Introducing Gretta. The AI agent for Compliance testing and remediation.

Why now

Over the last year working with fast-growing fintechs and public financial services companies, a recurring theme began to appear. Teams were hacking our QA system to run compliance testing, pulling interactions manually, scoring them against spreadsheet templates, logging findings one row at a time. They were making it work. But the tool was never built for this.

That told us something important.

The problem they were solving for is one of the most under-resourced in financial services. Compliance testing is not optional. It is a regulatory requirement. Examiners from the CFPB, OCC, and FDIC expect evidence that your institution is regularly testing the controls you said you would maintain across every product line, every channel, and every customer-facing workflow. If you cannot produce that evidence, you are exposed. And for most teams, producing that evidence means doing it by hand.

The process today

Here is what that looks like in practice.

Once a year, key controls are established around the risks the compliance team cares about. These controls map to specific regulations. BSA/AML flags around transaction monitoring and SAR filing obligations. UDAAP controls focused on Sales and Customer service. Reg E requirements around error resolution and dispute handling timelines. Reg Z disclosures in lending and credit workflows. Each control gets a template. Each template lives in a sheet. And then the testing begins.

Throughout the year, compliance teams run tests monthly, quarterly, or annually depending on the control. For each test, analysts pull a predefined template and evaluate it against live interactions: disputes, sales calls, customer service conversations, lending decisions, marketing materials, and fraud cases.

Each control typically requires 50 samples. Higher-risk areas require 100+ samples. For every sample, a reviewer pulls the interaction, reads or listens through it, compares it against the requirement, records a pass or fail, documents the finding, and moves to the next case with each review taking between 30 minutes and 1 hour. Most compliance teams we spoke with spend 30 to 50 hours per analyst each month just on testing. Some spend more.

And that is just the scoring.

Before a single finding gets logged, a reviewer has to locate the interaction in one system, pull the customer record from another, cross-reference the product terms from a third, check whether a similar issue was flagged in a prior testing cycle, and then manually calculate whether the defect rate crosses a threshold that requires escalation. Every step is a context switch. Every context switch is time. And at the end of it all, everything gets typed into a spreadsheet that will sit in a shared drive until the next examiner asks for it.

That is not a workflow problem. That is a structural problem. And it gets worse as your product surface area grows, as you add new customer segments, as you move into new states or new regulatory jurisdictions, as you hire AI agents to handle onboarding or servicing and suddenly have to monitor those too.

Nobody has built the right tool for this. Until now.