Why Boring-Old-Data Still Wins the AI Arms Race

June 6, 2025

In an era obsessed with artificial intelligence and shiny new tech, a quieter truth has re-emerged—boring-old-data is still the crown jewel of digital innovation. Just ask Palantir and Snowflake.

Palantir’s recently announced partnership with Fannie Mae is a powerful indicator of where enterprise AI is heading. The goal? Use AI to sniff out mortgage fraud—not with guesswork, but with decades of meticulously archived loan performance data. Meanwhile, Snowflake has unveiled a new suite of AI agents, purpose-built to make data lakes accessible to business users without a technical background. These tools promise to turn raw data into actionable insight at the click of a button.

But here’s the rub:

Both these cutting-edge moves depend entirely on a far less glamorous asset—having deep, high-quality, accessible data. And that’s a luxury most smaller firms can’t afford.

The AI Gold Rush Is a Data Story

It’s tempting to believe that the AI arms race is about models. Who has the biggest LLM? Who’s built the most predictive fraud engine? But under the hood of every AI marvel lies something far less headline-worthy: vast, structured data repositories.

Palantir’s edge with Fannie Mae doesn’t come from their machine learning prowess alone. It comes from their ability to embed intelligence into workflows already fed by historical underwriting, servicing, and risk data. Likewise, Snowflake’s AI agents aren’t magically smarter—they’re useful because the underlying data is already organized, normalized, and permissioned.

In both cases, the data is the hard-won asset. The AI just activates it.

The Haves and the Have-Nots of Mortgage Tech

This brings us to the uncomfortable reality for small to mid-sized mortgage lenders. They don’t have data lakes. They don’t have historic tapes cleanly indexed across underwriting, servicing, and secondary markets. And they certainly don’t have the budget or IT bandwidth to unify decades of fragmented data into an AI-ready environment.

That’s where the cracks begin to show.

The big players can afford to turn data into defense and differentiation. Smaller players, however, are often stuck in systems where CRM data lives in one silo, LOS data in another, and disclosures are still double-checked by hand.

This isn’t just a tech gap—it’s a survivability issue. As fraud detection becomes automated, customer personalization becomes data-driven, and underwriting becomes semi-autonomous, those without a clean data foundation are left reacting instead of innovating.

Leveling the Playing Field with Tools That Center Data

This is where companies like Brimma come into play. While many vendors focus on narrowly solving high-pain problems, Brimma takes a broader, more transformative approach—layering large language models (LLMs) directly onto the operational data that matters most to small and mid-sized lenders. Instead of building AI in a vacuum, Brimma’s tools work with your existing systems to unlock the value in your data, making automation and insight accessible without massive infrastructure overhauls.

If a small-to-medium sized lender can successfully enable an LLM to access and understand data across its CRM, POS, LOS, and Servicing platforms, the implications are profound—and not just for internal operations.

Think of it this way: what Palantir is doing for enterprise-scale institutions like Fannie Mae—combining historical data, real-time analytics, and AI reasoning to identify fraud, optimize performance, and support strategic decisions—can now be mirrored, at scale and at speed, within the walls of a 100-person lending shop.

With unified access to customer interactions (CRM), application progress (POS), underwriting and funding details (LOS), and payment performance (Servicing), an LLM becomes more than just a chatbot. It becomes a virtual analyst, an operations assistant, and a compliance auditor—all rolled into one.

Imagine an underwriter asking, “What changed on this borrower’s application since last submission?” and getting an instant, cross-system answer. Or a branch manager querying, “Which loan officers have the most stalled apps, and what do their borrower notes suggest?” and receiving not just a report, but a narrative summary with recommendations.

This kind of intelligence has long been the domain of enterprise software platforms—expensive, bespoke, and locked behind years of data governance. Brimma’s approach, by contrast, is to provide contextual AI that adapts to the data lenders already have, no matter how fragmented.

The result? Smaller lenders can begin to act like big ones—anticipating risk, optimizing workflows, and enhancing borrower experience with the same strategic clarity that Palantir delivers to the giants. And in a volatile market, that kind of agility can be the difference between merely surviving and setting the pace.

Data First, but Not Data Alone

If you’re a lender pursuing AI while still wrangling with data silos, brittle integrations, or manual document checks, it’s time to reassess. AI won’t fix bad data—it’ll just make the consequences of that bad data show up faster and louder.

But when your data is reliable, organized, and accessible, AI becomes transformative. That’s the vision Brimma is delivering—not by replacing your systems, but by making them intelligible to AI.

And here’s where recent breakthroughs unlock the next frontier. With innovations like Anthropic’s Model Context Protocol (MCP), large language models can now reference an ever-expanding universe of external knowledge without needing to be retrained. Pair this with modern coding AIs that can spin up system integrations on demand, and testing AIs that proactively simulate edge cases across your LOS, CRM, and Servicing stack—and you’ve got a powerful trifecta.

This is the scaffolding that makes Brimma’s Vallia tools more than just automation—they’re intelligent systems that reason across your real-world data, in real time, for real impact.

Because in mortgage lending, data has always been king. But now, with the right infrastructure, it can also be clairvoyant. The firms that win won’t be the ones chasing hype. They’ll be the ones who understood that data was only the beginning—and built something brilliant on top of it.

Want to learn more about how Brimma can help you LLM-ify your data? Schedule a call!