ChatTracker: Turn Support Chats Into Revenue Insights

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1. 1. Project Setup

   Build the weekend MVP of "ChatTracker": it reads a small business's support chats and says in plain words where prospects stall.
   
   Stack: Next.js (App Router), TypeScript, Tailwind, Supabase (Postgres, Row Level Security, Auth with email magic link), the OpenAI API for classification and the weekly briefing, a Slack incoming webhook for follow-up alerts. Chats arrive through one generic webhook this weekend. Deploy on Vercel with a Vercel Cron route.
   
   Tables (Row Level Security on, scoped to the account's owner):
   - accounts(id, user_id, name, slack_webhook_url) slack_webhook_url is read on the server only
   - sources(id, account_id, name, webhook_secret)
   - conversations(id, account_id, source_id, external_id, transcript jsonb, topic, sentiment, outcome, key_phrases text[], follow_up_recommended boolean, processed_at) outcome is one of converted, stalled, unresolved, abandoned
   - insights(id, account_id, week_start, stats jsonb, recommendations jsonb, generated_at)
   
   Screens: /login, /conversations, /insights, /settings (the webhook URL and the Slack URL).
   Env vars (names only): NEXT_PUBLIC_SUPABASE_URL, NEXT_PUBLIC_SUPABASE_ANON_KEY, SUPABASE_SERVICE_ROLE_KEY (server only), OPENAI_API_KEY, CRON_SECRET.
   
   Do not build: billing, plans or an agency add-on, OAuth connections to chat tools, Intercom, Drift or Zendesk imports, email delivery, a job queue.
   Done when: npm run dev starts, you can sign in, and the four tables exist with Row Level Security on.

2. 2. Core Feature

   Build the one feature that proves ChatTracker: classify every chat, then turn a week of them into a short briefing.
   1. POST /api/webhooks/chat accepts { external_id, transcript: [{ role, text, at }] } with the source's webhook_secret in a header, and inserts a conversation. Reject a bad secret.
   2. A cron route (nightly, protected by CRON_SECRET, also callable from a Run now button) takes each unprocessed conversation and sends the transcript to OpenAI with a strict JSON schema { topic, sentiment: "positive" | "neutral" | "negative", outcome: "converted" | "stalled" | "unresolved" | "abandoned", key_phrases: string[], follow_up_recommended: boolean }. Save the result and set processed_at.
   3. The weekly pass counts topics, sentiment and outcomes in plain code and saves the numbers to insights.stats. Then ask the model for 3 to 5 plain-language recommendations written only from those numbers. It must not state any figure that is not in stats.
   4. If follow_up_recommended is true and outcome is "stalled", post the topic and a one-line summary to the account's Slack webhook within the same cron run.
   5. /insights shows the numbers first, then the recommendations under them.
   Rules: a failed model call leaves processed_at empty so the next run retries it. Never log a full transcript.
   Empty state: with no conversations, show the webhook URL and a sample curl command.
   Done when: three posted sample chats are classified, the weekly briefing cites only numbers that appear in stats, and a stalled chat sends a Slack message.

3. 3. Landing Page

   Build a one-page landing site for ChatTracker, chat analytics for small businesses.
   Hero: "Your chatbot has answers. You just can't see them yet." Sub: "ChatTracker reads every support conversation and tells you where deals stall, what customers keep asking and who needs a follow-up before they go cold." One button: Get early access, which joins the waitlist.
   Sections: a sample insight card ("5 prospects showed high intent but never got a follow-up"), the problem (chat volume without chat insight), how it works in four steps (connect, read, brief, alert), and an FAQ on data privacy, which chat tools work first and how long setup takes.
   Waitlist: store the email in a waitlist table in Supabase. No other service.
   Style: Geist, an editorial layout with an off-white background, generous whitespace and one teal accent.
   Done when: the page renders on a phone and a submitted email appears in the waitlist table.

4. 4. Branding Package

   Use a design or image tool for this one. A coding agent cannot draw a logo.
   Brand for ChatTracker: a wordmark and an icon that suggest a speech bubble with a rising line inside it.
   Colors: ink, off-white and one teal. Type: Geist, with tabular numbers for stats.
   Deliverables: wordmark, icon, three outcome tags (converted, stalled, abandoned) that differ by shape as well as color, and one launch graphic built from a sample insight card.
   Done when: each deliverable is saved in one folder and the outcome tags are distinguishable without color.