSON.parse(JSON.stringify(value)) as Prisma.InputJsonValue
+}
+
+export function classifyCustomerSentiment(text: string): AiAnalyticsSentiment {
+    const t = text.trim().toLowerCase()
+    if (!t) return "unknown"
+    if (/\b(angry|furious|terrible|awful|scam|fraud|lawsuit|chargeback|urgent|asap|immediately)\b/.test(t)) {
+        return "urgent"
+    }
+    if (/\b(bad|poor|wrong|broken|damaged|late|missing|not received|disappointed|unhappy|refund|complaint)\b/.test(t)) {
+        return "negative"
+    }
+    if (/\b(thanks|thank you|great|love|good|perfect|helpful|awesome|excellent)\b/.test(t)) {
+        return "positive"
+    }
+    return "neutral"
+}
+
+export function classifyAiResolution(params: {
+    content: string
+    usedTools?: string[]
+    module?: string
+}): AiResolutionStatus {
+    const text = params.content.trim().toLowerCase()
+    const tools = params.usedTools ?? []
+    if (tools.includes("ticket_intake") || params.module === "HUMAN_ESCALATION") {
+        return "escalated"
+    }
+    if (
+        /\b(i don'?t know|do not have|don't have|could not find|cannot look up|can't look up|not available|human support|team can confirm|verify your email)\b/.test(
+            text,
+        )
+    ) {
+        return "needs_follow_up"
+    }
+    return text ? "resolved_by_ai" : "unknown"
+}
+
+export async function logAiAnalyticsEvent(input: LogAiAnalyticsEventInput): Promise<void> {
+    try {
+        await prisma.aiAnalyticsEvent.create({
+            data: {
+                storeId: input.storeId,
+                conversationId: input.conversationId ?? null,
+                messageId: input.messageId ?? null,
+                eventType: input.eventType,
+                source: input.source ?? "storefront_chat",
+                productId: input.productId ?? null,
+                variantId: input.variantId ?? null,
+                quantity: input.quantity ?? null,
+                amount: input.amount ?? null,
+                currency: input.currency ?? null,
+                sentiment: input.sentiment ?? null,
+                resolutionStatus: input.resolutionStatus ?? null,
+                orderId: input.orderId ?? null,
+                attributionType: input.attributionType ?? null,
+                metadata: toJsonInput(input.metadata),
+                ...(input.createdAt ? { createdAt: input.createdAt } : {}),
+            },
+        })
+    } catch (error) {
+        logger.warn("logAiAnalyticsEvent failed", {
+            eventType: input.eventType,
+            storeId: input.storeId,
+            message: error instanceof Error ? error.message : String(error),
+        })
+    }
+}
+
+export async function logAiAnalyticsEvents(inputs: LogAiAnalyticsEventInput[]): Promise<void> {
+    await Promise.all(inputs.map((input) => logAiAnalyticsEvent(input)))
+}

src/lib/analytics/revenueAttribution.test.ts

@@ -0,0 +1,108 @@
+import { describe, expect, it } from "vitest"
+import { computeAiRevenueAttribution, parseOrderLineItems } from "./revenueAttribution"
+
+describe("revenueAttribution", () => {
+    it("parses Shopify line item identifiers conservatively", () => {
+        expect(
+            parseOrderLineItems([
+                { product_id: 123, variant_id: 456, title: "Socks", quantity: 2, price: "4.50" },
+            ]),
+        ).toEqual([
+            { productId: "123", variantId: "456", title: "Socks", quantity: 2, price: 4.5 },
+        ])
+    })
+
+    it("attributes direct upsell revenue only when the clicked product is in a matching order", () => {
+        const result = computeAiRevenueAttribution({
+            events: [
+                {
+                    id: "event-1",
+                    conversationId: "conv-1",
+                    eventType: "upsell_clicked",
+                    productId: "p-upsell",
+                    variantId: "v-upsell",
+                    quantity: 1,
+                    createdAt: new Date("2026-05-01T10:00:00Z"),
+                },
+                {
+                    id: "event-2",
+                    conversationId: "conv-1",
+                    eventType: "customer_question",
+                    productId: null,
+                    variantId: null,
+                    quantity: null,
+                    createdAt: new Date("2026-05-01T09:59:00Z"),
+                },
+            ],
+            conversations: [
+                {
+                    id: "conv-1",
+                    customerEmail: "buyer@example.com",
+                    boundShopifyOrderId: null,
+                    createdAt: new Date("2026-05-01T09:58:00Z"),
+                    updatedAt: new Date("2026-05-01T10:02:00Z"),
+                },
+            ],
+            orders: [
+                {
+                    shopifyOrderId: "order-1",
+                    orderNumber: "#1001",
+                    email: "buyer@example.com",
+                    totalPrice: "50.00",
+                    currency: "GBP",
+                    shopifyCreatedAt: new Date("2026-05-01T10:10:00Z"),
+                    createdAt: new Date("2026-05-01T10:10:00Z"),
+                    lineItems: [
+                        { product_id: "p-base", variant_id: "v-base", title: "Shoes", quantity: 1, price: "40.00" },
+                        { product_id: "p-upsell", variant_id: "v-upsell", title: "Socks", quantity: 2, price: "5.00" },
+                    ],
+                },
+            ],
+        })
+
+        expect(result.directUpsellRevenue).toBe(10)
+        expect(result.aiAssistedRevenue).toBe(50)
+        expect(result.directRows).toHaveLength(1)
+        expect(result.assistedRows).toHaveLength(1)
+    })
+
+    it("does not inflate direct revenue without exact product or variant matching", () => {
+        const result = computeAiRevenueAttribution({
+            events: [
+                {
+                    id: "event-1",
+                    conversationId: "conv-1",
+                    eventType: "upsell_clicked",
+                    productId: "p-clicked",
+                    variantId: "v-clicked",
+                    quantity: 1,
+                    createdAt: new Date("2026-05-01T10:00:00Z"),
+                },
+            ],
+            conversations: [
+                {
+                    id: "conv-1",
+                    customerEmail: "buyer@example.com",
+                    boundShopifyOrderId: null,
+                    createdAt: new Date("2026-05-01T09:58:00Z"),
+                    updatedAt: new Date("2026-05-01T10:02:00Z"),
+                },
+            ],
+            orders: [
+                {
+                    shopifyOrderId: "order-1",
+                    orderNumber: "#1001",
+                    email: "buyer@example.com",
+                    totalPrice: "50.00",
+                    currency: "GBP",
+                    shopifyCreatedAt: new Date("2026-05-01T10:10:00Z"),
+                    createdAt: new Date("2026-05-01T10:10:00Z"),
+                    lineItems: [{ product_id: "p-other", variant_id: "v-other", title: "Shoes", quantity: 1, price: "50.00" }],
+                },
+            ],
+        })
+
+        expect(result.directUpsellRevenue).toBe(0)
+        expect(result.directRows).toHaveLength(0)
+    })
+})

src/lib/analytics/revenueAttribution.ts

@@ -0,0 +1,195 @@
+type EventLike = {
+    id: string
+    conversationId: string | null
+    eventType: string
+    productId: string | null
+    variantId: string | null
+    quantity: number | null
+    createdAt: Date
+}
+
+type ConversationLike = {
+    id: string
+    customerEmail: string | null
+    boundShopifyOrderId: string | null
+    createdAt: Date
+    updatedAt: Date
+}
+
+type OrderLike = {
+    shopifyOrderId: string
+    orderNumber: string
+    email: string | null
+    totalPrice: string
+    currency: string | null
+    lineItems: unknown
+    shopifyCreatedAt: Date | null
+    createdAt: Date
+}
+
+type LineItemLike = {
+    product_id?: unknown
+    productId?: unknown
+    variant_id?: unknown
+    variantId?: unknown
+    title?: unknown
+    name?: unknown
+    quantity?: unknown
+    price?: unknown
+}
+
+export type DirectRevenueRow = {
+    eventId: string
+    orderId: string
+    orderNumber: string
+    productId: string | null
+    variantId: string | null
+    title: string
+    amount: number
+    quantity: number
+    currency: string
+    attributionType: "direct_upsell" | "direct_cross_sell"
+    createdAt: Date
+}
+
+export type AssistedRevenueRow = {
+    orderId: string
+    orderNumber: string
+    amount: number
+    currency: string
+    conversationId: string
+    createdAt: Date
+}
+
+function asString(value: unknown): string | null {
+    if (value === null || value === undefined) return null
+    const text = String(value).trim()
+    return text ? text : null
+}
+
+function asNumber(value: unknown): number {
+    const n = Number(value)
+    return Number.isFinite(n) ? n : 0
+}
+
+export function parseOrderLineItems(raw: unknown): Array<{
+    productId: string | null
+    variantId: string | null
+    title: string
+    quantity: number
+    price: number
+}> {
+    if (!Array.isArray(raw)) return []
+    return (raw as LineItemLike[]).map((line) => ({
+        productId: asString(line.product_id ?? line.productId),
+        variantId: asString(line.variant_id ?? line.variantId),
+        title: asString(line.title ?? line.name) ?? "Product",
+        quantity: Math.max(1, Math.trunc(asNumber(line.quantity) || 1)),
+        price: asNumber(line.price),
+    }))
+}
+
+function orderDate(order: OrderLike): Date {
+    return order.shopifyCreatedAt ?? order.createdAt
+}
+
+function orderMatchesConversation(order: OrderLike, conversation: ConversationLike) {
+    if (conversation.boundShopifyOrderId && conversation.boundShopifyOrderId === order.shopifyOrderId) {
+        return true
+    }
+    const email = order.email?.trim().toLowerCase()
+    const convEmail = conversation.customerEmail?.trim().toLowerCase()
+    return Boolean(email && convEmail && email === convEmail)
+}
+
+export function computeAiRevenueAttribution(params: {
+    events: EventLike[]
+    conversations: ConversationLike[]
+    orders: OrderLike[]
+    attributionDays?: number
+}) {
+    const attributionMs = (params.attributionDays ?? 14) * 24 * 60 * 60 * 1000
+    const conversationById = new Map(params.conversations.map((c) => [c.id, c]))
+    const directRows: DirectRevenueRow[] = []
+
+    const directEvents = params.events.filter(
+        (event) => event.eventType === "upsell_clicked" || event.eventType === "cross_sell_clicked",
+    )
+
+    for (const event of directEvents) {
+        if (!event.conversationId) continue
+        const conversation = conversationById.get(event.conversationId)
+        if (!conversation) continue
+
+        const matchingOrders = params.orders.filter((order) => {
+            const createdAt = orderDate(order).getTime()
+            return (
+                createdAt >= event.createdAt.getTime() &&
+                createdAt <= event.createdAt.getTime() + attributionMs &&
+                orderMatchesConversation(order, conversation)
+            )
+        })
+
+        for (const order of matchingOrders) {
+            for (const line of parseOrderLineItems(order.lineItems)) {
+                const productMatch = event.productId && line.productId === event.productId
+                const variantMatch = event.variantId && line.variantId === event.variantId
+                if (!productMatch && !variantMatch) continue
+                directRows.push({
+                    eventId: event.id,
+                    orderId: order.shopifyOrderId,
+                    orderNumber: order.orderNumber,
+                    productId: line.productId,
+                    variantId: line.variantId,
+                    title: line.title,
+                    amount: Math.round(line.price * line.quantity * 100) / 100,
+                    quantity: line.quantity,
+                    currency: order.currency ?? "GBP",
+                    attributionType:
+                        event.eventType === "cross_sell_clicked" ? "direct_cross_sell" : "direct_upsell",
+                    createdAt: orderDate(order),
+                })
+            }
+        }
+    }
+
+    const aiConversationIds = new Set(
+        params.events
+            .filter((event) => event.eventType === "customer_question" || event.eventType === "ai_answered")
+            .map((event) => event.conversationId)
+            .filter((id): id is string => Boolean(id)),
+    )
+
+    const assistedRows: AssistedRevenueRow[] = []
+    const seenOrders = new Set<string>()
+    for (const conversation of params.conversations) {
+        if (!aiConversationIds.has(conversation.id)) continue
+        for (const order of params.orders) {
+            if (seenOrders.has(order.shopifyOrderId)) continue
+            if (!orderMatchesConversation(order, conversation)) continue
+            const amount = asNumber(order.totalPrice)
+            if (amount <= 0) continue
+            seenOrders.add(order.shopifyOrderId)
+            assistedRows.push({
+                orderId: order.shopifyOrderId,
+                orderNumber: order.orderNumber,
+                amount: Math.round(amount * 100) / 100,
+                currency: order.currency ?? "GBP",
+                conversationId: conversation.id,
+                createdAt: orderDate(order),
+            })
+        }
+    }
+
+    return {
+        directRows,
+        assistedRows,
+        directUpsellRevenue: directRows
+            .filter((row) => row.attributionType === "direct_upsell")
+            .reduce((sum, row) => sum + row.amount, 0),
+        directCrossSellRevenue: directRows
+            .filter((row) => row.attributionType === "direct_cross_sell")
+            .reduce((sum, row) => sum + row.amount, 0),
+        aiAssistedRevenue: assistedRows.reduce((sum, row) => sum + row.amount, 0),
+    }
+}

src/lib/api-schemas/public.ts

@@ -52,6 +52,15 @@ export const storefrontChatActionSchema = z.object({
     sourceMessageId: z.string().trim().max(128).optional(),
 })
 
+export const storefrontChatEventSchema = z.object({
+    shop: storefrontShopSchema,
+    visitorId: storefrontVisitorIdSchema,
+    type: z.enum(["cart_link_opened"]),
+    productId: z.string().trim().min(1).max(128).optional(),
+    variantId: z.string().trim().min(1).max(128).optional(),
+    sourceMessageId: z.string().trim().max(128).optional(),
+})
+
 /** Anonymous storefront widget message */
 export const storefrontChatPostSchema = z
     .object({
@@ -149,6 +158,7 @@ export const storefrontOtpVerifySchema = z.object({
 export type NewsletterSubscribeInput = z.infer<typeof newsletterSubscribeSchema>
 export type StorefrontChatPostInput = z.infer<typeof storefrontChatPostSchema>
 export type StorefrontChatActionInput = z.infer<typeof storefrontChatActionSchema>
+export type StorefrontChatEventInput = z.infer<typeof storefrontChatEventSchema>
 export type ChatMessagePostInput = z.infer<typeof chatMessagePostSchema>
 export type StorefrontOtpRequestInput = z.infer<typeof storefrontOtpRequestSchema>
 export type StorefrontOtpVerifyInput = z.infer<typeof storefrontOtpVerifySchema>

src/lib/api-schemas/schemas.test.ts

@@ -3,6 +3,7 @@ import {
     newsletterSubscribeSchema,
     storefrontChatPostSchema,
     storefrontChatActionSchema,
+    storefrontChatEventSchema,
     chatMessagePostSchema,
     CHAT_MESSAGE_CONTENT_MAX,
     CHAT_ATTACHMENT_JSON_MAX,
@@ -75,6 +76,29 @@ describe("storefrontChatActionSchema", () => {
     })
 })
 
+describe("storefrontChatEventSchema", () => {
+    it("accepts cart-link-opened events", () => {
+        const result = storefrontChatEventSchema.safeParse({
+            shop: "x.myshopify.com",
+            visitorId: "v1",
+            type: "cart_link_opened",
+            productId: "p1",
+            variantId: "v1",
+        })
+        expect(result.success).toBe(true)
+    })
+
+    it("rejects unknown storefront analytics events", () => {
+        expect(
+            storefrontChatEventSchema.safeParse({
+                shop: "x.myshopify.com",
+                visitorId: "v1",
+                type: "checkout_started",
+            }).success,
+        ).toBe(false)
+    })
+})
+
 describe("chatMessagePostSchema", () => {
     it("rejects oversize content", () => {
         expect(

src/lib/dashboard/analyticsTimeline.test.ts

@@ -0,0 +1,26 @@
+import { describe, expect, it } from "vitest"
+import { bucketKey, emptyTimeline, getAnalyticsWindow } from "./analyticsTimeline"
+
+describe("analyticsTimeline", () => {
+    it("defaults 30 day ranges to day grain", () => {
+        const window = getAnalyticsWindow({
+            range: "30d",
+            now: new Date("2026-05-11T12:00:00Z"),
+        })
+        expect(window.grain).toBe("day")
+        expect(window.labels).toHaveLength(30)
+    })
+
+    it("buckets weeks from Monday", () => {
+        expect(bucketKey(new Date("2026-05-11T12:00:00Z"), "week")).toBe("2026-05-11")
+        expect(bucketKey(new Date("2026-05-17T12:00:00Z"), "week")).toBe("2026-05-11")
+    })
+
+    it("creates zero-filled rows for every label", () => {
+        const rows = emptyTimeline(["2026-05-10", "2026-05-11"], "day", { questions: 0, revenue: 0 })
+        expect(rows).toEqual([
+            { key: "2026-05-10", label: "May 10", questions: 0, revenue: 0 },
+            { key: "2026-05-11", label: "May 11", questions: 0, revenue: 0 },
+        ])
+    })
+})

src/lib/dashboard/analyticsTimeline.ts

@@ -0,0 +1,118 @@
+import {
+    addDays,
+    addMonths,
+    addWeeks,
+    addYears,
+    format,
+    startOfDay,
+    startOfMonth,
+    startOfWeek,
+    startOfYear,
+    subDays,
+    subMonths,
+    subYears,
+} from "date-fns"
+
+export type AnalyticsRange = "today" | "7d" | "30d" | "90d" | "12m" | "year" | "all"
+export type AnalyticsGrain = "day" | "week" | "month" | "year"
+
+export type AnalyticsWindow = {
+    range: AnalyticsRange
+    grain: AnalyticsGrain
+    since: Date
+    until: Date
+    labels: string[]
+}
+
+export function parseAnalyticsRange(raw: string | null | undefined): AnalyticsRange {
+    if (raw === "today" || raw === "7d" || raw === "30d" || raw === "90d" || raw === "12m" || raw === "year" || raw === "all") {
+        return raw
+    }
+    return "30d"
+}
+
+export function defaultGrainForRange(range: AnalyticsRange): AnalyticsGrain {
+    if (range === "today" || range === "7d" || range === "30d") return "day"
+    if (range === "90d") return "week"
+    return "month"
+}
+
+export function parseAnalyticsGrain(
+    raw: string | null | undefined,
+    range: AnalyticsRange,
+): AnalyticsGrain {
+    if (raw === "day" || raw === "week" || raw === "month" || raw === "year") return raw
+    return defaultGrainForRange(range)
+}
+
+export function startOfBucket(date: Date, grain: AnalyticsGrain): Date {
+    if (grain === "day") return startOfDay(date)
+    if (grain === "week") return startOfWeek(date, { weekStartsOn: 1 })
+    if (grain === "year") return startOfYear(date)
+    return startOfMonth(date)
+}
+
+export function addBucket(date: Date, grain: AnalyticsGrain): Date {
+    if (grain === "day") return addDays(date, 1)
+    if (grain === "week") return addWeeks(date, 1)
+    if (grain === "year") return addYears(date, 1)
+    return addMonths(date, 1)
+}
+
+export function bucketLabel(date: Date, grain: AnalyticsGrain): string {
+    if (grain === "day") return format(date, "MMM d")
+    if (grain === "week") return `Week of ${format(date, "MMM d")}`
+    if (grain === "year") return format(date, "yyyy")
+    return format(date, "MMM yyyy")
+}
+
+export function bucketKey(date: Date, grain: AnalyticsGrain): string {
+    return format(startOfBucket(date, grain), "yyyy-MM-dd")
+}
+
+export function getAnalyticsWindow(params: {
+    range?: string | null
+    grain?: string | null
+    now?: Date
+} = {}): AnalyticsWindow {
+    const now = params.now ?? new Date()
+    const range = parseAnalyticsRange(params.range)
+    const grain = parseAnalyticsGrain(params.grain, range)
+    const until = now
+    const since =
+        range === "today"
+            ? startOfDay(now)
+            : range === "7d"
+              ? subDays(startOfDay(now), 6)
+              : range === "30d"
+                ? subDays(startOfDay(now), 29)
+                : range === "90d"
+                  ? subDays(startOfDay(now), 89)
+                  : range === "12m"
+                    ? subMonths(startOfMonth(now), 11)
+                    : range === "year"
+                      ? startOfYear(now)
+                      : subYears(startOfYear(now), 4)
+
+    const labels: string[] = []
+    let cursor = startOfBucket(since, grain)
+    const final = startOfBucket(until, grain)
+    while (cursor.getTime() <= final.getTime() && labels.length < 400) {
+        labels.push(bucketKey(cursor, grain))
+        cursor = addBucket(cursor, grain)
+    }
+
+    return { range, grain, since, until, labels }
+}
+
+export function emptyTimeline<T extends Record<string, number>>(
+    labels: string[],
+    grain: AnalyticsGrain,
+    seed: T,
+) {
+    return labels.map((key) => ({
+        key,
+        label: bucketLabel(new Date(`${key}T00:00:00`), grain),
+        ...seed,
+    }))
+}