Context
The situation.
Guidance, templates, and precedent were spread across document libraries, inboxes, and individual experience. Search returned documents rather than answers.
Leadership was interested in AI but had not defined which decisions could be assisted, which had to remain human, or how outputs would be evaluated.
Approach
What we did.
- 01
Use-case and risk assessment
Candidate use cases were ranked by value, data readiness, feasibility, and the consequences of an incorrect answer.
- 02
Knowledge audit
Content sources, owners, quality, review dates, and access requirements were mapped before any model work began.
- 03
Prototype with evaluation
A retrieval-based assistant was built over approved sources, with citations, access controls, and a defined test set.
- 04
Governance and adoption
Review responsibilities, escalation to a person, feedback, and monitoring were agreed before wider rollout.
Outcome
What changed.
- The firm established a governed knowledge base with named owners and review dates.
- The assistant was piloted with a defined user group and an evaluation routine rather than an open launch.
- Decisions that required professional judgment remained with people, with the assistant limited to retrieval and drafting support.
These case studies describe representative engagement patterns. Client identities, specific figures, and attributable results are omitted. They are not testimonials, and they do not represent a promise of a particular outcome. Sector and engagement pattern are described; the client is not identified.
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