We help you set an AI strategy and turn your policies into rules teams follow every day: who can use which tools, who approves what and what gets recorded. Teams keep shipping, and you can show auditors how each decision was made.
What's included
6 capabilities, delivered by one squad. Use what you need now and add more as you grow.
Decide who owns AI, who approves spending and which use cases come first, tied to the business results you want.
Your AI principles become specific controls for data, models and generative AI tools, each with an owner.
Sort AI systems by risk, then set the testing, review and approval each tier needs before launch.
Role-based access, change control and review steps added to the tools your teams already use to ship.
Decision logs, data lineage and approvals kept in one place and exportable when an auditor asks.
Alerts when a live model drifts or its output looks unsafe, plus a written plan for what happens next.
Our approach
The usual way
AI policies sit in a document that nobody checks against day-to-day work.
How we do it
Each policy becomes a check or approval inside your delivery pipeline, so the rule is applied every time.
The usual way
Audits turn into a scramble because logs and approvals are missing.
How we do it
Decisions, data sources and approvals are recorded as the work happens, and can be searched at any time.
The usual way
Risk is shared by everyone and owned by no one.
How we do it
Each AI use case gets a named owner, a review board and an escalation path, written into a RACI chart.
Architecture
Each layer has a clear job, so the system is easier to secure, test and extend.
List every AI use case, set its risk tier, name owners and agree who decides what.
Each principle becomes a control with an owner, a version and a review step.
Decision logs, data lineage and sign-offs are kept in a format auditors can read and export.
Live models are watched for drift and unsafe output, with a set process for exceptions and incidents.
How we deliver
4 phases, each ending with an output you can review.
We list your AI use cases and agree risk tiers, owners and who signs off on what.
Output: Governance baseline
We write the controls library, define the approval gates and set up review boards and sign-off steps.
Output: Working controls framework
We set up decision logs, data lineage and sign-off records that match what your auditors ask for.
Output: Audit-ready evidence store
We add monitoring, incident playbooks and exception handling, then adjust the rules as you add new AI use cases.
Output: Governance your teams can run
Your team
Specialists join your squad for this work, alongside a delivery lead who keeps you updated.
Shapes the AI programme, maps each policy to a control and agrees which use cases need a full review and which can move fast.
Builds approvals, role-based access and change control into the pipeline your teams use to ship.
Sets up the decision logs, data lineage and sign-off records your auditors will ask for.
Watches live AI systems, handles incidents and control exceptions, and decides when a model needs retraining.
Trust and control
Every policy maps to a check or approval that runs before an AI change goes live.
Role-based access, separation of duties and change approvals, so no one person can push a risky change alone.
Decision logs, data lineage and sign-offs are ready to export when auditors ask.
Drift and incident alerts reach the right people, with a clear plan for what to do next.
You keep full ownership of the code, configuration and documentation we create, with no vendor lock-in.
We pick what fits your product and team, not the other way round.
Results
We helped a company with many departments set up practical AI controls: approved tools, data rules, risk levels, human review, ownership and ongoing monitoring. Teams kept working at the same pace.
We helped a growing services company give employees a safer way to use AI with internal documents and customer information, by adding access controls, data protection, approved workflows and activity tracking.
We built an AI monitoring layer for a growing SaaS product. The team can now see how the AI is really performing, where users struggle and which responses or workflows need work.
For a large health system, we built a claims platform that matches EHR evidence to payer rules, automates routine authorisations and logs every decision. It is built to HIPAA requirements. Claims recovery rose 42%, and staff saved 15k hours.
An AI governance framework covers four things: a list of every AI use case with its risk tier and owner, controls that turn policy into checks and approvals, records that show auditors what was decided, and monitoring for live systems. We build each part into the tools your teams already use, not a separate document.
We take each line of a policy and decide where it should be checked: an access rule, an approval step or an automated gate in your delivery pipeline. Each control gets an owner and a version. Teams then follow the policy as part of their normal work, and every check leaves a record.
We list every AI use case, then rate each one on the data it touches, the people it affects and the harm a wrong answer could cause. Higher tiers need more testing, a review board sign-off and closer monitoring. Low-risk tools get a lighter process, so teams aren't slowed down for no reason.
Our governance work covers generative AI and AI agents as well as traditional models. We set rules for prompts and agent workflows, decide which data each tool may see, and add human review where a wrong output could cause harm. Each tool then sits in the same inventory, risk tiers and audit records as the rest.
An alert goes straight to the named owner, and a written incident plan sets out the next steps. We set up drift and safety alerts, a route for handling exceptions and triggers for retraining a model. Every incident is logged with the decision taken, so your audit record stays complete.
A governance review's length depends on how many AI use cases you run and how many teams own them. We begin with a baseline review that lists each use case, sets its risk tier and shows your most urgent control gaps, then agree the next phase. A scoped project can kick off in 1–2 weeks.
AI governance work is priced in one of two ways: a fixed-scope quote for a defined piece of work, such as a baseline review, or a flat monthly fee for a managed squad that builds and runs the controls with you. After a free first call we suggest the right team and give you a rough quote range.
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