Middlesex Federal Savings, a community bank with multiple fintech partnerships, has rapidly embraced AI across its operations while keeping human expertise at the center of its adoption strategy. What began as a three-month pilot with the Bank’s IT team and senior leadership has expanded into a multi-department rollout, with AI agents helping streamline work ranging from SOC 2 report reviews to financial reconciliation.

With support from Middlesex Federal’s Bank President, Vice President of Information Technology Kosta Georgopoulos, and StackAI’s implementation team, the Bank has successfully built a governance framework, an AI policy, and a culture of validation-first AI use, with promising operational results in a short period.

The Challenge

Like many financial institutions, Middlesex Federal relied on manual, time-intensive processes across several departments. Reports required pulling data from multiple sources, extracting figures from PDFs and HTML pages, and combining them into final documents. SOC 2 reviews were hours of tedious reading. Even locating specific policy guidance could require searching through hundreds of pages. The manual nature of these tasks consumed time that could be spent advancing the business.

At the same time, the Bank needed to move carefully. As a regulated institution, anything touching client data, compliance reporting, or internal policy had to meet a high bar for accuracy and security. Any AI solution would be expected to fully satisfy auditors, examiners, and a detail-oriented Board.

Finding StackAI

Kosta first encountered StackAI at MIT’s AI Executive Academy, where the platform was featured as a leading example of a no-code enterprise AI platform. After evaluating several alternatives, including enterprise ChatGPT, UiPath, and other orchestrators, he chose to move forward with StackAI.

The Bank started with a three-month pilot in July 2025. When results looked promising, the team extended it to six months to bring in more users. By January, Middlesex Federal had signed on as a full customer.

Rolling Out Across the Organization

The rollout began with Kosta’s IT department and quickly expanded. Today, several teams are actively building agents, including accounting, loan operations, platform innovation, and information security. These groups meet weekly with StackAI’s implementation team to identify use cases, refine workflows, and support adoption.

Kosta noted, “StackAI’s implementation team has been exceptional. They worked closely with our teams and helped accelerate adoption across departments in a practical, structured way. They solved problems even outside of our scheduled touchpoints.”

Interest has spread organically throughout the organization. Teams that have not yet started using StackAI are already asking to join after hearing about the results from their peers.

“Once teams started seeing the time savings and results other departments were getting, interest spread quickly across the Bank,” recalled Kosta.

Key Use Cases

The Bank’s use cases span document analysis, report automation, and internal knowledge retrieval.

One early proof of concept involved a cumbersome report that required visiting several websites, drilling into PDFs and HTML pages, extracting the right data points, and assembling them into a single document. Previously, this was a multi-hour manual process; the agent now completes it in a few minutes.

“Once we saw it handle that kind of complex workflow,” Kosta recalled, “we realized there were a lot more areas where it could help.”

The Bank also built an internal chatbot that houses company policies, procedures, and Bank intelligence. Unlike a generic LLM, the chatbot returns Bank-specific answers grounded in the Bank’s documentation.

“One of the most useful things for us has been being able to quickly find the right policy or guidance when we need it. Instead of searching through documentation, we can easily identify the relevant information and then cite the source directly.”