How I'd build Solo Analytics
I'd build this as a Next.js application on the frontend with a separate Python FastAPI agent service handling the weekly data pulls and analysis. The frontend connects to Postgres for user state and integration credentials, Stripe for billing, and a message queue (I'd use Bull/Redis) to coordinate the agent jobs. Rough estimate is 200-250 hours for a launch-ready MVP: that's auth, billing, three data connectors (Stripe, GA4, Shopify), the agent logic, notification delivery, and a basic dashboard to show historical briefs. Add another 40 hours for DevOps and monitoring.
Day-by-day plan
- Days 1-2: Provision Postgres schema for users, integrations, and stored signals. Set up NextAuth with email magic links. Establish the FastAPI agent skeleton with task scheduling via APScheduler.
- Days 3-4: Integrate Stripe API for the three pricing tiers. Wire up subscription status checks and customer portal embeds. Implement webhook handlers for failed payments and upgrades.
- Days 5-6: Build the onboarding flow - OAuth for Stripe and Google Analytics 4, credential storage encrypted in Postgres, test data pulls to verify each integration works end-to-end.
- Days 7-8: Implement the agent's comparative analysis logic - current period vs. prior period vs. 90-day baseline, statistical flagging (>1 standard deviation), and cause-chain inference for the three primary metrics.
- Days 9-10: Add the notification pipeline - email via Resend, Slack webhook ingestion, Telegram bot scaffolding. Store briefs in Postgres and generate a timeline view for longitudinal comparison.
- Days 11-12: Build the customer dashboard - brief history with search and filtering, integration health status, manual re-sync triggers, and preference controls for notification frequency and threshold sensitivity.
- Days 13-14: Hardening - webhook idempotency keys, exponential backoff for third-party API failures, circuit breakers for flaky endpoints. Set up Sentry error tracking and structured logging.
- Day 15: Record a Loom walkthrough, write the runbook, deploy to Vercel and Railway.
What's hard about this build
The real friction lives in the data connectors. Stripe's API distinguishes between MRR movements by reason (new, expansion, contraction, churn, and refunds), but the schema is cryptic and the edge cases are numerous - partial refunds, proration credits, dunning retries that succeed silently, subscription pauses that don't affect MRR the way founders expect. GA4 requires careful event validation because most solopreneur Webflow and Framer builds ship with broken instrumentation out of the box; scroll depth won't fire, form submissions won't register, and the agent needs to detect and flag these gaps rather than baking bad data into the brief. The third risk is notification delivery itself - Slack rate limiting, Telegram bot timeouts, and email deliverability have different failure modes that need graceful degradation. Finally, credential escrow is non-negotiable; if a customer connects their Stripe API key or GA4 service account and you store it plaintext, a database breach exposes their business directly and gives attackers enumeration vectors into their customer base.
What's fast because of AI
Claude accelerates the entire agent logic layer - writing the statistical comparison functions, enumerating Stripe's refund and proration edge cases, and drafting the cause-chain inference rules takes maybe four hours of pair-coding instead of two days of design and testing. Scaffolding the FastAPI and Next.js boilerplate cuts another day. For UI copy - the brief narrative format, dashboard labels, onboarding microcopy - Claude writes a passable first draft in minutes; I refine tone and test comprehension in one more hour instead of a day of iteration. Debugging is where the payoff is largest: when GA4 returns blank sessions or Stripe webhook signature validation fails, Claude enumerates root causes and suggests targeted log output, saving half a day per incident. Edge-case test coverage - scenario matrices for refunds, partial chargebacks, canceled trials - gets generated in bulk, which lets me ship with confidence instead of spending a week on manual test enumeration.
How I'd hand it off
I'd record a Loom covering the admin panel (user and billing management, integration health dashboard), agent job logs, and credential rotation. The runbook documents deployment (Railway redeploy, Vercel promotion), common failure modes (GA4 event misconfiguration, Stripe webhook retry logic), and clear escalation paths. I'd include a 30-day pager rotation schedule with success metrics: zero unhandled agent crashes, <2% data pull failures, <1% notification delivery loss. Credentials transfer via encrypted passphrase manager with audit logging. One async weekly check-in for the first month to catch operational surprises before they compound.