Engagements that deliver running AI systems.

Each engagement is scoped to your team and stage. We start with a short discovery, agree on a measurable outcome, and stay until the system is in real use.

AI Readiness Audit

Where you are, what's holding you back, and the shortest path through.

A two-week deep-dive into your AI surface area. We interview the people building, deploying, and owning AI features, then deliver a written assessment with a prioritised backlog.

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AI Strategy & Roadmap

Decision frameworks, build-vs-buy, and a quarterly plan tied to revenue.

We work alongside your leadership team to translate AI ambition into a sequenced plan: where to invest, what to defer, and the trade-offs each call carries.

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Production Build Squads

A specialist squad alongside your engineers — evals-first from day one.

A small principal-level team partners with your engineers for 8–16 weeks to design and deliver a real AI system. Code lives in your repo, on your CI, on day one.

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Team Enablement

Workshops, internal docs, and pairing so your engineers own what we built.

Hands-on training built around your codebase and your team's level. We pair, document patterns, and run internal workshops until your engineers are confident.

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Value Engineering

Cost, latency, and quality tuned to the business outcome — not the demo.

We profile your AI workloads end-to-end and surface the highest-leverage levers — model routing, caching, prompt design, retrieval tuning — then deliver the optimisations.

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LLM Operations

Observability, evals-in-CI, and on-call playbooks for AI systems in prod.

The boring infrastructure that makes AI features dependable: structured tracing, regression evals on every PR, alerting that reflects user-visible quality.

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