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We graded our Agent product with the Bretton Evaluator across two generations. V2 improved on every measure we track against V1. On enhanced due diligence for individuals, errors fell 37%, and the share of reports with a perfect score rose from 42% to 59%. On company due diligence, errors fell 23% and that share rose from 26% to 38%. Across the two, V2 cut the errors V1 carried on the same subjects by 30%, and neither gain cost any run time. In this post, you'll learn how we established that, and what the new architecture specifically fixed.

Quality in production cannot be validated once and assumed to hold. It has to be measured continuously, on real runs, in real-time. The first piece in this series made the general case that production requires a second system around the agent. The Bretton Evaluator is that system. It is the evaluation engine inside the Bretton AI Platform's Trust Infrastructure, and its job is to answer, run after run, whether the agent is still meeting the standard it was given.

The integration challenge is all about identifying the right path quickly, getting the right access, and agreeing on exactly what data needs to move. Those are organizational problems as often as technical ones, and they are where timelines actually go. So we built our process around two things that remove most of that uncertainty before implementation starts: a small number of repeatable integration patterns, and a clear checklist of what we need from the customer up front. In recent deployments, that combination has taken integrations from first access to production in about a week.

The BPO model has reached a breaking point. The wage gaps between markets narrowed. The work itself became much harder, because regulated operations now require stringent documentation, quality, and data-residency expectations that will most definitely punish a revolving bench of undertrained staff. And the hidden costs of distance, such as the time-zone lag, the language friction, the staff turnover, the slower feedback loop where a client flags a problem and nothing changes, became insurmountable. 

Our challenge comes from not just ensuring that our agents are reasoning well. It comes from making the whole investigation complete, grounded, consistent, and auditable, from intake to disposition. That is what our agent harness is built to do. It is the execution layer beneath every agent on the Bretton AI Platform, and its job is to hold a long investigation to a standard the model alone cannot guarantee.

We’ve always been confident in our ability to build a strong agent for any one of those workflows. We do have a cracked engineering team, after all. The harder question was how to ensure our platform is truly functional at scale: we need to build hundreds of them, across different use cases and customers, without initiating a new software project. What we needed was a repeatable way to transform our customers’ workflow into an agent that meets their standard. That is what Builder is.

We completely rebuilt our architecture three times since starting Bretton due to the rapid advancement of AI technology. Every generation described here ran in production inside financial institutions, carrying real cases under real controls. Each move took a different kind of complexity out of code and put it in the right layer of the system. Learn how we did it and what changed.

This post showcases how Bretton AI validates AI agents before and after they go into production with an anonymized customer example. It covers our model risk framework, the scenario testing protocol we ran against a synthetic dataset, the fairness analysis we conducted across ~130 countries and multiple identity field types, and, most importantly, the gaps we found and what we are doing about them.

The foundation models work great and will keep getting better. So why do most agent projects in financial services still fail? And fail in the same, predictable way? For those who have tried, it becomes quickly apparent that there’s a vast distance between a demo that impresses a room and a system an institution can rely on, at production volume, inside its own controls. That distance is where the build-versus-buy conundrum is almost always underestimated.

MVB, a subsidiary of MVB Financial Corp. (Nasdaq: MVBF), has selected Bretton AI to deliver AI-native Managed Services for its back-office operations. A dedicated US-based Bretton AI team will run Anti-Money Laundering transaction monitoring and Know Your Customer work, delivered as completed alerts and cases, in line with MVB's policies and risk framework.

Risk Management Solutions Group (RMSG) and Bretton AI have formed a strategic partnership to help financial institutions modernize compliance and risk operations through a combination of deep regulatory expertise and AI-native execution.

Hand us your managed service contract, and we transform it into an AI-native program. Priced on outcomes rather than headcount, get a true win-win-win: advance your AI agenda, comply with new regulatory expectations, all while your operations costs come down.

The more you grow, the more it costs to run. Every new market, every new product, every new customer adds compliance, risk, and operational load. For a hundred years, growing the front office has meant growing the back office right alongside it.

Compliance teams can now bring trusted Equifax data directly into Bretton AI's investigation workflows, reducing manual effort, improving consistency, and producing audit-ready outputs across their day-to-day operations.

Bretton AI has allied with LexisNexis® Risk Solutions to help compliance teams apply this investigative data more consistently and efficiently within their day-to-day workflows.

Kaufman Rossin's financial crime and risk advisory practice is now delivering Bretton AI to its clients, combining decades of regulatory expertise with AI-native operations to change how compliance work gets done.

Learn the difference between bank-grade and general-purpose agentic AI for compliance and why it matters for financial institutions.

Horizon handles the laborious data gathering and documentation, freeing up your experts to focus on the critical thinking and nuanced analysis that only a human can provide.

Today, I’m thrilled to announce that we have raised a $75M Series B funding round and are updating our name from Greenlite AI to Bretton AI.

Learn about the ROI-boosting features that matter most for financial institutions when evaluating agentic AI solutions for compliance.

Bretton AI's Business Due Diligence is now integrated with Alloy, automating KYB workflows with intelligent agents. Reduce manual review time by 70% through AI-powered document analysis, risk assessment, and plain-language explanations. Alloy customers can enable this feature today to transform business onboarding while maintaining transparency and control.

Bretton AI, the leading AI agent platform for financial services, announces partnership with Middesk, comprehensive business verification platform for KYB and enhanced due diligence.

Greenlite AI, the leading AI agent platform for financial services, today announced a $15 million Series A funding round, bringing its total capital raised to $20 million.

How to build successful BaaS partnerships

Bretton AI, leading generative AI platform for banks and fintechs, announces partnership with Sandbar, comprehensive activity monitoring platform for AML.

Announcing our $4.8M seed round, led by Greylock

Compliance officers across the country are dealing with high stress, long hours, mental health issues, and broken operations. How can the system improve the workload for AML employees?

We've achieved the industry-leading certification for information security, data privacy and processing integrity

Bretton AI agents work 24/7 to investigate high-risk customers flagged by AML systems, helping your team support more customers, volume and transactions.