How I'd build InspectionAI
I'd build this on Next.js and Postgres, with Stripe for billing and an async job queue (BullMQ on Redis) to handle the photo-to-report pipeline. The frontend is Next.js App Router with shadcn/ui, the backend is Python FastAPI running the Claude vision model calls and orchestrating report generation, and Stripe webhooks sync the billing state. Rough estimate: 280-320 hours start to paying customer, accounting for image processing debugging and E&O compliance review.
Day-by-day plan
Day 1-2: Scaffold Next.js app with Postgres auth (next-auth), multi-tenant schema (org_id partitioning), and Stripe customer creation on signup. Day 3: Wire Stripe checkout for three tiers (Starter $29/mo, Professional $99/mo, Team $299/mo) and handle subscription state changes. Day 4-5: Build the core upload flow - S3 bucket setup, file validation, queue job submission via BullMQ. Day 6-7: Python FastAPI service that pulls queued jobs, calls Claude vision API with the photo and inspection template, streams output to Postgres. Day 8: Build the report review UI - inline editing, regenerate on specific sections, export to PDF via Puppeteer. Day 9: Add invitation and team management so inspectors can share reports with clients. Day 10-11: Metrics dashboard with reports generated, trial-to-paid conversion rates, and support ticket queue visibility. Day 12-14: Load testing, compliance audit documentation, and staging parity.
What's hard about this build
The hardest part is photo quality inference and graceful degradation. Field photos are consistently underlit, angled, or motion-blurred. Claude's vision model is good but not magic: if it hallucinates a damp stain that isn't there, or misses actual mold, that's a liability lawsuit for the inspector. I'd build guardrails: confidence scoring on each finding, require human review for high-risk categories (electrical, structural, moisture), and a "confidence flag" workflow that surfaces to the report before delivery. The second gotcha is E&O insurance. We'd need to audit the exact language in our disclaimers with a lawyer, maybe carry E&O ourselves, and document that inspectors remain liable for final report contents. Third: onboarding complexity. Inspectors are not SaaS-native. They need video walkthroughs, phone support during trial, and integration with their existing client CRM if they have one. Underestimating this kills conversion.
What's fast because of AI
Claude compresses the entire scaffolding phase. With Claude and the agentic workflow, I skip weeks of UI spec-writing and just generate working shadcn/ui components from wireframes. Test coverage accelerates because I use Claude to enumerate edge cases (badly lit photos, corrupted image uploads, truncated EXIF data, inspector vs viewer role boundaries) and generate test fixtures automatically. Copywriting for email flows, the onboarding tour, and report disclaimers is 80% written by Claude: I refine tone and legal accuracy, but the heavy lifting is automated. Debugging the Claude vision integration is faster because Claude helps me reason through API response structures, handle partial failures, and map inference confidence scores. What used to be two weeks of trial-and-error around photo preprocessing becomes three days of Claude-guided iteration.
How I'd hand it off
I'd record a Loom walkthrough of the full user flow from signup to report delivery, covering key menus and the admin dashboard. I'd write a runbook covering database schema, environment variables, Stripe API key rotation, BullMQ job monitoring, and E&O escalation steps. You'd get 30 days of on-call support where I'm the first responder for production bugs (avg. SLA 4 hours), then hand off to your ops team. Credentials transfer via 1Password with audit trail. Available for one follow-up week of ad-hoc questions as your team ramps.