AI strategy from people who actually build it.
Strategy and architecture for teams adopting AI — from picking the right model and designing the data pipeline to writing the roadmap and evals. We are engineers who ship production AI, so the advice is grounded in what actually works, not vendor hype or a slide deck.
Who it's for
- Leaders who know AI matters but need a clear, honest plan before spending on a build.
- Engineering teams that want a second opinion on architecture, model choice, or evals.
- Companies burned by an AI project that over-promised and want a realistic path.
- Teams that need a RAG or data pipeline designed right the first time.
What you get
Model & vendor selection
A model-agnostic recommendation based on your use case, cost, and data — not a kickback.
Data & RAG architecture
How to structure, retrieve, and ground your data so answers are accurate and traceable.
Roadmap
A prioritised, realistic plan: what to build first, what to defer, and what to skip.
Eval strategy
How to measure quality so you can ship with confidence and catch regressions.
Cost & risk review
Where inference cost, latency, and compliance risk hide — and how to control them.
Team enablement
Practical training so your team can carry the work forward.
How we work
Audit
Understand your goals, data, stack, and constraints.
Recommend
Model choice, architecture, and a prioritised roadmap with trade-offs.
Enable
Walk your team through the plan and the evals; optional hands-on build follow-on.
Tools & tech we use
We are model- and tool-agnostic — this is a typical stack, chosen per project for your use case, cost, and data.
How we work
We sign NDAs
Mutual NDA before anything sensitive is shared. Your data and ideas stay yours.
Evals & observability
Every build ships with eval pipelines and tracing, so it keeps working after launch.
Honest timelines
Most projects ship in 3–6 weeks. We scope tight and tell you the truth about dates.
You own everything
Your code, your repos, your cloud accounts. Documented handover, no lock-in.
Typical first engagement · 1 week+. Larger or more complex work is scoped individually — book a call and we'll talk it through.
See full pricing →AI Integration Consulting FAQ
The questions we get asked most before a project starts.
It covers model and vendor selection, data and RAG architecture, a prioritised roadmap, an eval strategy, and a cost and risk review — delivered by engineers who build production AI, so the recommendations are practical and implementation-ready.
AI integration consulting starts from $1,500 for a focused engagement (1 week+). Many clients use it to de-risk a decision before a larger build, and the cost is often credited toward the implementation if they proceed with us.
No. We are model-agnostic — OpenAI, Anthropic, and open-source models depending on the use case. Recommendations are based on your requirements, cost, and data, not a partnership incentive.
Yes. Consulting often leads into a build, but it does not have to — you can take the roadmap to any team. If you do continue with us, the work moves straight from plan to implementation.
Tell us what you want to ship.
A 30-minute call to scope your project. If we're a fit, we'll send a proposal within 48 hours.