Overview
Solutions
Explore connected business solutions for finance, operations, customer service and production AI.
AI evaluation, governance & security
Make AI performance something you can explain.
Evaluate AI quality, security, cost and operating risk with documented tests, ownership and release controls.
Understand the workBuild the right capabilityMeasure what changes
What we build
A model that performs well in a demonstration may behave differently with real business data. We build task-specific evaluation sets, inspect failure modes and define who can approve changes. Security and governance become part of the operating system for AI, with evidence available for review.
- Model and use-case inventory
- Grounding, robustness and access-boundary evaluation
- Human oversight and release criteria
- Cost, latency, drift and incident monitoring
Start with the right foundations
Business scenarios, permitted test data, risk priorities, current models and prompts, and the people accountable for deployment.
Measure what changes
Track unsupported answers, policy violations, regressions and cost per accepted result. Evaluate outcomes across relevant languages and user groups.
From a defined scope to a working service
We agree the workflow, operating responsibilities and acceptance criteria first. A focused implementation validates the important assumptions before broader integration, rollout and handover. Ongoing support is scoped around the service and the team using it.
Common questions
What does a ai evaluation, governance & security engagement begin with?
Business scenarios, permitted test data, risk priorities, current models and prompts, and the people accountable for deployment. We use discovery to turn these inputs into a defined scope and acceptance plan.
How do you evaluate the result?
Track unsupported answers, policy violations, regressions and cost per accepted result. Evaluate outcomes across relevant languages and user groups.