Bretton AI Raises $75M to Deploy Agent Workforce Across Financial Crime Operations
Financial institutions spend over $60 billion annually on compliance operations in North America alone, yet 95% of alerts generated by legacy anti-money laundering systems are false positives. Bretton AI’s $75 million Series B, led by Sapphire Ventures, represents a decisive shift toward autonomous agent infrastructure for the financial crime operations bottleneck.
The company, formerly known as Greenlite AI, rebranded to Bretton AI alongside the funding announcement—an homage to the Bretton Woods agreement that established the modern financial system. The parallel is deliberate: CEO Will Lawrence positions AI agents as a similar inflection point for how regulated institutions operate at scale.
The Compliance Workforce Crisis
Financial crime operations have become the breaking point for institutional growth. Manual investigations span dozens of disconnected tools and data sources, requiring human analysts to manually stitch together evidence under evolving regulatory expectations. Legacy detection systems flood compliance teams with alerts, but 95% lead nowhere—creating what Sapphire Ventures calls “the false positive tax.”
The operational burden scales linearly with headcount, making compliance a brake on innovation rather than an enabler of growth. Banks face mounting regulatory scrutiny while trying to expand customer bases and launch new products. The result is an industry-wide capacity crisis where compliance work bottlenecks business objectives.
Bretton AI addresses the core problem: not detection, but what happens after alerts are generated. While competitors focus on improving risk flagging through better rules and models, Bretton AI deploys agents to execute the investigative work traditionally performed by first-line analysts.
Agent-Native Compliance Infrastructure
Rather than replacing existing systems, Bretton AI’s platform integrates directly into current compliance stacks. Agents ingest alerts, institutional SOPs, and risk policies, then execute end-to-end investigations across multiple systems. They consolidate evidence, test findings against policy, auto-resolve low-risk cases, and escalate only what requires human judgment—complete with audit-ready narratives and citations.
The core differentiator is Bretton AI’s proprietary Trust Infrastructure, a governance system that embeds regulatory guidance, model risk management, continuous evaluation, and quality assurance into every agent. This allows financial institutions to deploy AI that meets regulatory standards from day one.
Investigations that previously took days now complete in minutes. Agents operate across systems, reason over incomplete data, and maintain the consistency and defensibility required for regulatory scrutiny. The platform handles KYC and KYB reviews, AML and sanctions investigations, and ongoing monitoring workflows.
Enterprise Adoption Evidence
Bretton AI is trusted by OCC-, FDIC-, and Federal Reserve-regulated banks and global financial platforms including Robinhood, Mercury, Gusto, Lead Bank, and Coastal Community Bank. The total market capitalization of companies using the platform has grown from $150 billion to over $1 trillion in the past year.
Since the Series A in May 2025, average customer contract value has increased from $85,000 to $201,000, reflecting deeper production-scale deployments. Agents have completed more than 1.2 million financial crime investigations, eliminating over 195,000 hours of manual compliance work and delivering more than $10 million in customer savings.
Measurable results include a $15+ billion financial institution reducing BPO spend by $5.35 million in its first year, a Fortune 500 company cutting client onboarding time by 50%, and an FDIC-regulated bank reducing loan origination timelines by up to 90%. Brian Hamilton, President at Coastal Community Bank, reports “meaningfully reduced L1 investigation times, improved consistency across decisions and strengthened our overall risk posture.”
Regulatory Trust Infrastructure Emergence
The rebrand from Greenlite to Bretton AI reflects evolution from AI tooling provider to category-defining platform for financial crime operations. Lawrence frames the shift: “Greenlite AI was about proving that trusted AI agents could work in compliance. Bretton AI is about defining the standard for how AI operates inside regulated financial institutions.”
The Trust Infrastructure approach addresses the core challenge of deploying AI in heavily regulated environments. Banks can’t use generic AI—they need systems built from the ground up for regulatory accountability. Bretton AI’s agents are audit-ready, explainable, and regulator-aligned by design.
This infrastructure-first approach differentiates Bretton AI from broader financial services AI vendors retrofitting existing tools for compliance use cases. The platform was purpose-built for the unique requirements of regulated financial institutions, where explainability and governance are not optional features but foundational requirements.
Looking Forward: Agent Workforce at Scale
The Series B funding will expand Bretton AI’s platform across additional financial crime domains, deepen regulatory engagement, and accelerate adoption among larger institutions. The company will continue investing in product development and scaling engineering and go-to-market teams.
Financial crime compliance represents one of the clearest use cases for autonomous agents in regulated industries. The work is complex, unstructured, and deeply scrutinized—exactly the conditions where AI agents can deliver measurable impact while meeting the highest standards for accuracy and explainability.
As Lawrence notes: “Financial crime is the breakout use case for AI in financial services. We’ve proven that AI agents can operate in production inside the world’s most regulated institutions when built with the right trust and governance foundations.”
The autonomous agent infrastructure category continues expanding beyond early adopters into mission-critical enterprise operations. Bretton AI’s approach—building trust infrastructure from the ground up rather than retrofitting existing tools—may define how regulated industries deploy AI agents at scale. For organizations building complex workflows that require both automation and regulatory compliance, platforms like Overclock provide complementary orchestration capabilities for multi-step autonomous processes.