How I'd build PodcastAI
I'd reach for Next.js on the frontend with a FastAPI backend in Python, Postgres for the database, and Stripe for billing. I'd integrate with an audio transcription service (likely Deepgram or Whisper API) to avoid building that from scratch, then layer AI enhancement on top using Claude's API for show notes and editing suggestions. The full build including onboarding, dashboard, affiliate tracking, and initial GTM infrastructure runs about 180 to 220 hours at your budget.
Day-by-day plan
Day 1: Provision Postgres multi-tenant schema. User, workspace, and subscription tables. Set up Clerk for auth to save time on password resets and social login.
Day 2: Wire Stripe billing. Three tiers defined, webhooks configured for subscription changes and cancellations, trial periods enforced.
Day 3-4: Build the onboarding flow. Podcast metadata capture, Stripe payment form, empty-state dashboard with a CTA for the first upload.
Day 5-6: Audio upload endpoint and S3 integration. Implement resumable uploads via Uppy on the frontend. Queue transcription jobs in a Celery worker.
Day 7: Transcription webhook receiver and database storage. Parse Deepgram or Whisper output, strip silence, and store raw transcript.
Day 8: Claude API integration for show notes and editing suggestions. Take the transcript, pipe it through Claude with a system prompt tuned for podcast content. Cache the prompt to reduce API costs on repeated requests.
Day 9: Customer dashboard. Show upload history, transcript status, generated content, and basic analytics on workspace usage.
Day 10-11: Landing page and affiliate integration. SEO-friendly copy, a blog section for content seeding (RSS-ready), and Refersion or similar for tracking affiliate revenue.
Day 12: Hardening. Rate limits on endpoints, PII scrubbing in logs, export user data compliance, cancel subscription flow.
What's hard about this build
Audio processing at scale kills most SaaS MVPs. Storage is cheap, but compute isn't. A 90-minute podcast burning 3-5 minutes of CPU time through a transcription service eats margin fast. I'd offload that to Deepgram or Whisper API immediately rather than self-hosting; the unit cost is worth it for simplicity. The harder piece is retention. Podcasting has 70% annual churn by design, so your CAC needs to be aggressively low. That means affiliate partnerships have to convert at 5-8% or the unit economics break. The final risk is product positioning against Descript, which owns that market. You're not out-featuring them. You're instead building a narrow, faster, cheaper alternative for podcasters who don't need video editing or the Descript ecosystem. That narrative has to be locked before you ship.
What's fast because of AI
Claude compresses scaffolding from days to hours. I generate Stripe integration boilerplate, the Celery worker skeleton, and the Postgres schema with one prompt, then refine the output manually. Test coverage gets written 3x faster because Claude enumerates edge cases I'd otherwise miss, like partial upload resumption and webhook retries on failure. Copywriting for error messages and onboarding UI takes minutes instead of days. And when debugging unusual behavior, Claude's systematic approach to root-cause analysis saves me the cognitive load of tracing through logs manually. The biggest win is the pager handoff documentation, which I generate directly from the codebase and then verify against Loom recordings.
How I'd hand it off
I'd record a Loom walkthrough of the core user flow: upload, transcribe, generate notes, export. The runbook lives in a shared Notion doc and covers Stripe key rotation, database backups, Deepgram quota monitoring, and common support escalations. You'd have a 30-day pager rotation where I'm on-call at half rate, answering Slack questions and fielding production alerts. You'll receive Stripe and database credentials in a secure 1Password vault, along with Claude API and Deepgram keys. The Linear board is yours to manage. At day 31, you're live and independent.