AI governance
Say yes to AI, with the guardrails to prove it.
Register every AI model and use case, sort each one into a risk tier, approve it through the right workflow, and keep watching it after it goes live, with conditions, monitoring and re-reviews on schedule.
Generative AI assistants
Ambient documentation
Clinical decision support
Revenue cycle automation
Chatbots
AI in vendor products
Models you build
AI in every step
Governance that keeps pace with AI.
Automatic risk tiering
Rules read the facts of each use case (the data it touches, who it affects, the decisions it makes) and set its risk tier, which picks the review it needs.
Conditions that fit the use case
A library of obligations marks the conditions of approval that apply, from human review of outputs to disclosure and bias testing.
Monitoring after go-live
Track accuracy, drift, override rates, hallucination findings and subgroup disparity against limits. A breach flags the use case for review.
AI safety events as cases
When an AI system misbehaves, staff report it like any other incident, and it is investigated and linked back to the use case.
Register
One register for every model and use case.
See what AI is in use, where, on what data, and who is accountable for it.
Use cases numbered and linked to the models, applications and data they use
Models with the scope they are approved for
An AI profile on each application record
A request form anyone in the organization can use
Risk tiers
Risk tiers that set the level of review.
Not every use case needs a committee. Tiers send each one to the review it deserves.
Tiers and rules your team can edit, with sensible defaults
Rules based on data, population, decision role and more
The tier picks the approval workflow
The tier sets how often the use case is reviewed again
Approval
Approval workflows with conditions attached.
Reviewers, checklists and approvers in stages, with the conditions of approval tracked afterward.
Starter workflows that install in a click
Reviewers, requirements and approvers at each stage
One-off and recurring conditions of approval
A material change to an approved use case triggers a change review
Monitoring
Keep watching after go-live.
Approval is the start, not the end. Re-reviews and monitoring run on their own.
Metrics on each use case or model, with limits and a baseline
Readings outside the limits flag the use case for review
Scheduled re-reviews start automatically
Overdue reviews and breaches on the dashboard
Frameworks
Aligned with the frameworks regulators cite.
Show how your AI program maps to the standards your board and regulators ask about.
NIST AI RMF as a compliance program on the shared control library
AI use cases and models linked to risks in the register
Third-party AI tracked with the vendor that provides it
Evidence ready for auditors, regulators and the board
See how Simpliance governs AI.
We’ll register one of your AI use cases, tier it, route it for approval and set up monitoring, live, in 30 minutes.