Know how your AI is behaving
As AI moves into business processes, oversight needs to move with it. Ushyaku helps organizations establish the ownership, evaluation and operational controls needed to deploy and manage AI with clearer accountability.
Turn policies into decisions your teams can apply—and evidence they can review.
Keep responsibility visible as AI expands
An inventory of models is useful, but it does not explain who approves a use case, what an acceptable failure rate looks like or when a release should be stopped. Those decisions need to be made before issues become incidents.
We help translate organizational expectations into practical controls across AI design, release and operation. The work connects product owners, engineers and risk stakeholders around documented responsibilities, meaningful evaluations and an agreed response when performance changes.
From policy to production controls
Use-case and risk assessment
Document intended use, affected users, data dependencies and potential failure consequences to inform proportionate oversight.
Evaluation and release gates
Define task-specific tests and acceptance criteria, including quality, security and fairness considerations where they apply.
Access and safeguards
Implement appropriate tool permissions, data controls, review checkpoints and defenses against identified misuse patterns.
LLMOps and monitoring
Track model, prompt and configuration changes, monitor production behavior and support rollback, investigation and improvement.
Governance your delivery teams can use
Clear ownership
Responsibilities for approval, monitoring and incident response are assigned rather than left between teams.
Controls that match the use
Oversight reflects the application's role and consequences instead of applying the same process everywhere.
Evidence at decision points
Evaluation results and change records support practical release and operational decisions.
Ongoing attention
The approach accounts for new data, changed models and shifting usage after the first deployment.
Practical use cases for AI Governance
AI portfolio oversight
Document use cases, owners and assessment requirements across departments.
Release assurance
Assess a proposed model or prompt change against agreed acceptance criteria.
Operational review
Investigate a decline in quality and identify corrective action or rollback.
Frequently Asked Questions
Make your next AI release easier to account for
Review a planned or existing AI application and identify the controls, evidence and ownership needed to operate it responsibly.
Explore related expertise
Managed Support
Connect AI monitoring with application operations and incident response.
Explore Managed Support