Engagements - AI product build for founders: one senior engineer, one outcome, weekly shipped increments
The demo wowed everyone. Then real users hit it and the cracks show: no streaming, no retries, no evals to tell you whether last week's prompt change made answers worse, token costs eating your runway, and no way to actually charge anyone. The model was never the hard part — the product around it is. An AI product build hands that whole product to one senior engineer for a month or more: not a queue of tickets and not a rotating product team, but one person, one outcome at a time, shipping an increment you review every week. Whether it's an AI SaaS MVP from an empty repo or a RAG product build on top of your own documents, you see working software on a weekly demo from week one.
- AI startups
- Pre-seed
- Model-first teams
What you'll have at launch
A production AI product wrapped around your model: streaming UI, a RAG pipeline, evals you can trust, usage metering, and billing you can charge on.
- A streaming chat or generation UI that renders token-by-token, handles stop and regenerate, and recovers when the model errors mid-stream.
- A RAG pipeline behind a clean API — chunking, embeddings, a vector store, retrieval with reranking, and citations back to the source.
- AI usage billing wired to Stripe so you can meter tokens, enforce plan limits, and charge for what people actually consume.
- An eval suite and tracing on every request, so a prompt or model change is a measured decision instead of a guess.
- An LLM gateway with fallback routing and caching, so one provider's outage doesn't take your product down.
How we build it
A build runs 6–10 weeks typical, shipped week by week in increments you review and merge.
Week 1 · Discovery & scoping
Before any code, we agree on the one outcome this build is for — the workflow, the users, the quality bar, and what 'good enough to charge for' means. You get a written scope, a phase plan, and the eval cases we'll hold the product to. Anything that isn't on the path to that outcome gets parked, not silently built.
Weeks 2–3 · Gateway, streaming & the core loop
We put a provider-agnostic LLM gateway in front of your model — retries, timeouts, caching, fallback routing — then build the core generation loop with real streaming over SSE. This is the layer that keeps prod up when a provider hiccups, so it goes in first. You're clicking a deployed streaming UI by the end of week two.
Weeks 4–7 · RAG architecture, evals & the product
The retrieval pipeline gets built — ingestion, chunking, embeddings, a pgvector store, reranking, and citations — and wrapped in the actual product UI. Evals run against your real documents from the first retrieval commit, so answer quality is a number on a dashboard you both watch, not a vibe. Weekly increments you review and merge.
Weeks 8–10 · Usage billing, observability & launch hardening
AI usage billing goes in — token metering, plan limits, overage, Stripe invoicing — alongside tracing, cost dashboards, and rate limits. Then the launch pass: guardrails on runaway usage, background jobs for long-running inference, error handling, a staging run, and the flip to live.
What's included
- A streaming chat/generation UI with token-by-token rendering, stop/regenerate, message persistence, and graceful mid-stream error handling.
- A RAG pipeline: document ingestion, chunking, embeddings, a pgvector store in PostgreSQL, retrieval with reranking, and citations passed to the frontend.
- A provider-agnostic LLM gateway with fallback routing, retry-with-backoff, response caching, and per-key rate limiting.
- AI usage billing on Stripe: per-user token tracking, plan limits, overage metering, and usage-based invoicing you can actually charge on.
- Evals and observability: a regression suite over real cases, per-request tracing, latency and cost dashboards, so you know when a prompt or model swap makes things worse.
- A background job queue for long-running inference and batch jobs, with completion webhooks and a status endpoint your frontend can poll.
- Cost controls that protect your runway: caching, cheaper-model routing where it's good enough, and guardrails on runaway usage.
What a build like this looks like
Scopes we've shaped this engagement around. Yours is one of them, or close enough that week one settles it.
- AI SaaS MVP. An empty repo to a paying AI SaaS MVP: auth, the streaming product surface, subscription and usage billing, and a deploy you own. Roughly the SaaS MVP build with an inference layer down the middle.
- RAG product build. Your documents, contracts, or knowledge base turned into a grounded, cited answer product — ingestion, embeddings, reranking, and an eval set that proves retrieval quality against real questions before launch.
- AI usage billing retrofit. You already ship an AI feature but can't charge for it. Token metering, plan limits, overage, and Stripe usage-based invoicing get wired into the product you have, without a rewrite.
- Cost and reliability rescue. The product works and the bill doesn't. A gateway, response caching, cheaper-model routing, rate limits, and tracing go in until unit economics and p95 latency are both things you can defend to an investor.
Stack
- Next.js
- Express
- PostgreSQL
- pgvector
- Stripe
How this build lowers your risk
- You see working software every week. Each week ends with a deployed increment and a demo, not a status update. If the build is going somewhere you don't like, you find out in week two on a live URL, not in month three on a slide.
- Quality is a number, not an opinion. Evals are written during discovery and run on every change from the first retrieval commit. Prompt and model decisions get made against a regression suite over your own cases, so nobody has to argue about whether answers got worse.
- One senior engineer, one outcome. No account manager, no rotating product team, no juniors learning your codebase on your budget. One person holds the whole system in their head and works one priority at a time — which is why a build lands instead of sprawling.
- No contract, no lump-sum quote. It runs on the flat monthly subscription, with multiple plans available. You pay for the active weeks, reprioritize mid-build as user feedback lands, and pause between phases. Everything ships into your repo and your cloud account as it's built, so leaving is never a hostage negotiation.
Is this the right build for you?
A good fit when
- You have a model, a prompt, or a notebook that works, and no product around it.
- You want one senior engineer for a month or more, not a task queue or a five-person pod.
- You can spend an hour a week reviewing a demo and making the calls only a founder can make.
- Streaming UX, retrieval quality, or usage billing is what stands between you and charging money.
Not a fit when
- You need ML research, model training, or fine-tuning — we build the product around your model, not the model.
- You want a one-week fix or a handful of small tickets; a build is a month-plus engagement by design.
- You need a large team shipping several workstreams in parallel from day one.
- The scope can't be narrowed to a single outcome for the next several weeks.
How it runs on the subscription
An AI product is weeks of connected engineering, so it runs as a build — the board dedicated to your product, shipping the gateway, the RAG loop, evals, and AI usage billing in weekly increments. We match the monthly plan to the workload, with most builds landing in six to ten weeks. We build the product, not the model: bring your provider or weights and reprioritize as user feedback reshapes the plan. Pause between phases anytime, and after launch the same subscription flips to task mode for the steady stream of prompts, evals, and screens a live AI product generates.
Frequently asked questions
- Do you train models or do ML research?
- No — this is AI product development, not model development. Bring a provider API (OpenAI, Anthropic, others) or your own weights, and we build the streaming UI, RAG pipeline, gateway, evals, metering, and billing that turn it into something people can use and pay for. The research stays yours.
- How is this different from hiring a product team or an AI agency?
- A product team gives you several part-time people, a process, and a coordination tax. An AI product build gives you one senior engineer working one outcome at a time for a month or more, shipping an increment you review every week. Fewer people who understand the whole system beats more people who each understand a slice of it — and you're not paying for the standups between them.
- Can you build a RAG pipeline on our own documents?
- Yes — a RAG product build is one of the most common shapes this takes. Ingestion, chunking, embeddings, a pgvector store, retrieval with reranking, and citations back to the source so answers are grounded and traceable. We tune retrieval against your real documents during the build and hold it to an eval set, so quality is measured, not assumed.
- How do you keep inference costs from blowing up?
- Cost control is built in, not bolted on: response caching, routing simpler calls to cheaper models where quality holds, per-key rate limits, and guardrails against runaway usage. The metering layer also means you can see and bill for exactly what each user consumes.
- Can you make the product actually charge for AI usage?
- Yes. AI usage billing is part of the build: per-user token metering, plan limits, overage, and Stripe usage-based invoicing, plus the customer billing portal for self-serve plan changes. If billing is the only piece you're missing, the Stripe billing system build covers that on its own.
- What if we switch LLM providers later?
- That's exactly what the gateway is for. Your product talks to one internal interface, and the gateway routes to whichever provider is behind it — with fallback if one is down. Swapping a provider or adding a second becomes a config change, not a rewrite.
- Is the selected plan the full price for the AI product?
- No. It's the flat monthly subscription the build runs on, not a fixed total. Most AI product builds take six to ten weeks, so you pay for the active months and can pause between phases. There's no contract and no lump-sum project quote.
Got a project? Let's ship it.
3 spots open. Tell us what you need shipped. We’ll match the plan and timeline to the work on a short sales call, then deliver it in reviewable increments.