The whole thing, front to back — frontend, backend, auth, payments, and data — with the AI feature wired in properly and an eval + guardrail layer that keeps it correct and safe in production. Deployed, documented, and owned by your team at handoff. The same discipline behind the products I run myself.
Design system, frontend, backend, auth, payments, and data model — on Next.js + Supabase or your stack. The boring-but-critical parts done right.
Assistant, agent, or RAG pipeline built as a first-class part of the backend — structured outputs, schema validation, fallbacks — not a fragile demo call.
The differentiator: a golden set, safety probes, and a CI gate that catches a wrong or unsafe answer before your customer does. Most builds ship without this.
Tracing, per-run cost tracking, and drift monitoring, so quality decay and runaway spend show up on a dashboard instead of in a support ticket.
Live in your accounts, with a runbook and a walkthrough so your team runs it without me — and the work outlives me being unavailable.
fixed scope · quoted after a spec-and-architecture week · your repo, your accounts, your stack
A scoped spec and architecture — the stack, the data model, the AI surface, and where the eval and guardrail layer goes. A concrete plan and quote you own.
Start here — get a quote →One real, deployed slice: the core flow end to end, with auth and the AI feature live behind a basic gate — something you can put in front of a user, not a prototype.
Get a quote →The production product: frontend, backend, auth, payments, data, the AI feature, and the eval + guardrail layer — deployed with a runbook your team owns at handoff.
Get a quote →fixed-price starting points from $497, plus a custom quote for larger or unusual builds — scoped in writing before we start, so you pay for your problem, not a package · every engagement ends with a product you own — and if the scoping shows I can’t help, I’ll say so and it costs nothing
An AI-native learning platform — catalog, lesson player, in-browser code labs, certificates, and an admin back office — designed, built, and deployed end to end by one engineer. The AI tutor is RAG-grounded so it answers from the course material, not its imagination, behind the same eval discipline I ship for clients.
Design system, frontend, backend, auth, payments, data, the AI tutor, and the eval layer — one owner, one coherent product.
Live in production with real users — the grounded tutor answers from source, with a quality gate behind it.
One of several products I build and run under Sage Ideas. The method on a client build is the same.
The whole thing — frontend, backend, auth, payments, data model, and deploy — with the AI feature wired in properly rather than bolted on. If you already have an app and just need the AI surface built and made safe, that's in scope too.
Next.js + Supabase is my default for speed and ownership, deployed on Vercel — but I work in your stack if you have one. The point is a product your team can maintain, not one locked to my tools.
Both. I run a design system with a real typography and motion language — the same discipline behind this site and the products I ship — so you get something that looks intentional, not a default template.
The eval and guardrail layer. Most teams can build the UI; the hard part is an AI feature that stays correct and safe in production. Every build ships with the regression and safety gate that catches a wrong or unsafe answer before your customer sees it.
Scoped and quoted in writing after a short call — fixed scope, not open-ended hours. Most engagements start with a spec-and-architecture week, and that fee credits into the build if you continue.
Everything — the code in your repo, the deploy in your accounts, the eval suite, and a runbook and walkthrough so your team runs it without me. Nothing is retained or locked to my platform.
You leave with a concrete plan either way — the call is free and the plan is yours.