Practice

AI

How we help clients put AI to work safely — principles, services, in-flight initiatives, and the cadences that keep the practice grounded.

Principles

We start from a measurable workflow, not a model.
We ship the smallest useful evaluation before the first feature.
We treat prompts, retrieval, and tools as code — versioned, tested, and reviewed.
We design for the failure modes; the happy path takes care of itself.

Map a client's workflows to where AI moves the needle today, where it doesn't, and what to build first.

End-to-end delivery of AI features — retrieval, evaluation, observability — through to production rollout.

Stand up internal copilots and agents on top of the client's own knowledge, with proper access controls.

A focused review covering data readiness, security posture, evaluation discipline, and team capability.

DOING

Reusable eval harness for client engagements — prompts, judges, regressions.

TODO

Internal guide for choosing among current frontier models per use case.

DONE

Reference patterns for tool use, planning, and memory across engagements.

BLOCKED

Default RAG architecture — blocked on Security review.