Apply organizational policies to AI behavior with interpretable controls
CTGT develops technology for interpreting and controlling AI model behavior. Its current public offering includes a Policy Engine intended to apply organizational requirements to model outputs. The company positions this work around enterprise AI governance, particularly where a convincing response is not enough and the organization also needs to understand whether an output follows its rules.
The official material describes deterministic policy graphs and auditable records, including cryptographically attestable trails. These are advertised technical approaches, not a blanket guarantee that a deployment complies with every regulation. A business must first define the relevant policy clearly and determine how it should apply to different requests, exceptions and downstream actions.
CTGT's research focus includes analyzing, monitoring and standardizing AI behavior. Its model-agnostic positioning suggests use across different model workflows, but specific compatibility should be confirmed rather than assuming every provider or deployment is supported. The public pages reviewed did not establish a complete self-service API, connector catalog or fixed model list. A technical evaluation should therefore clarify how the controls are inserted into the actual application.
Pricing is not published as a standard subscription table. A useful discussion starts with a concrete policy problem and a set of representative examples, including ambiguous cases and attempts to produce prohibited outputs. Compare policy decisions with expert review and test how the system behaves when information is missing or the surrounding model changes. Performance claims from research demonstrations should not be generalized to every enterprise workload. CTGT can provide a layer of AI control and evidence, while policy ownership, application design and oversight remain responsibilities of the organization deploying the system.
