Generative AI Consulting and Implementation

Most organisations are past the question of whether generative AI matters and stuck on the question of where to apply it without creating risk. The usual state is a scattering of departmental pilots, an unclear policy on what staff may put into a chat window, procurement pressure from a dozen vendors adding an AI tier, and no shared view of which opportunities are actually worth funding.

Our consulting engagements produce three things. A prioritised opportunity portfolio: use cases scored on value, feasibility, data readiness and risk, with a recommended sequence that front-loads the ones that build capability rather than the ones that demo best. A governance framework: an acceptable-use policy people can follow, data classification determining what may reach which model, human oversight requirements graded by decision stakes, model and vendor evaluation criteria, and the documentation trail that emerging regulation such as the EU AI Act expects. And a build-versus-buy analysis, because for a great many use cases the correct answer is to configure something that exists.

Then we implement the cases that clear the bar. Typically that means internal knowledge assistants grounded in your documentation with permission-aware retrieval, copilots embedded in the tools staff already use, document processing that extracts and validates structured data from unstructured input, or customer-facing assistants with tightly bounded scope and clean human escalation. Each ships with an evaluation harness, cost controls and an audit trail.

We also invest in your people. Enablement sessions for the teams who will operate these systems, prompt and evaluation practice for the internal owners, and a written operating model covering who approves a new use case and on what evidence — so the capability keeps compounding after the engagement ends.