Three ways to ship AI that actually moves the business.

Whether you need a roadmap, an internal team that knows what they're doing, or a working product in production — pick the engagement that matches where you are.

a / Build

Implementation

Technical build & continuous ownership

End-to-end design of agentic and generative AI products
Data pipeline, governance, and evaluation harness
Vendor & model selection grounded in cost and risk
Production rollout with monitoring and feedback loops
Continuous ownership — not a drop-and-leave delivery

b / Plan

Strategy

Defining the roadmap and prioritising ROI

AI opportunity mapping across your business units
Use-case scoring by feasibility, ROI, and risk
12–24 month roadmap with measurable milestones
Responsible-AI policy aligned with EU AI Act and ISO 42001
Executive briefings & board-level decision support

c / Teach

Enablement

Education and training for the internal team

AI fluency curriculum (Anthropic-qualified educator)
Hands-on workshops for product, data, and ops teams
Prompt & agent design playbooks tailored to your stack
Champion programs to scale know-how internally
Change management and stakeholder alignment

How an engagement actually goes.

Every engagement runs through the same four phases — the depth and ownership shifts depending on the service.

01 / Discover

Listen first

Stakeholder interviews, data audit, and a candid look at what's already been tried — and why it stalled.

02 / Frame

Pick the right problem

Score use cases by feasibility, ROI, and risk. Commit to one or two — the rest go on the roadmap, not the backlog.

03 / Build / Teach

Ship something real

A working prototype in weeks, a trained internal team, or a published roadmap — whichever the engagement calls for.

04 / Embed

Make it stick

Hand-off with playbooks, evaluation harness, and ownership loops so the work survives after the engagement ends.