AI you can explain to a regulator.
Evaluation, guardrails, audit trails and responsible-AI controls designed into every system we build, and available on their own for AI you already run.
Governance added at the end never quite fits.
Regulators, auditors and boards now ask the same questions about AI: what does it decide, on what evidence, who checks it, and how do you know it still works?
Those questions are hard to answer for a system designed without them. Logs are missing, evaluation is ad hoc, and nobody owns the model once the project team moves on.
We design governance in from the first sprint (evaluation sets, approval thresholds, audit trails and ownership) and can retrofit the same controls to AI you already run.
What we build and run for you.
Evaluation frameworks
Test sets, scoring and regression checks that run on every change to prompts, models or data.
Guardrails
Input and output checks for sensitive data, policy violations and out-of-scope requests.
Audit and traceability
Every input, retrieval, tool call and decision logged and replayable.
Operating model
Roles, approvals and review cadence for AI in production, aligned to your risk framework.
Production, not pilots. Built to run inside your operations.
Every agent we ship has an owner, an evaluation set and an audit trail before it touches a live case.
Governance as engineering, not paperwork.
Classify
riskRate each AI use case by impact and set the controls it needs.
Test
evalsAgree evaluation sets with the business and gate releases on them.
Trace
auditLog every step of every run in a form auditors can review.
Review
cadenceMonitor quality and drift, with named owners and scheduled reviews.
- Risk tiering per use case
- Release gates on evaluation results
- PII detection and redaction
- Data residency by deployment design
- Model and prompt version history
Where it earns its keep.
Illustrative patterns from the sectors we work in. Client details stay anonymised.
Audit-ready decision support
Every recommendation stored with its evidence, citations and the officer's final decision.
Model risk alignment
Evaluation and monitoring mapped to existing model-risk governance.
Governance retrofit
Add evaluation, logging and ownership to AI already in production.
Responsible-AI policy to practice
Turn a written AI policy into concrete controls in the delivery pipeline.
Chosen for the job, not the vendor.
Work this builds on.
A regulator's entire licensing lifecycle, rebuilt as one digital platform
A single integration backbone and Centre of Excellence for a global healthcare group
Client details anonymised per delivery agreements.
Questions we get asked.
How do you handle data sovereignty for AI workloads?
By choosing the deployment topology that fits the requirement. Azure OpenAI in a regional Azure tenancy, AWS Bedrock in the relevant region, on-prem inference on open-weight models, or hybrid where retrieval runs locally and generation runs in a contained tenancy. We have shipped Gulf-region government programmes where the residency requirement was explicit and unmovable. The architecture starts from that requirement, not from the model catalogue.
What does "audit by design" actually mean in your delivery?
Every state change in the platform produces an audit record that can be replayed; access decisions are logged with the policy that authorised them; document lifecycle is fully traceable; and the audit surface is designed for the regulator who would actually use it, not for an internal compliance tickbox. We have shipped regulatory platforms where the auditor's view was a first-class deliverable, not an afterthought.
Can you add governance to AI systems another team built?
Yes. We start with an inventory and risk review of what is running, then add evaluation, logging and ownership in order of risk. Most of this can be wrapped around an existing system without rebuilding it.
What would you hand to an agent first?
Bring one workflow. In 60 minutes an AI architect maps where an agent helps, what it needs to reach, and what a first working version would take.