Custom AI solutions and governance

Generic assistants may not understand your business context or safely execute the work you need. Design a focused assistant or agent around approved knowledge, bounded tools, and measurable task quality, with governance built into the delivery approach.

Capabilities

Assess task feasibility, business ownership, source quality, and permitted data use.
Design permission-aware knowledge retrieval, source references, and handling of uncertain answers.
Connect approved tools and Model Context Protocol servers with scoped permissions and action approvals.
Test representative tasks, denied access, misleading inputs, and safe failure behavior.
Define release decisions, human escalation, monitoring, change review, and retirement.

Deliverables

A solution blueprint defining task boundaries, knowledge sources, permissions, and decision ownership.
A scoped prototype demonstrating the agreed workflow and integration boundaries.
An evaluation dataset and results report covering task quality, groundedness, and failure cases.
A production-readiness backlog with release gates, monitoring requirements, and support ownership.

Business value

Establish whether a custom agent solves a real problem well enough to justify further investment. Measure task success, unsupported answers, safe failure, latency, and cost per useful interaction rather than relying on a polished prototype.

Start with a scoped use case and clear boundaries.

Bring representative tasks, approved information, an accountable owner, and intended integrations. Start with a bounded prototype; production deployment and expanded permissions require separate readiness and authorization decisions.

Copilot Studio, Foundry, Entra, Purview