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Founded in 2024 by Dan Bolger and Atul Davda, Flatirons AI is building industry-specific AI agents that help banks handle complex, highly regulated workflows with speed and precision.
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Ask any bank what slows them down, and “compliance” will be near the top of the list. Internal audits, regulatory policy lookups and documentation workflows eat up time, create risk, and rely heavily on institutional knowledge that’s rarely easy to access.
Flatirons AI is changing that—agent by agent.
Founded in 2024 by Dan Bolger and Atul Davda, Flatirons AI is building industry-specific AI agents that help banks handle complex, highly regulated workflows with speed and precision. They started with compliance, but that’s just the beginning.
This isn’t another chatbot startup. Flatirons is building deep, domain-aware systems that understand context, nuance, and regulation designed to operate within the real-world boundaries banks care about: policy, privacy, and trust.
While much of the AI market rushed to build copilots and document summarizers, Flatirons took a different approach. They saw an opportunity to not just speed up but eliminate the slowest, most manual processes inside banks.
“We launched the company based around a good compliance use case with the goal of bringing very, very specific, industry-specific applications to financial service and particularly the banking sector.” — Dan Bolger, CEO
Compliance was the perfect entry point: high-value, process-heavy, and full of interpretive complexity. Flatirons’ system doesn’t just return answers, it delivers responses tailored to which regulator is asking and what rulebook they’re applying.
From day one, Flatirons was designed with real-world enterprise constraints in mind.
Authentication works seamlessly with bank infrastructure. Data retrieval is permission aware. And when IT teams do their due diligence, the result is often a fast greenlight.
“Our meetings with IT groups usually last about 45 minutes before they say, ‘These guys are good, let them in.’” — Dan Bolger
That’s because Flatirons doesn’t just “run on Azure”, it’s built around enterprise-readiness, from technical architecture to user workflows.
Now in year two, Flatirons is onboarding customers, expanding their AI agent capabilities, and unlocking new use cases.
They're moving into retail operations, back-office workflows, and commercial lending. They're even building strategic tools that help bank executives model acquisition scenarios using AI-informed logic and real data.
“We’re starting to break out into other areas... retail operations, commercial lending, back office, and even the strategic side.”
— Dan Bolger
At the core is a simple idea: most of the answers banks need already exist. They’re just buried in documents, spreadsheets, or SharePoint and Flatirons is bringing that data back to life.
To deliver a solution that meets the demands of large financial institutions, Flatirons built its stack on Microsoft Azure, leveraging Azure Open AI and Azure AI Search to power agents that are both intelligent and compliant. This foundation wasn’t just technical, it was strategic. Most of Flatirons’ target customers already trust Microsoft, and aligning with that ecosystem eased adoption from day one.
When it came time to scale, the team turned to WeTransact to simplify what’s often a complicated path: publishing on the Azure Marketplace. That listing didn’t just unlock co-sell support. It gave Flatirons a seat at the table with enterprise buyers who increasingly prefer and sometimes require procurement through the marketplace.
“WeTransact has been really helpful in getting us from ‘we want to do this’ to ‘live’ on the marketplace.” — Dan Bolger
Marketplace presence also opened doors to joint opportunities with Microsoft’s field teams, giving Flatirons reach and visibility that’s hard to achieve alone.
Flatirons isn’t just another AI vendor trying to wedge into financial services. They’re building products that reflect a deep understanding of how banks actually work and what slows them down.
Their mission isn’t to replace humans. It’s to give smart people better tools to do complex work—faster, more accurately, and with far less friction.
They started incompliance. They’re now building agents for the rest of the enterprise.
And they’re just getting started.
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