# Know what your AI can do, and who is accountable. Turn your AI policies into controls people can use. We connect ownership, permissions, evaluation and reporting to the systems your teams run every day. [Discuss your project](https://www.aigentcy.com/contact/) 1. AI activity 2. Permissions and approval 3. Evidence for review ## Make the policy part of the process. Your teams need to know which AI systems are in use, what they can access and who answers for their decisions. We help put that responsibility into the workflow and make the evidence available for review. ### Map the systems, owners and risks Start with an AI inventory, data flows and risk assessment. Vendor due diligence and regulatory mapping, including the EU AI Act and ISO 42001 where applicable, help your team identify the work to address with its qualified advisers. ### Put responsibilities into the workflow Responsible AI policies need named owners and clear limits. We implement permissions, evaluation and human approval around the actual use case, with audit trails and the context people need to examine an output or decision. ### Give oversight something to work with Board and operational reporting should draw from recorded usage, costs, changes and incidents. We build the reporting and review paths so your team can investigate what happened and decide what needs to change. ### What you have at handover Controls and evidence your technology, operations and risk teams can use together. Legal conclusions and certification stay with qualified advisers. ## Where this shows up in the work - Map AI systems, owners and data flows - Define permissions and approval boundaries - Evaluate outputs against agreed examples - Track usage, incidents and changes ### Give engineers context before they change a system A queryable map of services and dependencies gives engineers and their assistants scoped context for planning and reviewing changes. The case study follows the code-context layer: what engineers can retrieve, how they inspect dependencies and where review stays with a person. [Read the case study : Shared code knowledge for an engineering team](https://www.aigentcy.com/case-studies/agentic-second-brain/) ## When this is a good fit AI is already being used across your team, but ownership, access or oversight is inconsistent. ## Before you build A document-only exercise will not tell you who accessed a model or approved its output. Implementation work is most useful when you need those controls to operate in the actual workflow. ## Which AI system or workflow needs attention? Tell us what it needs to do, which systems it touches and what is holding it back. We'll work through the implementation with you. [Discuss your AI project](https://www.aigentcy.com/contact/)