When 40% of enterprise data is either duplicate, inconsistent, or simply wrong, what happens to the decisions based on it? For many businesses, the consequences remain invisible ,  until they surface in the form of compliance violations, customer losses, or financial missteps. As the world becomes more data-dependent, a silent crisis has emerged around one of the least glamorous but most vital aspects of digital infrastructure: data quality.

According to an IBM estimate, bad data costs U.S. businesses more than $3.1 trillion annually. In finance, where decisions must be precise, timely, and heavily regulated, this data debt can translate into direct losses or regulatory fines. Yet most data quality checks remain reactive, manual, and incapable of scaling with the velocity of modern digital systems.

Vamsi Kunaparaju, a data expert and founder of multiple technology ventures, believes the time has come for a radical shift. His latest startup, DMC, is taking on this challenge head-on with a suite of AI-powered agents designed to continuously monitor, clean, and improve data quality at its source.

“Most organizations are building on sand,” Kunaparaju explains. “Their analytics are only as good as the data underneath. And the data underneath is rarely as good as they think it is.”

Kunaparaju's journey to launching DMC is rooted in a unique vantage point: years of working across sectors through LabFox, a product development studio he co-founded. LabFox specialized in helping early-stage startups build their first versions, and through those partnerships, a pattern emerged.

“At LabFox, we were working on projects in finance, construction, healthcare ,  very different industries,” he says. “But the problem we saw again and again was data fragmentation. Every startup had data issues hiding in plain sight, and fixing them manually just wasn’t sustainable.”

One notable collaboration was with Sarus, a Boulder-based startup building lift-planning software for the heavy-lifting industry. Sarus needed a minimum viable product to demonstrate to investors and clients ,  and they needed it fast. Under Kunaparaju’s leadership, LabFox rapidly built a robust MVP, integrating real-time geo-location and automated reporting features.

“The challenge wasn’t just shipping the product,” he recalls. “It was doing it in a way that handled messy data from physical environments, sensors, and human inputs. That kind of work forces you to think deeply about data integrity.”

While Sarus went on to raise over half a million dollars post-launch, for Kunaparaju, the takeaway was even larger: the need for a scalable, industry-agnostic solution to data quality.

That solution is now called DMC (short for Data Management Company ), which brings to market a suite of intelligent agents including DQX-AI, MDX-AI, and DQRX-AI. These agents perform everything from deep code-level data vulnerability scans to real-time anomaly detection, end-to-end validation pipelines, and lineage build-up.

“Think of DMC as your 24/7 audit partner that doesn’t sleep,” says Kunaparaju. “It’s constantly scanning, learning, and improving your data organisation without interrupting business operations.”

The results so far have been promising. Early adopters across finance, healthcare, and retail report an average 75% reduction in manual data cleansing efforts, along with measurable gains in data confidence and reporting accuracy.

For one global financial services client, DMC helped identify a series of silent failures in customer onboarding data that had led to inaccurate risk assessments and regulatory friction.

“They had all the right tools, but no centralized truth,” Kunaparaju explains. “Our AI agents found inconsistencies in naming conventions, transaction tagging, and even metadata lineage that had been causing cascading errors for months.”

The community-level impact of such work becomes clear when you understand where bad data ends up: in risk models, credit decisions, public health dashboards, or infrastructure spending plans. Fixing data quality isn’t just a technical goal ,  it’s a civic imperative.

“If your data says a patient doesn’t have a condition– they don’t get treated. If your financial system mislabels risk, capital gets misallocated. These aren’t abstract problems,” Kunaparaju emphasizes. “These are real-world outcomes.”

DMC’s approach is deeply aligned with growing trends in AI governance, privacy-by-design, and data ethics. As more organizations adopt artificial intelligence, ensuring the integrity of training data has become paramount. DMC not only cleans data but also provides transparent reporting and audit trails, helping institutions remain compliant with emerging regulations like the EU AI Act and state-level data privacy laws in the U.S.

Looking ahead, Kunaparaju sees DMC becoming a cornerstone of digital infrastructure.

“We’re not building a dashboard or a reporting tool,” he clarifies. “We’re building the plumbing. We want to be the layer that guarantees clean, trustworthy data for every tool that sits on top.”

With its intelligent automation, human-centered design, and cross-platform integrations, DMC is quietly changing how modern institutions think about the foundation of their digital operations.

“The best tech should feel invisible,” Kunaparaju says. “But its absence? That you’d definitely notice.”

By tackling one of the least talked about but most fundamental problems in modern enterprise systems, Vamsi Kunaparaju and DMC are offering a blueprint for not just smarter business,  but safer, fairer, and more accountable ones too.

About Author:

Michael Cain is a NewsBreak contributor and an Editor at Springer Nature, focusing on tech-driven narratives and financial reporting. With a background spanning artificial intelligence, cloud computing, and emerging fintech innovations, Michael has authored pieces like “AI-Powered Merchant Risk Assessment” and “Breaking New Ground in Data Security,” spotlighting cutting-edge solutions that shape modern businesses. Equally at home analyzing corporate earnings or exploring advanced technology trends, Michael aims to bridge the gap between complex concepts and everyday impact. 

Connect with him at [email protected] for insights into the evolving frontiers of tech, finance, and beyond.