Overview
AI integration consulting 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.
- Audit (1–2 weeks). Understand the existing product end to end. Read the code. Talk to the people who use it.
- Locate (1 week, written memo). Propose where AI earns its place — and, more importantly, where it doesn't.
- Prototype (2–4 weeks). Build a working version against real data before committing to scope. End-to-end, ugly is fine.
- Measure (continuous). Instrument what we ship — an eval suite, a regression set, the metrics that actually move.
- 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.
Common questions
What does an AI integration consultant actually do?
Figures out where AI earns its place in your product — and where it doesn't — then builds it, measures it, and hands it over. The consulting part is the audit and the written memo; the part that matters is that the same people ship the production integration and the evaluation suite behind it.
How much does AI integration consulting cost?
Depends entirely on scope, which is why every engagement starts with a short audit and a fixed quote before we commit. Ongoing work runs as a weekly retainer. If the audit says AI is the wrong answer for your problem, we tell you that instead of quoting.
How long does it take?
Small agent automations are usually live in a week or two. Product integrations — retrieval over your own data, an assistant inside the app, an MCP server — run six weeks to six months depending on how much of the pipeline already exists.
We're not sure AI fits our product. Is that a fit?
That's the best starting point, honestly. The Locate step exists to answer exactly that question, in writing, before any build spend. Describe the situation to the scoping assistant and we'll take a look.