CollAI is building a runtime trust layer to help regulated organizations use AI in production without losing control of sensitive data, decisions, or evidence. Its approach governs the point between a model’s output and an operational action: sensitive context is controlled before entering automated workflows, generated responses are inspected, and high-impact actions require accountable human approval. CollAI also describes an audit process designed to create reviewable, hash-chained records for incident investigation.

The company’s stated controls include VPC scrubbing, StegoGuard output inspection, named-human approval for destructive actions, and audit-ready incident records. These are intended to address risks such as data leakage, unsafe agent behavior, and outputs that evade basic scanning. CollAI positions its platform for regulated teams that need machine-speed assistance while maintaining clear boundaries and accountability. Its current offering includes a self-serve live demo and a design-partner program focused on regulated financial services teams. The company also provides private architecture briefings for qualified partners.