Leveraging AI in Capital Deployment to Accelerate Economic Expansion

The AI tsunami is already here. While conversations mostly focus on disruptions to existing industries, the need to discuss how to leverage the technology for constructive purposes remains. In this context, AI is beginning to shape the future of capital access for businesses at scale, beyond just consumer use cases.

In personal finance, there is a fair bit of AI deployment. Perplexity offers equity markets on its finance platform. Neobanks like Fi Money and Jupiter have deployed AI engines to provide deep spending insights. HDFC Bank has deployed Eva, an AI assistant that uses natural language processing to assist retail customers.

Regarding capital deployment and due diligence, developments exist, but they are mostly siloed or exclusive.

For instance, Deloitte has AI Due Diligence, which can evaluate “AI‑driven risks and readiness across technology, data and talent to protect deal value and identify actionable AI use cases that drive revenue uplift and cost efficiency.” And similarly, there are a few VC-exclusive tools.

A Broken Reality: Due Diligence

“…Conventional due diligence is a broken, backward-looking process reliant on fragmented PDFs, slow human audits, and weeks of manual back-and-forth…”

Bhavik Vasa’s observations are not unfounded. In fact, anyone who has tried to raise capital in India in the last decade or so knows this is a painful reality. This is despite India’s startup growth being widely seen as a success story. And the same applies to MSMEs and other smaller ventures looking for capital.

It’s quite surprising that due diligence feels like familiar red tape, only slowing capital deployment. By the time a human analyst clears a profile, a modern enterprise on the cusp of rapid scale may lag behind the curve by weeks or months. Then there’s bias, and the classic “need to know” someone influential.

“..Conventional diligence takes weeks and looks at where a business was six months ago,” says Vasa, founder & CEO of GetVantage, a fintech and embedded-finance platform.

Shift to Agentic AI: Augmentation Over Replacement

Speaking to Entrepreneur India, Founding Partner Chetan Mehta of AUM Ventures explains that venture capital has historically relied on human network checks and weeks of manual auditing. How do you see the introduction of Agentic AI — systems that can autonomously investigate, reason, and cross-reference data — fundamentally changing this traditional approach?

“Agentic AI has the potential to significantly improve the efficiency and depth of the diligence process by enabling faster, broader, and more structured analysis of information. Traditionally, due diligence has relied heavily on manual reviews, fragmented data gathering, reference checks, and human-led synthesis. AI systems can augment this process by rapidly consolidating and cross-referencing large volumes of information across legal, technical, regulatory, and market datasets,” Mehta added.

According to Mehta, agentic systems can assist with cross-verification of patent filings across databases such as the Indian Patent Office, USPTO, EPO, and WIPO, founder and corporate background verification, analysis of regulatory filings and compliance records, benchmarking startups against historical datasets of successful and unsuccessful ventures in similar sectors, and mapping competitive landscapes, supply-chain dependencies, and emerging market signals.

In deeptech specifically, one of AUM Ventures’ key focus areas, Mehta says AI can also help investors process highly technical information at greater speed, which is increasingly important as sectors such as aerospace, semiconductors, and robotics become more globally interconnected and technologically complex.

“That said, AI should be viewed as an augmentation layer rather than a replacement for investor judgment. The role of these systems is to improve the quality and velocity of information synthesis, allowing investment teams to focus more deeply on strategic evaluation and conviction-building,” he added.

Case Study: The Digital Due Diligence & AI

Vasa and the team have come up with an AI-enabled solution called “DDx” to help stakeholders fast track due diligence.

In a familiar dashboard-like interface, the process is entirely digital. And it begins the moment an investor, lender, or founder initiates a request on the platform.

The USP of the platform is that it does not ask for stacks of paper but relies on a secure integration of real-time business APIs, including GST data, payment gateways, banking stacks, and marketplace transaction logs.

The AI engines onboard are pre-trained on data from more than 120,000 businesses. It instantly helps clean, organize, and map unstructured data. The result is billions of data points quickly synthesized into actionable investor-grade risk and health metrics.

Like typical plug-and-play, investors can quickly use the Applied AI to track business performance.

Vasa of GetVantage explains: “The Vision behind GetDDx.com  (by GetVantage) is to move the industry past the generic AI “FOMO” and deploy true Applied AI to solve a massive structural bottleneck in business: the  velocity of capital deployment. Conventional due diligence is a broken, backward-looking process  reliant on fragmented PDFs, slow human audits, and weeks of manual back-and-forth. DDx completely flips this equation by shifting from human-driven documentation to API-driven proprietary data synthesis.”

“We built DDx because the biggest friction in enterprise growth isn’t a lack of data; it’s the lack of velocity in understanding it. DDx turns due diligence from a weeks-long manual underwriting into an instant 10-minute investor-grade report for financing & funding decisions,” he added.

Are there any takers for such AI tools? And who’s buying it?

If Vasa of GetVantage is to be believed, there are quite a few. He disclosed that the market validation has been immediate and overwhelming with VCs using DDx to instantly screen their early-stage pipelines, saving hundreds of hours of analyst time.

Lenders and NBFCs are leveraging it to accelerate their credit committees, deploying capital into MSMEs at “unprecedented speeds”. According to Vasa, founders like the tool as it eliminates the painful “diligence fatigue” of raising capital, and institutional investors praise it for revealing hidden operational risks that standard balance sheets completely miss.

“”The ecosystem is realizing that speed is a massive moat. The feedback from VCs and lenders is clear: once you experience a 10-minute automated diligence loop, going back to a 4-week manual audit feels like moving backward in time,” he said.

As mentioned above, an efficient AI-enabled diligence dashboard also brings a level-playing field to the game. Such tools allow a regional lender or a boutique fund the exact same institutional-grade analytical horsepower as a global player.

GetVantage Founders (L- Amit Srivastava, R- Bhavik Vasa)

For younger, non-connected founders, especially those operating in India’s booming tier-2 and tier-3 economies it strips away personal bias and “pedigree” gatekeeping. Capital allocation becomes purely meritocratic, based on real-time data performance, not who you know.

Trust is Must

Can AI-driven diligence be fully trusted? Yes given they are able to consistently deliver accurate, reliable, and explainable outputs over time.

In many ways, that adoption has already begun.  AI today is increasingly being used as a support layer within evaluation and diligence workflows, according to Mehta of AUM Ventures.

“However, full automation is unlikely, particularly in deeptech investing. Deeptech companies are inherently non-standardized. Every venture may have a unique technology stack, regulatory pathway, scientific dependency, manufacturing challenge, or IP architecture. Evaluating these businesses requires contextual understanding, technical intuition, and long-term judgment that cannot be fully automated,” explained.

He noted that venture investing is not purely a data exercise. Founder quality, resilience, integrity, ambition, and the ability to execute through uncertainty remain deeply human assessments.

“The future is therefore likely to be a hybrid model, where autonomous systems handle large-scale investigation, verification, and data synthesis, while investors continue to lead judgment, relationship-building, and nuanced decision-making,” he said.

Vasa adds that the company uses DDx as a critical utility layer for the financial ecosystem, operating on a B2B SaaS model (pay-per-report) alongside enterprise API licensing for high-volume institutional lenders, VCs, and embedded finance platforms.

“Given our institutional position, DataTech’s first approach, operating within the regulatory and compliance guardrails and deeply integrated into the digital public infrastructure, data security isn’t an afterthought; it’s our foundational architecture. DDx operates on a strict zero-trust, consent-driven architecture. Data is entirely encrypted end-to-end, anonymized at the model layer, and fully compliant with India’s Digital Personal Data Protection (DPDP) frameworks and RBI guidelines,” Vasa concluded.

The GetVantage team now is looking to aggressively scale the platform to move from retrospective analysis to active operational execution. Our upcoming pipeline introduces real-time tender analysis and automated B2B proposal workflows. The engine will read massive, complex  multi-page corporate or government RFPs, cross-reference them against an enterprise’s proprietary capacity database, and draft tailored compliance and bidding proposals instantly.