An AI governance framework answers four questions: which systems need a gate, who signs, what stops a release, and when a decision gets revisited. Relevant Software settles all four with your leads and builds the answers into your pipeline, so each one can hold on every release.
Our consultants produce one agreed structure: the policy set, the risk tiers, the roles, and the review points, each clause traceable to the clause in the standard you certify to.
Placement on a tier decides which duties attach, so our consultants write criteria precise enough that two reviewers reach the same answer. The duties themselves come through our AI compliance services, mapped to the EU AI Act, ISO/IEC 42001, and the US rules reaching your users.
Certification asks for policies, roles, and review records that work as one system, and our consultants build that structure clause by clause, then run the internal audit round that closes findings before the certification body books its visit.
A policy line becomes a check your build server evaluates: impact assessment complete, evals above threshold, approval recorded against a model version. Our engineers write those checks and put them in the path a release already takes.
A licensed tool becomes useful once it reads from the systems that hold the records. Our engineers configure the workflows, wire the integrations, and load the data, so the dashboard your risk owner opens matches the estate.
A gate passes or fails on a number. Relevant produces those numbers as an AI security services engagement: eval suites, red-team regression cases, and permission checks, each returning a result the pipeline reads.
Between releases your policy set has to hold on every request. At the model boundary that enforcement ships as AI guardrails implementation, with input validation, output filtering, and thresholds tuned on your own traffic.
Agents decide and act between reviews, so the record of what they did has to reach the same people who approve releases. Where agents already run, AI agent monitoring supplies the trace and the approval record each gate reads.
Your AI policy points at datasets: which one trained a model, who owns it, how long it is kept. Those answers belong to data governance, which sets the catalogue, lineage, and retention your framework references.
Workshops with your risk, legal, and engineering leads settle the policy set, the tiers, and the roles. You approve one document, and the scope and price are fixed against it.
Your inventory goes through the criteria once, and the session ends with a tier per system.
Each policy line becomes a condition with a data source and a threshold, and the rest stays as written guidance.
The first implementation lands on a single team’s pipeline, with the checks running and the approval recorded.
Each remaining team gets the same checks with its own thresholds, and our consultants sit in on their first reviews. You finish with the runbooks in your team’s hands.
The consultants who write your policy sit with the engineers who implement it. A clause gets tested against the pipeline while it is still in draft, so the framework arrives already implementable.
Every policy line we implement resolves to a pass or a fail your build server reads. Rules that must hold become checks, and the rest ships as written guidance for reviewers.
Up to 50% faster delivery and up to 20% fewer bugs come from the AI-assisted development practice behind the gate implementation, and senior review clears each release.
The same team builds LLM, RAG, and agent systems in production. A gate designed by people who have shipped through one blocks what matters and lets the rest through.
92% senior talent means the people writing your gate checks are the ones your engineers will review pull requests with.
96% employee retention means the person who defined your review gate is available when the first contested release reaches it.
Framework work is close to fixed, and the implementation scales with your build estate. Four things move it:
Most engagements reach a signed framework draft and a priced rollout plan within 14 days of kickoff. When you want a figure before committing to the framework phase, contact us and we will walk the four variables against your estate.
Yes, we can build on an existing policy, and we keep its structure so your auditors see continuity rather than a fresh start.
The seam shows up clause by clause. Consultancy policies name controls without naming the system that would prove one ran, so the first pass walks each clause and asks what would answer it: a field in the model registry, a CI check, a signature in a ticket. Clauses that already point at a source survive untouched.
Clauses written as intentions get restated as something measurable. A few describe a control nobody built, and those become implementation work with a price against them. Where the policy was written against a framework you have since moved away from, rewriting the scope section costs less than reconciling the old one.
The override path is part of the design. A named approver can release past the gate, and the override records who approved it, the reason, and an expiry after which the original condition applies again.
The trade-off is worth stating plainly. An override that is easy to reach turns the gate into a formality. One that is hard to reach gets routed around, and teams start shipping through paths the gate never sees, which is worse than having no gate. We set that friction with your risk owner during the framework phase and then measure the override rate, because the rate tells you whether the criteria were set correctly. A gate firing on a quarter of releases usually has the wrong threshold rather than a reckless engineering team.
Several parts transfer directly. The ownership model, the decision rights, the cataloguing habit, and the stewardship roles carry straight across, and companies with a working data governance function reach a running gate faster.
What is new is that a model makes a decision. Data governance answers where a record came from and who may see it. AI governance answers what the system concluded, about whom, on what basis, and who accepted that conclusion on the company’s behalf.
The arrangement most of our clients land on is one council and two registers: the same people meet, and the AI register carries fields the data catalogue has no place for, such as intended purpose, tier, evaluation results, and the approver of the current version.
A rollout starts with the systems you can name today, and that list is usually shorter than the estate. Our consultants run the framework workshops against what you name, then widen the inventory during the tiering session, where business units surface the vendor features and the tools nobody logged.
Teams that want the ground mapped before committing to a framework book an AI maturity assessment first, which scores governance alongside data, infrastructure, and talent, and returns a sequenced roadmap. Teams with a regulatory date already in sight skip that and start with the framework, because the assessment answers questions a deadline has already answered for them.
The gate runs on your pipeline before the engagement closes.
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