How I'd build ManufactureAI
I'd reach for Next.js on the frontend, FastAPI for the backend, Postgres for multi-tenant data, Stripe for billing, and Resend for transactional email. The quoting engine itself - the core of this product - would be a thin Python service that ingests job histories, learns shop-specific pricing patterns, and outputs bid recommendations via the API. Rough estimate: 400-420 hours, which puts us comfortably inside the $32k budget with room for deployment and documentation.
Day-by-day plan
Day 1: Provision auth schema with Clerk or Auth0, set up multi-tenant isolation at the database layer, and scaffold the main Next.js app structure. Day 2: Wire Stripe billing across three pricing tiers, set up webhook handlers for subscription lifecycle events. Day 3-4: Build the customer onboarding flow - email verification, shop details form, initial quoting template import. Day 5-6: Build the core quoting interface and the FastAPI backend that takes job history as CSV input and surfaces pricing recommendations. Day 7: Integrate Stripe's white-label quoting setup - allow customers to invite their team members and set permissions. Week 2: Build the analytics dashboard showing save rate on quoted jobs, conversion tracking back to actual bids. Week 3: Spin up the per-shop training data ingestion - let shops upload their historical job data, label it, and retrain the quoting model. Week 4: Testing, edge cases, database migration scripts. Week 5: Deploy to staging, run through the GTM flow as a customer would see it, iterate. Week 6: Production deployment, monitoring setup, Datadog alerts on quoting latency.
What's hard about this build
The real risk isn't the tech - it's the data problem. Quoting is tribal knowledge. A shop's pricing reflects decades of material supplier relationships, machine utilization patterns, labor cost structure, and how the owner thinks about margin. An AI that doesn't learn this quickly becomes a liability. We need the onboarding to extract enough historical job data in the first week to train a model that's accurate within the shop's pricing variance. If we miss that window, churn is high. The second risk is incumbent lock-in. Most shops run JobBoss or Shoptech or E2 Shop, and their entire quoting workflow lives there. We're not replacing the ERP - we're sitting alongside it as a "second brain" for better pricing. But switching cost is real labor, not just money. The third risk is the trust barrier. AI isn't a selling point to a skeptical shop owner; a saved lost bid is. Demos have to show a real saved job, not a list of features.
What's fast because of AI
Claude compresses weeks into days on scaffolding - the multi-tenant schema, API routes, middleware patterns. I prompt for the entire database migration strategy and get a working schema in an hour instead of building it from scratch. Test generation is huge: I write one test case for quoting logic and Claude enumerates edge cases - material surcharges, rush fees, setup costs, minimum orders - then generates parametrized tests. Copywriting: UI microcopy, email sequences for the GTM, onboarding docs. Instead of writing ten emails myself, I describe the customer journey and Claude generates options I shape. Debugging is faster too. When a quoting calculation is off by 3 percent on a specific material type, Claude helps me trace through the logic, spot the rounding error, and write the fix.
How I'd hand it off
I'd record a Loom walkthrough covering the admin panel, customer onboarding workflow, and how to run a quoting job end-to-end. Runbook goes in a private wiki: how to backup the Postgres database, how the Stripe webhook handler works, how to scale the quoting service if throughput spikes. I'd do 30 days of pager rotation - any tier-2 issues come to me first, then I hand them over with context. Tools and credentials: GitHub repo access, Linear for bug tracking, Stripe and Postgres credentials stored in 1Password with instructions for rotation.