A PE-backed banking-software platform had been rolled up from seven companies into one business serving thousands of US financial institutions.
Leadership had done the visible integration: one set of policies, one security stack. AI was running across three clouds, several engineering teams, and contractors who each held their own access, and no one could actually say what was live, who was using what, or where customer data was going. On the engineering side, developers were shipping AI-generated code (Claude Code, Cursor) with no consistent review or security path. On top of that, non-engineering teams were using unauthorized MCPs and a sprawl of vendors that each solved only part of a problem, on plans that weren't aligned with actual usage, adding significant cost overhead. From the customer's side, the AI capabilities already shipping inside the platform were weakly governed and had limited eval harnesses.
An AI audit and governance program for a seven-company banking roll-up, run on the Tensile platform (Python SDK, ingest gateway, eval engine, dashboard) deployed in the client's AWS, spanning shadow-AI discovery, engineering AI security, product-AI evals, and an ISO 42001-aligned governance framework.
First unified count that the company had as audit proof
Across multiple entities, with GTM and Sales contribution 30% to this.
Per the updated AI policy
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