Credit risk automation platform Kaaj raises $3.8M seed from Kindred Ventures

Shivi Sharma spent nearly ten years working in credit risk roles at companies such as American Express and Varo Bank. Over time, she noticed a significant inefficiency: credit teams were dedicating the same amount of effort to evaluating every loan application — whether the request was for $100,000 or $5 million. That imbalance made smaller loans costly and time-intensive for lenders, ultimately pushing many small business owners out of the financing pipeline.

Seeing this gap, Sharma and her husband, Utsav Shah, recognized a clear opportunity.
“She saw how countless small businesses failed to secure the capital they needed simply because the economics didn’t work for lenders,” Shah told TechCrunch.
“With our background in scaling AI-driven decision systems and our expertise in credit and fraud risk, we knew next-generation agentic AI could finally solve this longstanding issue,” he added.

In 2024, the couple founded Kaaj, a platform designed to automate credit risk evaluation and reduce underwriting timelines from days to minutes. According to the company, Kaaj has already processed more than $5 billion in loan applications and works with lenders such as Amur Equipment Finance and Fundr. The startup has now secured a $3.8 million seed round, led by Kindred Ventures with participation from Better Tomorrow Ventures.

Here’s how the product works: when a small business applies for financing and submits documents like financial statements, bank records, or tax returns, underwriters typically spend significant time verifying and entering this information into the Loan Origination System (LOS).

Kaaj automates this entire workflow. Its AI ingests, identifies, validates, and organises all the submitted materials directly into a lender’s LOS. It also conducts tampering and fraud checks and integrates with widely used CRM platforms, including Salesforce, HubSpot, and Microsoft systems. The tool can even flag whether an applicant meets the lender’s internal policy requirements.

“This means a team that previously handled around 500 applications a month can now manage 20,000 with the same staffing,” said Shah, now CEO of Kaaj. “Suddenly, smaller loan applications become economically worthwhile.”

The broader vision is to make small-business lending more accessible by lowering operational costs for banks and lenders.

Kaaj enters a competitive landscape that includes companies such as Middesk, Ocrolus, and MoneyThumb. However, Sharma believes Kaaj differentiates itself by fully automating the entire credit analysis lifecycle rather than just individual components.
“We deploy agentic AI workflows that function like an extension of the lender’s underwriting team, ananalyzingoan packages end-to-end,” she said.

The new funding will support the expansion of Kaaj’s product capabilities, the introduction of new AI agent features, and broader adoption among independent and small-business lenders. The team also plans to increase its modules and grow its customer base of lenders and brokers.

Shah and Sharma ultimately aim to modernise a process that remains heavily paper-based.
“By bringing automation to the science behind credit assessment, we free underwriters to focus on the art of evaluating deals — the part where their skills provide the most value,” Shah said.