Case Study: AI-Powered Underwriting Accelerates Capital Access for Modern Businesses

Written by Steve Iskander (Intrepid), April Ray (Intrepid), and Mary Dougherty (TechPoint)

The commercial funding ecosystem has long relied on underwriting systems designed for an earlier era of business finance. These outdated workflows slow underwriting cycles, increase operational costs, and limit the ability of capital providers to evaluate businesses accurately.

The structure of the modern economy has evolved significantly. Service-based companies, SaaS businesses, and recurring revenue models now represent a growing share of the small and mid-sized business landscape. Yet many underwriting systems still rely on outdated credit frameworks that struggle to interpret these financial patterns. As a result, businesses seeking growth funding frequently encounter lengthy application processes, redundant documentation requirements, and inconsistent underwriting outcomes.

Intrepid addresses this infrastructure gap. Rather than operating as a lender, the company is building an AI-powered technology platform designed to modernize how capital is evaluated, structured, and deployed. The platform connects businesses, brokers, and lenders within a unified ecosystem, integrating data from banking platforms, accounting systems, and payment processors to generate a real-time financial profile for each business.

By transforming financial data into real-time insights, Intrepid enables lenders to move beyond manual document review toward automated, data-driven decision-making. The result is faster underwriting decisions, more efficient capital allocation, and improved access to growth financing for businesses. Importantly, the platform is designed with no cost to founders, allowing business owners to focus on running and growing their companies rather than navigating complex capital-raising processes.

Industry Context & Business Challenge

The commercial lending industry has always relied heavily on a manual underwriting process that evaluate businesses through tax returns, bank statements, and financial documents submitted as static files. While this approach has historically served lenders, it introduces significant operational constraints. Manual financial review can take hours or even days per application, slowing underwriting cycles, increasing operational costs, and limiting the number of businesses lenders can evaluate.

These workflows also create redundant and inefficient application processes. Businesses and brokers are often required to submit the same documentation repeatedly across multiple lenders and funding products. At the same time, traditional credit models tend to prioritize historical balance sheet metrics that do not fully reflect the financial dynamics of modern businesses, such as SaaS companies or firms with recurring revenue models. Because static financial documents provide only historical snapshots, lenders have limited visibility into real-time business performance.

As a result, structural inefficiencies persist across the capital ecosystem. Many viable businesses struggle to access financing despite strong operational performance, while lenders face capacity constraints that prevent them from evaluating more opportunities. The complexity and time required to complete applications also discourage many founders from pursuing funding at all.

These challenges contribute to a broader gap in capital access. In 2025, only 41 percent of businesses received the funding they applied for, highlighting the limitations of traditional underwriting infrastructure. At the same time, demand for faster and more flexible financing continues to grow, leading borrowers to explore alternative lenders, private credit funds, and specialty finance providers while often assembling their capital stack manually from multiple sources.

How can the commercial lending industry overcome slow, manual underwriting processes and outdated credit evaluation models to enable faster, more accurate access to growth capital for modern businesses?

AI Implementation Strategy

Designing a Data-Driven Capital Platform

Intrepid designed its platform to rebuild underwriting infrastructure around three foundational principles:

• Real-time financial data integration
• AI powered financial analysis
• Marketplace based capital deployment

Rather than operating as a single funding source, the platform functions as a multi-sided ecosystem connecting businesses, brokers, and capital providers. This architecture streamlines the flow of financial information across the capital ecosystem while enabling lenders to evaluate opportunities faster and more efficiently.

Replacing Static Documents with Live Financial Data

Traditional underwriting typically begins with collecting static financial documents such as tax returns and bank statements. Instead, Intrepid prioritizes direct integrations with financial systems including banking platforms, accounting software, and accounts receivable systems. These integrations allow the platform to generate a dynamic financial profile for each business, providing lenders with real-time insight into financial performance rather than relying solely on historical snapshots.

AI-Assisted Financial Analysis

Once financial data is integrated, Intrepid’s AI models analyze multiple financial signals simultaneously, including cash flow health, revenue stability, accounts receivable quality, seasonality trends, and potential risk indicators. The system aggregates and normalizes data from multiple sources, then classifies patterns and behaviors to generate structured underwriting insights that lenders can evaluate almost instantly. This automated analysis enables faster and more consistent risk assessment across a wide range of business models.

Institutional Capital Infrastructure

Beyond financial analysis, the platform connects businesses to a broad network of institutional capital providers, including private credit funds, venture capital firms, venture equity investors currently in beta, and SBA-backed financing programs. Through this institutional network, the platform can facilitate funding transactions ranging from approximately $5,000 to $50 million, creating a more flexible and scalable infrastructure for capital deployment.

Solution and Implementation

The platform launched in beta with a rapidly expanding network of brokers and capital providers, validating strong market demand for AI-powered underwriting infrastructure. During development, the company conducted more than 200 pilot programs with early lending partners to refine the product and ensure it addressed real-world underwriting challenges. As a result, the beta platform successfully onboarded more than 150 brokers and private credit lenders, demonstrating strong adoption across the capital ecosystem.

At the core of the platform is a financial intelligence engine that processes financial data through a structured pipeline of ingestion, normalization, classification, and detection. These processes transform raw financial information into structured underwriting insights within seconds, allowing lenders to evaluate opportunities quickly and consistently. To further streamline the funding process, Intrepid introduced a unified capital application that enables businesses to submit a single application and document upload for multiple lenders, significantly reducing friction in traditional funding workflows.

In addition to underwriting analysis, the platform identifies potential tax incentives and financial opportunities through a partnership with McGuire Sponsel. This capability allows businesses to uncover tax credits and incentives that might otherwise be overlooked, helping founders build a more comprehensive and optimized capital strategy.

Outcomes

Accelerated Underwriting Decisions

AI-powered automation dramatically improved underwriting speed and efficiency. Financial reviews that previously required hours of manual analysis can now be completed in seconds. Overall document collection and underwriting review time decreased from roughly eight hours of manual analysis to less than two minutes of automated evaluation, allowing lenders to make faster and more informed funding decisions.

Broker Workflow Acceleration

The platform also significantly improved broker productivity by streamlining deal preparation. Tasks that previously required two to three days can now be completed in under one hour. As a result, brokers increased deal volume by approximately 68% while reducing administrative workload and improving responsiveness to clients seeking capital.

Increased Underwriting Capacity

Automation expanded the capacity of underwriting teams without requiring additional staffing. By reducing manual analysis and standardizing financial evaluation, lenders were able to increase underwriting capacity by up to 90% while maintaining consistent risk standards and decision quality.

Expanded Access to Capital and Early Market Validation

The improved infrastructure translated into greater access to capital for businesses. Companies using the platform secured an average initial funding of approximately $150,000 in growth capital. Within the first 90 days of deployment, the platform attracted strong market adoption, onboarding institutional capital providers, private credit firms, and dozens of brokers on paid subscriptions. As adoption accelerated, the company expanded its commercial team in 2026 by adding eight additional sales representatives to support continued growth.

Why Now? AI and Financial Data Infrastructure

Several technological shifts have made AI-powered underwriting infrastructure increasingly viable. Advances in financial data accessibility driven by open banking frameworks and modern financial APIs now allow secure integration with banking systems, accounting platforms, and payment processors. At the same time, modern machine learning models can analyze large financial datasets quickly, identifying behavioral patterns and financial signals that traditional credit models often miss.

These developments coincide with growing demand for greater capital efficiency across the lending ecosystem. Banks, private credit funds, and specialty finance providers are under increasing pressure to deploy capital faster while maintaining strong risk controls. As alternative lending markets continue to expand, financial institutions require technology infrastructure capable of evaluating diverse business models and financial structures more accurately and at greater speed.

Conclusion

The commercial funding industry has long relied on underwriting systems built around manual processes and static financial documentation. As modern businesses have evolved particularly with the growth of service-based, SaaS, and recurring-revenue models these legacy systems have struggled to keep pace. As a result, many lenders face inefficiencies in evaluating businesses, while many companies encounter barriers when seeking growth capital.

Intrepid introduces a new infrastructure model for capital access built around real-time financial data, AI-powered analysis, and integrated capital deployment technology. By transforming financial data into structured insights, the platform enables faster underwriting decisions, more efficient capital allocation, and improved visibility into borrower performance.

By integrating multiple funding sources, tax incentives, and financial insights into a single platform, Intrepid enables businesses to build a more intelligent and optimized capital stack. This approach not only improves operational efficiency for lenders but also expands access to growth financing for businesses that may be overlooked by traditional underwriting models, allowing founders to focus on operating and growing their companies.

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