Engagements - Turn your model into a real product: streaming, RAG, and usage billing
The demo wowed everyone. Then real users hit it and the cracks show: no streaming, no retries, token costs eating your runway, and no way to actually charge anyone. The model was never the hard part — the product around it is. A build turns your notebook or prompt into software: streaming that doesn't choke, retrieval that's grounded, and usage billing so it makes money.
- 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, 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.
- Usage metering wired to Stripe so you can meter tokens, enforce plan limits, and bill for what people actually consume.
- 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.
Weeks 1–3 · Gateway & core loop
We put a provider-agnostic 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.
Weeks 4–7 · RAG & the product
The retrieval pipeline gets built — ingestion, chunking, embeddings, a pgvector store, reranking, and citations — and wrapped in the actual product UI. It ships in weekly increments so you're testing retrieval quality on your own data the whole way, not at the end.
Weeks 8–10 · Metering, billing & launch
Usage metering, plan limits, and Stripe billing go in so the product can charge; background jobs handle the long-running and batch work; and we do a cost and reliability pass — caching, rate limits, guardrails — before flipping it 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.
- Usage metering and Stripe billing for AI: per-user token tracking, plan limits, overage metering, and usage-based invoicing.
- 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.
Stack
- Next.js
- Express
- PostgreSQL
- pgvector
- Stripe
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, and billing in weekly increments. Same flat monthly subscription, from $6,900/month, 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.
Frequently asked questions
- Do you train models or do ML research?
- No — we build the product around your model, not the model itself. Bring a provider API (OpenAI, Anthropic, others) or your own weights, and we build the streaming UI, RAG pipeline, gateway, metering, and billing that turn it into something people can use and pay for. The research stays yours.
- Can you build a RAG pipeline on our own documents?
- Yes — that's a core part of this build. 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 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.
- 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 $6,900 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. Subscribe today, hand off the first outcome, and we'll ship it in weekly increments. Smaller tasks still usually land in 48 to 72 hours. No call required.