Integral transforms real-world data into AI-ready assets for training, labeling, and enterprise AI applications.
NEW YORK--(BUSINESS WIRE)--Integral Privacy Technologies today announced $25 million total raised with participation from Venrex, The General Partnership, Array Ventures, GreatPoint Ventures, LiveRamp Ventures, Haystack, Virtue Ventures, Also Capital, Caffeinated Capital, LifeX Ventures, Circle & Co, and WS Investments.
The funding will accelerate the deployment of Integral's Forward Deployed Privacy Services, providing an independent privacy layer that activates real-world data safely for the AI economy.
The first generation of AI was built on public data and human-curated data. The next generation is now bringing in real-world data. Health records, financial transactions, customer interactions, operational systems, codebases, and other real-world datasets contain the real-life complexity and decision-making patterns. AI companies are actively seeking these datasets, while companies and individuals are looking to monetize them. This demand is new and accelerating. As public sources get exhausted and AI seeks to expand deeper into expert domains and real-world use cases, AI builders are competing to secure enterprise and proprietary data that has never before been cleared to move. Enterprises are opening to the idea of producing alternative revenue streams from monetizing proprietary data pipelines for AI use cases.
But real-world data is difficult to use in its raw form. It contains sensitive information, contractual obligations, and regulatory constraints that make traditional approaches to data sharing slow, expensive, and risky. Masking and synthetic generation are now largely commoditized and available to any modern data stack. What is scarce and in high demand today is the privacy engineering expertise to apply the right processing methods surgically without stripping data utility, and independent assessment of residual risk, which a buyer cannot produce itself.
Integral was built on a simple belief: privacy and utility are not opposing forces. They can be solved simultaneously through privacy engineering.
As AI adoption accelerates, enterprise buyers, government contractors, and downstream commercial partners are increasingly asking the same questions: How was this data acquired? What risks remain? Who assessed it? Teams that can answer those questions clearly are unlocking bespoke datasets, moving faster through procurement, and building more defensible businesses.
Integral is the independent privacy layer that makes those answers possible.
"We spent the last four years solving this problem in healthcare, one of the most regulated data environments in the world," said Shubh Sinha, CEO and co-founder of Integral. "What we learned is that the hard problem was never access to the data. The hard problem was preserving the signal that makes data valuable while mitigating the privacy, regulatory, and business risks that prevent it from being used. That challenge now exists across every industry as real-world data becomes the next frontier for AI. This financing allows us to bring what we've built to the broader real-world data economy."
Integral's Forward Deployed Privacy Services embeds a dedicated team of statisticians, privacy engineers, software engineers, and methodologists directly into customer data pipelines. The program operates across two core functions.
First, Integral performs entity-preserving remediation and transformation that reduces re-identification risk while maintaining the longitudinal relationships, rare cohorts, and behavioral signals required for AI applications. Second, Integral independently measures privacy risk in the context of the specific dataset, intended use case, and recipient. These assessments are continuously re-evaluated as data pipelines evolve rather than performed as periodic, static reviews. The assessment produces documentation matched to the framework. Where HIPAA applies, that is an Expert Determination under §164.514(b)(1) signed by a qualified statistical expert. In other contexts, it is a signed defensibility opinion or another instrument that the situation requires.
The output is defensibility, high-quality data, and speed.
Integral's methodology is grounded in peer-reviewed statistical disclosure limitation research and was initially validated across healthcare, life sciences, and health data marketplaces. Today, the company supports AI labs, data platforms, and vertical AI builders operating in highly regulated environments.
A leading pharmaceutical organization recently used Integral's Forward Deployed Privacy Services to unlock a multi-source training dataset that had previously been considered too risky to use, embedding privacy engineering directly into dataset assembly rather than attempting to retrofit privacy controls at final review.
The new financing will support expansion of Integral's privacy engineering and statistical methodology teams, continued investment in entity-preserving linkage and continuous risk assessment infrastructure, and go-to-market expansion across AI labs, data and annotation platforms, and enterprises participating in the real-world data economy.
About Integral Privacy Technologies
Integral is the independent privacy layer for the real-world data economy. The company embeds directly into data pipelines to make sensitive real-world data usable for AI while independently assessing re-identification risk under peer-reviewed methodology. Integral produces the defensibility artifacts required for enterprise, regulatory, and commercial use of real-world data. The company's Forward Deployed Privacy Services support AI labs, data platforms, enterprises, and data originators across healthcare, life sciences, and other regulated industries.
Signal preserved. Risk assessed. Built to be examined.
Learn more at useintegral.com
Contacts
Media Contact
Kelsey Thomas
Head of Marketing
Integral Privacy Technologies
kelsey.thomas@useintegral.com
