Shivi Sharma spent a decade working in credit risk at companies like American Express and Varo Bank. During her career, she observed a significant inefficiency. Teams were spending equal amounts of time analyzing all types of loans, regardless of whether a loan was for one hundred thousand dollars or five million dollars. This meant the process of assessing smaller loans was ultimately unprofitable and time-consuming for lenders.
She and her husband, Utsav Shah, identified a major opportunity in this problem. Shah explained that Shivi saw how the vast majority of small business owners could not access the capital they needed to grow. This was simply because the economics did not work for the banks. With their combined skills in building AI-powered decision-making systems and their deep expertise in credit and fraud risk assessments, they realized they could apply next-generation AI agent workflows to solve this long-standing issue.
The married couple decided to launch a company named Kaaj in 2024. Kaaj helps automate credit risk analysis, reducing underwriting from days to just minutes. The company has already processed more than five billion dollars worth of loan applications. Its clients include Amur Equipment Finance and Fundr. Kaaj recently announced a three point eight million dollar seed funding round from Kindred Ventures and Better Tomorrow Ventures.
The product functions in a specific way. When a small business applies for a loan, it submits all required documents such as financial statements, bank statements, and tax returns. Traditionally, underwriters then spend days manually verifying this information and logging it into their Loan Origination System. Kaaj uses AI to automatically identify, classify, verify, and organize this information directly into the system. It also runs assessments to check for document tampering for the fraud team. The product integrates with existing Customer Relationship Management systems like Salesforce, HubSpot, or Microsoft. It can even show a lender if a business is meeting the specific criteria of the lender’s policy.
This automation dramatically increases efficiency. The company’s CEO, Utsav Shah, stated that a team processing five hundred applications monthly could potentially handle twenty thousand applications with the same staff. This makes smaller loans economically viable for lenders. The hope is that as it becomes more cost-efficient for banks to process these applications, more small businesses will be able to access the loans they need.
Other companies in this market include Middesk, Ocrolus, and MoneyThumb. Shivi Sharma hopes Kaaj will stand out by automating the entire credit analysis process from end to end, rather than just parts of it. She explained that they deploy agentic AI workflows that mimic lender teams to analyze complete loan packages.
The new seed funding will be used to accelerate product development and expand their reach across independent and small business lenders. The company is focused on enhancing its AI agent capabilities, expanding its module offerings, and scaling its customer base of lenders and brokers.
Overall, Shah and Sharma hope Kaaj can revolutionize small business lending by bringing automation to what is still a very paper-heavy process. Shah concluded that by automating the science of credit analysis, they free up human underwriters to focus on the art of deal-making and subjective assessment, which is their true competitive advantage.