Streaming, done properly
Tokens on screen immediately, cancellable, and resilient to a dropped connection — the difference between fast and broken.
AI feature build-out
Ship the AI features you sketched
The demo worked. Then it needed streaming, retries, cost caps, and an answer for what happens when the model is wrong. That's the part we build.
In short
AI feature build-out is the work of turning a prompt that works in a playground into a feature that holds up in production: streaming so it feels fast, retrieval so answers are grounded in your data, tool use so the model can act, and the cost caps, timeouts, evaluations and fallbacks that keep it predictable. Gen2Prod builds these on the current Claude models inside your existing codebase, and instruments them so you can see what each feature costs per request.
- Typical timeline
- 1–3 weeks per feature
- Models
- Claude Opus, Sonnet and Haiku, plus whatever you already use
- Patterns
- Streaming, RAG, tool use, agents, structured output
- Always includes
- Cost controls, evaluations and a fallback path
You probably need this if…
- The prompt works in the playground and falls apart in the app.
- Responses arrive all at once after nine seconds of nothing.
- The model invents facts about your own data.
- Nobody knows what a single user costs you in tokens.
- There's no plan for what happens when the provider is down.
What ai feature build-out covers
Retrieval over your data
Chunking, embeddings and a vector store wired so answers cite your content instead of inventing it.
Tool use & agents
The model calling your functions with validated arguments, inside limits you set, with every call logged.
Structured output
Schema-validated responses you can store and render, instead of parsing prose and hoping.
Cost & latency controls
The model chosen per task, prompt caching where it pays, token caps per user, and a dashboard showing what each feature costs.
Evaluations
A test set that catches the regression when a prompt changes, so prompt edits stop being a coin flip.
How we run it
- 1
Scope the feature
What it does, what it must never do, and how you'll know it worked — agreed before a single prompt is written.
- 2
Build instrumented
The feature ships with logging, cost tracking and an evaluation set from day one, rather than bolted on after the first bill.
- 3
Tune against the evals
Model, prompt and retrieval tuned until quality and cost both land where you need them.
What you get back
- The feature, shipped and streaming, in your own codebase
- An evaluation set you can run before every prompt change
- Cost per request visible, with caps enforced
- A fallback path for when the provider is slow or down
- A short note on why each model and pattern was chosen
Tools we reach for
- Claude API (Opus, Sonnet, Haiku)
- Vercel AI SDK
- RAG with pgvector & vector stores
- Prompt caching
- Tool use & function calling
- Structured output schemas
- Evals & tracing
AI feature build-out — questions we get asked
Which model should we use?
Usually more than one. Small fast models handle classification and routing; the strongest model handles the reasoning users actually notice. We choose per task and show you the cost and quality difference on your own data rather than on a benchmark.
How do we stop it hallucinating about our data?
Ground it. Retrieval puts the relevant source text in front of the model, structured output constrains the shape of the answer, and evaluations catch the cases where it still goes wrong. It never reaches zero, so we also build the path for when the answer is wrong.
What will this cost per month to run?
It's a function of tokens, model and traffic, and it's knowable before you launch. We instrument cost per request during the build and can hold it under a cap — we'd rather show you the number than surprise you with it.
Can you add AI to the app we already have?
That's the usual case. We work inside your existing codebase and stack; nothing here requires a rebuild.
Often paired with
All servicesHandoff & docs
The goal isn't to keep you on a retainer. It's to hand back something a new engineer can be productive in on day one.
Learn moreRefactor & cleanup
Lovable, Bolt, v0 and Cursor write code fast — and they write it flat. We reshape it into modules with clear boundaries, so the next feature takes an afternoon instead of a week.
Learn morePerformance tuning
A prototype is fast because it holds twelve rows. We profile the paths your users actually take, then fix the ones that fall over at scale.
Learn more