Add AI to your product without the rewrite.
LLM features, RAG pipelines, and agentic workflows integrated into the product you already have — shipped to production with evals, observability, and cost controls, not left as a fragile demo. I've shipped 12 production AI systems, and the pattern is always the same: the model is the easy part; the engineering around it is the product.
- A working AI feature in your product — chat, search, extraction, summarization, or automation — scoped to a real user problem
- RAG pipelines that actually retrieve the right context: chunking, embeddings, reranking, and the eval data to prove it
- Agent workflows with function calling and structured outputs, constrained so they can act without going off the rails
- An eval harness in CI so prompt and model changes can't silently degrade quality
- Cost architecture: token budgets, caching, model routing, and per-user metering if you're billing for AI
- Observability from day one — cost per request, latency, and failure modes on a dashboard, not in your inbox
Feasibility & scope (week 1)
I dig into your codebase, data, and the user problem. You get a technical SoW: what we're building, the model and retrieval strategy, what it will cost to run, and a fixed price to build it.
Build against evals
The feature is developed against an eval suite from day one — real examples from your domain, scored automatically. You see progress in a weekly demo and daily async updates.
Production hardening
Fallbacks, rate limits, cost caps, prompt versioning, and monitoring wired in before launch — the difference between a feature and an incident generator.
Launch + 30 days
We ship behind a flag, roll out, and I stay for 30 days to tune quality and cost against real traffic. You get handoff docs your team can actually maintain.
Intavue — an AI interview-prep platform with a real-time voice interviewer, multi-agent coach, and code-grading sandbox. Built solo, end to end, running live on paid plans.
How to add AI to your existing web app without rewriting it — the integration playbook I use on client work.
Why your RAG pipeline is probably broken — the retrieval failures I fix most often, and how to detect them.
Fixed-scope with milestones — you know the price before anyone writes code. The 30-minute intro call is free, and if I think AI is the wrong tool for your problem, I will say so on that call.
Can you add AI to our existing app, or do we need a rebuild?
Almost always your existing app. AI features integrate at the API layer — your frontend calls a new endpoint, and the intelligence lives behind it. I've added LLM features to codebases I'd never seen before without touching the core product. A rebuild is only on the table if the codebase blocks you for reasons that have nothing to do with AI.
Which AI providers and models do you work with?
OpenAI, Anthropic, and Google models day to day, plus ElevenLabs for speech and Pinecone or Supabase pgvector for retrieval. I pick per use case — and I build behind a provider seam, so when a better or cheaper model ships, you swap it in config, not in a rewrite.
How do you keep AI API costs under control?
Cost is an architecture decision, not a surprise. I set per-feature token budgets, cache aggressively, route simple requests to cheaper models, and meter usage per user or workspace. If you're monetizing AI features, I'll wire credit metering so your margin per request is known, not guessed.
How do you test features when the output is different every time?
With an eval harness, not manual spot checks. I define expected behaviours, score outputs automatically (including LLM-as-judge where it fits), and run the suite in CI — so a prompt tweak or model upgrade can't silently break production. I've written publicly about exactly how this works.
What does 'production-grade' actually include?
Observability (logs, traces, cost per request), fallback policies for provider outages, rate limiting, prompt versioning, and a rollback path. A demo that works once is a weekend project; a feature your users hit 10,000 times a day is engineering.
What does an engagement look like?
A free 30-minute call, then a fixed-scope SoW with milestones and a price. Typical AI integrations run 2–6 weeks from $6k, with daily async updates and a demo every week. After launch I stay for 30 days to fix what production reveals.