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AI Integration.

Adding AI to existing products — agents, RAG, MCP servers, model orchestration. Skeptical of hype, serious about evaluation.

Typical shape
Systems Design 18%Infra 8%DevOps 7%Data 19%AI 25%Backend 14%Frontend 9%
Leading
AI25%Data19%Systems Design18%
Typical length6 weeks – 6 months
Team size1 senior, sometimes 2
Next availabilityOne engagement at a time — booking now

Overview

AI Integration is what it sounds like: taking a product that already exists and adding AI where it actually earns its place — agents, retrieval, MCP servers, model orchestration. Not a chatbot bolted onto a marketing page. We're skeptical of hype, but we've shipped real AI work that held up under measurement.

When this fits

  • You have a product and want to add AI features that are genuinely useful, not a checkbox.
  • "Chatbot" is the floor for what you're imagining, not the ceiling.
  • You want someone skeptical of hype who's still shipped real AI work.
  • You care more about whether it works than whether it ships fast.

What you get

Each engagement delivers a written audit, a working prototype against your own data, a production integration, an evaluation suite that runs in CI, and the operational notes the team that takes over needs.

  • Architectural memo with options and a recommendation
  • End-to-end prototype on real data, before we commit to scope
  • Production integration with observability and cost guards
  • Evaluation suite running in CI from day one
  • Handover doc for the team that maintains it

What we won't do

  • Slap a model call on something and call it done.
  • Use AI where deterministic code is faster and cheaper.
  • Promise outcomes we can't measure.
  • Take the work if we don't think AI is the right answer for it.

How we work

Five moves, in order. We don't skip Audit or Measure — the difference between AI that earns its place and AI that's theater is whether you can prove it works.

  1. Audit (1–2 weeks). Understand the existing product end to end. Read the code. Talk to the people who use it.
  2. Locate (1 week, written memo). Propose where AI earns its place — and, more importantly, where it doesn't.
  3. Prototype (2–4 weeks). Build a working version against real data before committing to scope. End-to-end, ugly is fine.
  4. Measure (continuous). Instrument what we ship — an eval suite, a regression set, the metrics that actually move.
  5. Iterate (until it's boringly good). Production with evaluation running. Tune prompts, swap models, harden the bits that matter.

Fixed quote per integration, weekly retainer for ongoing work — let's talk about what you're building.

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