Monitoring
We track how the solution runs in production: whether it answers correctly, takes the right steps, and behaves consistently as the data change.
Service
AI governance and maintenance after deployment
AI governance is the set of rules, roles and tools that lets a company know what its AI system does, who is accountable for it, and what to do when something goes wrong. CrAIT puts it in place together with maintaining the solution after the pilot.
Scope
What AI governance covers
A working pilot is not yet a predictable system. Governance turns it into a solution that can be controlled, explained and developed.
Logs and audit trail
Decisions that involve AI can be reconstructed: which data were used, what action the model took and who approved the result.
Documentation
The purpose and limits of the solution, data sources, integrations and the procedure for a wrong answer — written so the team can actually use them.
Decision roles
We agree what the model may do on its own, what needs human approval, and who owns the solution on the company side.
Maintenance
Maintenance and growth after the pilot
Delivery does not end on launch day. After the pilot, the solution moves to production and needs ongoing support. The scope of that support is agreed case by case — individual elements may enter the contract in full or in part.
Stable production
We make sure the solution launches cleanly and runs predictably in the production environment.
Team training
The people who work with the solution know how to use it and when to verify an answer.
Growth plan
After the pilot, the plan for the next use cases is based on what the data and users have shown.
Security policies
The data-access and integration rules set in the architecture stage keep applying after go-live.
How it works
How we put AI governance in place
Governance is not a document written at the end of a project. It is built together with the solution, stage by stage.
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01
Boundaries
We define what the model may do on its own and what needs a person.
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02
Roles
We name the people accountable for decisions and maintenance.
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03
Monitoring
We set up logs and a way to check output quality.
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04
Growth
We plan the next changes based on production data.
FAQ
Questions about this service
Why AI governance if the solution already works?
Because working in a pilot does not guarantee predictability in production. Governance brings monitoring, logs, documentation and decision roles, so the company knows what the AI system does, who is accountable for it, and how to respond to an error.
What should the documentation of an AI solution include?
The purpose and limits of the solution, data sources and integrations, the split between what the model does on its own and what needs human approval, the monitoring approach, and the procedure for a wrong answer. Documentation should serve the team, not only an audit.
Does maintenance include developing further use cases?
Yes. After the pilot we prepare a growth plan based on production data and user experience. Further use cases follow the same path: diagnosis, architecture, pilot, scale.
How does AI governance relate to the EU AI Act?
For high-risk systems, the EU AI Act phases in obligations that include technical documentation, event logging and human oversight. We design those same elements — documentation, logs and decision roles — as part of governance. Classifying the system and the legal assessment stay with lawyers; our work gives them the material for it.
Next step
Let's talk about who in your company should be accountable for decisions made with AI.
Write to CrAIT
We reply within 2 business days.