Small-business lending has always had a timing problem. A business owner may need capital to buy equipment, manage payroll, stock inventory, open a second location, or handle a sudden cash-flow gap. But the lending process often moves much slower than the business need itself.
That gap is where Shivi Sharma and Kaaj are building their story.
As Co-Founder and President of Kaaj, Shivi Sharma is working on one of the most difficult parts of financial services: helping lenders review small-business loan applications with more speed, consistency, and confidence. Kaaj is an AI-powered credit intelligence platform built for small-business lending, especially for lenders that still depend on manual document review, scattered workflows, and time-consuming underwriting steps.
The goal is not simply to make lending look more modern. It is to help financial teams move from piles of forms, statements, and documents to decision-ready analysis in a much shorter time. For small businesses, that can mean faster access to capital. For lenders, it can mean a better way to serve more borrowers without losing control of risk.
Who is Shivi Sharma
Shivi Sharma is the Co-Founder and President of Kaaj, a fintech company focused on bringing AI into credit analysis and small-business loan underwriting. Her background matters because Kaaj is not being built only from a software point of view. It is being shaped by someone who understands how credit teams think, how risk decisions are made, and why lenders are careful by nature.
Before building Kaaj, Shivi Sharma worked in credit and fraud risk across major financial and technology companies, including American Express, Uber, and Varo Bank. That experience gave her a close view of how risk models, borrower data, fraud checks, and financial controls come together inside real lending environments.
That kind of background is important in fintech because lending is not a simple automation problem. A loan decision is not just about whether a form is complete or whether a number looks strong. It involves judgment, policy, verification, borrower behavior, cash-flow patterns, and the lender’s own appetite for risk.
This is why Shivi Sharma’s role at Kaaj is important. She brings the practical risk lens that many AI products in finance need. In lending, speed only matters if the lender can still trust the process. Kaaj is trying to meet both needs at the same time.
What Kaaj is building
Kaaj describes itself as an AI-powered credit intelligence platform for small-business lending. In simple terms, the platform helps lenders analyze loan applications faster by using AI agents to review documents, verify businesses, examine financial data, assess risk, and create credit memos.
For many lenders, the underwriting process is still heavily manual. A credit team may need to review bank statements, tax forms, invoices, ownership records, business information, asset details, and other supporting documents before making a decision. Even when the loan size is relatively small, the review process can still require a lot of human time.
That creates a difficult business problem. Smaller loans can be important for borrowers, but they may not always be profitable for lenders to process if every file requires hours or days of manual work.
Kaaj is built around that problem. Its platform is designed to take a loan package and turn it into structured, usable analysis. Instead of asking credit teams to hunt through documents line by line, Kaaj aims to surface the key information they need to review a borrower more efficiently.
This can include business verification, bank statement analysis, cash-flow analysis, asset valuation, risk assessment, and AI-assisted credit memo generation. The result is a workflow that helps lenders move faster while keeping credit teams involved in the decision.
The lending problem Kaaj is trying to fix
Small-business lending sits in a difficult middle ground. The borrower may not have the same level of financial reporting as a larger company, but the lender still needs enough information to make a responsible decision.
A small business may submit bank statements, tax returns, invoices, equipment details, ownership information, and other documents. The lender then has to check whether the business is real, whether the financials make sense, whether the cash flow can support repayment, and whether the loan fits the lender’s credit policy.
That work takes time. It also requires consistency. If one underwriter reviews files differently from another, the lender may end up with uneven decisions. If the process is too slow, the borrower may leave for another funding option. If the process is too loose, the lender may take on risk it does not fully understand.
This is the core tension Kaaj is trying to solve. The company is focused on making small-business lending faster without turning the process into a black box. For lenders, the ideal outcome is not just a quick answer. It is a clearer file, better organized data, and a more reliable way to understand borrower risk.
How Shivi Sharma’s risk background shapes Kaaj
One reason Shivi Sharma stands out in this space is that her career connects directly to the product she is building. Credit-risk automation is not a category where surface-level knowledge is enough. The product has to fit the way lenders actually work.
Credit teams care about more than speed. They care about policy, documentation, auditability, fraud signals, borrower verification, and whether a recommendation can be explained. A lending tool that only promises automation but does not respect these realities will struggle to earn trust.
Shivi Sharma’s background in credit and fraud risk helps Kaaj approach the market with that understanding. Her work at companies such as American Express, Uber, and Varo Bank placed her close to the systems and decisions that shape financial risk. That experience can help a founder understand what lenders need before they feel comfortable adopting new technology.
This is especially important in AI-driven lending. Many financial teams are open to automation, but they do not want blind recommendations. They want tools that support their judgment, organize their work, and give them a clearer view of the borrower. Kaaj appears to be built around that kind of practical AI use case.
How Kaaj uses AI to make lending faster
The strength of Kaaj is its focus on the credit workflow itself. Instead of only helping with one narrow task, the platform is designed to support multiple steps in the underwriting process.
A lender may receive a loan package with many different files. Kaaj can help ingest those documents, extract important details, and organize the information into a format that is easier for credit teams to review. This reduces the time spent on repetitive manual work.
The platform can also help with business verification. This matters because lenders need to know that the borrower is legitimate and that the information in the application matches available records. In small-business lending, verification is a key part of managing both credit and fraud risk.
Bank statement analysis is another important piece. A bank statement can reveal cash-flow patterns, revenue movement, recurring expenses, and possible warning signs. Reviewing this manually can be slow, especially when lenders handle a high volume of applications. AI can help pull out patterns faster and present them in a more useful way.
Kaaj also supports financial analysis, asset valuation, risk assessment, and credit memo creation. Credit memos are especially important because they help summarize the borrower, the loan request, the risk factors, and the reasoning behind a decision. When AI helps prepare a memo, it can save time for underwriters while still leaving room for human review.
The result is a workflow where lenders can move from raw documents to a clearer lending picture much faster.
Why faster lending matters for small businesses
For a small business, timing can be everything. A restaurant may need funds to replace equipment. A retailer may need inventory before a busy season. A contractor may need working capital to take on a new project. A medical practice may need financing for new tools or expansion.
When capital arrives too late, the opportunity may already be gone.
This is why faster small-business lending matters. It is not only about convenience. It can shape whether a business can grow, hire, recover, or compete. A slow underwriting process can create stress for the borrower and make the lender seem difficult to work with, even when the lender is simply trying to be careful.
By helping lenders process applications more quickly, Kaaj could improve the borrower experience without asking lenders to skip important checks. That balance is important. Small businesses want speed, but lenders still need structure.
Why smarter underwriting matters for lenders
Speed is only one side of the story. Lenders also need smarter workflows.
In lending, a faster bad decision is still a bad decision. That is why underwriting automation has to support accuracy, consistency, and transparency. Credit teams need to know what the system reviewed, what it found, and where the possible risks are.
Kaaj is positioned around that need. By helping organize loan data and prepare decision-ready analysis, the platform can make the underwriting process more consistent across teams. This can be valuable for banks, credit unions, equipment finance companies, loan brokers, and alternative lenders that want to grow without adding unnecessary manual burden.
A smarter workflow also helps lenders protect their own teams. Underwriters often spend too much time on repetitive document review instead of higher-value judgment. If AI can handle more of the early analysis, credit professionals can spend more time on the parts of lending that require experience and context.
That is where Kaaj can become useful. It is not about removing the underwriter from the process. It is about giving the underwriter a cleaner, faster starting point.
Kaaj’s role in equipment finance and SMB lending
Kaaj is especially relevant for lenders working in SMB lending and equipment finance. These markets often involve real business needs, fast timelines, and detailed loan packages.
Equipment finance is a good example. A business may need funding to buy machinery, vehicles, medical equipment, restaurant equipment, or other assets. The lender has to understand both the borrower’s financial health and the value of the asset involved. That can make the underwriting process more complex than it may appear from the outside.
For small-business lenders, the challenge is scale. They need to review many applications without letting quality drop. Manual workflows make that difficult. AI-powered credit intelligence can help lenders handle more volume while keeping review standards more consistent.
This is why Kaaj’s product sits in a practical part of fintech. It is not chasing a vague idea of AI transformation. It is targeting a real bottleneck inside lending operations.
Funding and early momentum behind Kaaj
Kaaj has also gained attention from investors and industry partners. The company announced a $3.8 million seed round led by Kindred Ventures, with participation from Better Tomorrow Ventures and other backers. That funding gives the company more room to build its product and expand its reach across small-business lending and equipment finance.
The company has also said it has processed more than $5 billion in loan applications, which suggests that its product is being tested against serious lending volume. Customers and partners associated with Kaaj include names such as Amur Equipment Finance, Quality Equipment Finance, and Fundr.
For a young fintech company, this kind of traction matters. Lending is a trust-heavy market. Financial institutions do not adopt new systems only because they sound modern. They need to see that a product can work inside real workflows, handle sensitive information, and support decisions that affect both borrowers and lenders.
Why Kaaj’s approach feels different
Many fintech tools promise automation. What makes Kaaj interesting is the way it focuses on the full credit analysis journey.
A lender does not only need a form parser. It does not only need a dashboard. It does not only need a credit memo template. It needs a connected workflow that can move from application intake to borrower verification, financial review, risk analysis, and final credit documentation.
That is the broader space Kaaj is trying to serve. Its AI agents are designed to support the tasks that slow down underwriting teams every day. By connecting these steps, the company can offer more than a point solution.
This matters because lending workflows are rarely simple. One file can contain missing information, unclear financial patterns, inconsistent business details, or unusual activity that needs a closer look. A useful AI platform must help credit teams see those details rather than hide them.
For Shivi Sharma, this is where risk experience and product direction come together. Kaaj is not only about making AI available to lenders. It is about making AI useful in the exact places where lenders lose time.
What Shivi Sharma’s work says about the future of fintech
The story of Shivi Sharma and Kaaj reflects a larger shift in fintech. The next wave of financial technology is not only about digital applications or cleaner customer interfaces. It is about rebuilding the back-end workflows that decide how fast money moves.
Small-business lending has long needed better infrastructure. Many lenders still rely on fragmented systems and manual review, even as borrowers expect faster service. AI gives the industry a chance to rethink that process, but only if the technology is built with risk, compliance, and real lending judgment in mind.
That is why founders with deep domain experience can have an advantage. Shivi Sharma understands the pressure on credit teams because she has worked in environments where risk decisions matter. That understanding can help Kaaj build technology that feels practical rather than flashy.
In a market filled with AI claims, practical value is what matters most. Lenders need tools that reduce manual work, improve consistency, and help teams make better use of their time. Borrowers need lenders that can respond faster without treating their applications like a slow paperwork exercise.
Kaaj sits between those needs.
Why Shivi Sharma and Kaaj matter in the next chapter of small-business lending
Shivi Sharma is building Kaaj around a clear idea: small-business lending should be faster, but it should also remain thoughtful, transparent, and controlled.
That idea matters because small businesses are often underserved by traditional lending workflows. Their loan needs may be smaller, but their urgency is real. If lenders can process those applications more efficiently, they can serve more businesses while keeping risk management at the center of the process.
Kaaj is trying to make that possible with AI-powered credit intelligence. By helping lenders review documents, verify businesses, analyze financials, assess risk, and prepare credit memos, the platform gives credit teams a more modern way to work.
For Shivi Sharma, the opportunity is bigger than automation. It is about making small-business lending more practical for the lenders who provide capital and more responsive for the businesses that need it.That is what makes her work at Kaaj worth watching. She is not just building another fintech product. She is helping reshape a lending workflow that has been too slow for too long.
