Services - An AI product development agency, sized to one senior engineer.
Every AI product development agency can wire up a chat demo. Fewer can ship one that survives real users: retrieval that's actually relevant, costs that don't blow up, evals so you know when a change makes it worse. This is one senior, founder-led engineer who takes your AI product from prompt-in-a-notebook to a billable feature in production, one priority at a time.
Plans tailored to the build
One senior engineer, one priority at a time
One senior, founder-led engineer takes this build week by week and ships production-ready code as pull requests you review and merge. Time and cost vary by plan and scope, and we’ll cover both before you start.
What's included
- RAG done properly: ingestion and chunking, embeddings, a vector store, retrieval that returns relevant context, and citations back to the source — not a raw prompt stuffed with your whole knowledge base.
- Chat and agent features wired into your product with streaming responses, tool/function calling, and guardrails, built on your real data and permissions rather than a generic playground.
- The LLM plumbing that keeps it reliable and affordable: provider abstraction, retries and fallbacks, caching, token/cost tracking, and rate limits so one user can't run up your bill.
- Usage-based billing for AI features — metering tokens or actions and charging through Stripe — so the product pays for the model calls instead of quietly eating your margin.
- Evals and observability so you can change a prompt or model and see whether quality went up or down, with logging and tracing instead of vibes.
- The product surface around the model: the screens, history, feedback, and admin tools that turn a raw API call into something customers pay for.
How we build it, week by week
One priority at a time, shipped in reviewable increments you merge as they land — not one big drop at the end.
Week 1 — scope the AI use case and de-risk it
We pin down the one AI feature worth shipping first, prototype the retrieval or agent loop against your real data, and get an honest read on quality and cost before building the product around it.
Weeks 2–3 — the reliable core
The retrieval or agent pipeline built for production: provider abstraction, caching, guardrails, and evals, shipped as PRs you review. Streaming responses and citations wired into a real screen.
Weeks 3–4 — billing, limits, and the edges
Usage metering and Stripe billing so AI features pay for themselves, plus rate limits, cost tracking, and the admin surface to watch spend and quality in production.
Ongoing — tune with real usage
Once customers use it, evals and logs show what to improve. Prompt, retrieval, and model changes become the next priorities on the board, shipped weekly for as long as the build needs it.
Stack
- Next.js / React
- Node / Express
- OpenAI / Anthropic APIs
- Vector DB (pgvector / Pinecone)
- PostgreSQL
- Stripe usage billing
Is this the right build for you?
A good fit when
- You have an AI product or feature — a RAG assistant, an agent, an AI SaaS MVP — that needs to work in production, not just demo once.
- You want one senior engineer who understands both the model plumbing and the product around it, owning the build week by week.
- You care about cost, reliability, and evals, not just getting a first response back from an API.
Not a fit when
- You want pure ML research or to train a foundation model from scratch — this is applied AI product engineering, not a research lab.
- You need a team of specialists across data science, MLOps, and frontend in parallel to hit a hard date — that's a larger agency.
- You just want a one-off prompt or a throwaway demo — cheaper, faster tools cover that.
Builds like this
Real month-plus builds you can read end to end, delivered on the same subscription.
Why on a subscription
AI products move fast and unevenly: a hard build to get the core reliable, then constant tuning as models change and users push the edges. A flexible monthly plan you can pause gives you a senior engineer for the build and keeps them on tap for the iteration, with no agency retainer to negotiate and no ML salary to carry between pushes.
Frequently asked questions
- How are you different from a typical AI product development agency?
- An agency assembles a team and prices the engagement up front. This is one senior, founder-led engineer on a flat monthly subscription, working one priority at a time. You talk directly to the person building it, get weekly PRs you review and merge, and can pause anytime — no project manager in the middle and no fixed-bid contract to renegotiate when the model or scope shifts.
- Do you train custom models?
- Usually no, and that's deliberate. Most AI products win on good retrieval, solid prompting, evals, and product design around existing frontier models — not on training your own. We build the applied layer: RAG, agents, tooling, billing, and the product surface. If you genuinely need custom training, we'll tell you straight and help you scope who does.
- How do you keep AI costs from blowing up?
- Cost control is built in, not bolted on: caching, provider fallbacks, token and cost tracking per user, rate limits, and usage-based billing so the product charges for what it spends. You see spend in an admin view instead of finding out on the invoice.
- Do I own the code and the prompts?
- Fully. Everything ships as pull requests into your repository — the pipeline, the prompts, the evals — with no proprietary wrapper. When the subscription ends you keep all of it.
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.