A governed operating layer for AI at a fintech

A fintech company in the United States was adopting AI across engineering, cloud infrastructure, internal tooling and customer-facing systems, faster than any one team could keep track of.

AI activity sat in repositories, cloud services, identity platforms, model providers and internal applications at once. Without one operating layer, nobody could say reliably which systems were active, which had been reviewed, or which issues were still open.

An AI security and posture system that connects discovery, governance, security testing and runtime control in one workflow, built around the company's own stack rather than configured from a template.

We built it for the company's internal use.

• Discovers AI assets across repositories, cloud services and internal applications
• Registers each system with an owner
• Assesses posture and risk, and records what it finds
• Tests AI systems before they reach production
• Tracks issues through to remediation

The company started testing AI systems before they reached production, rather than after something went wrong.

Before that, AI shipped from several teams at once with no consistent point at which anyone checked it, and problems surfaced in production or not at all.

The first release prioritized discovery, testing and runtime control. Complete governance automation, formal registration workflows and several platform integrations were deferred rather than shipped half-built.