-- VeraData Holdings today announced a 20-year milestone in applying machine learning to nonprofit donor acquisition and retention, a capability the company has operated since its founding in 2007. The announcement comes as major fundraising platforms rush to embed artificial intelligence into their products and sector research shows organizations integrating AI into fundraising are reporting 20 to 30 percent increases in donations through improved targeting and personalized outreach.
Michael Peterman built VeraData on that exact premise in 2007.
"We have been doing this for nearly two decades," said Peterman, Founder and CEO of VeraData Holdings. "The tools have different names now. The underlying discipline is the same one we built this company around from day one. Data is the asset. The model is the engine. And the nonprofit sector has always deserved access to both."
What Peterman recognized at VeraData's founding was a structural inequity that most of the business world had never considered: the same AI and machine learning capabilities that Fortune 500 companies deploy to identify their best customers, predict purchase behavior, and optimize acquisition costs had no equivalent in the nonprofit sector. Organizations raising money for humanitarian aid, medical research, veteran services, and animal welfare were making donor acquisition decisions the way commercial marketers did in the 1980s, relying on basic demographic segmentation, historical response rates, and instinct.
VeraData was built to close that gap entirely.
What 20 Years of Sector-Specific Data Actually Means
The distinction Peterman draws between VeraData and the wave of AI tools now entering the nonprofit market is not about technology. It is about data. General-purpose AI models trained on commercial consumer behavior do not translate cleanly to charitable giving. A person who buys premium consumer goods does not necessarily have a higher probability of making a major gift to a humanitarian organization. The behavioral signals that predict philanthropic giving are sector-specific, accumulated over years of campaign history, and require models trained on nonprofit data to interpret correctly.
VeraData's proprietary database and predictive modeling infrastructure has been built on nearly two decades of nonprofit-exclusive campaign performance. The company's team of more than 25 PhD mathematicians does not work on general-purpose AI problems. They work exclusively on the mathematics of donor acquisition, lapse reactivation, and major donor pipeline development for mission-driven organizations.
That depth of specialization is what produces the outcomes VeraData puts behind its performance-based pricing model: routinely doubling or tripling an organization's active donor pool while reducing the cost of acquiring donors by 20 to 30 percent. Those are not projections derived from sector averages. They are outcomes the company is willing to guarantee, charging clients only when results are delivered.
"The organizations that have been using data science in fundraising for years already have a compounding advantage over the ones just starting now," Peterman says. "Every campaign makes the model smarter. Every donor interaction adds signal. The flywheel is real, and it takes time to build. The nonprofits that started late will spend years closing the gap."
Three Problems AI Actually Solves in Fundraising
Peterman is precise about where AI delivers genuine value in nonprofit fundraising, as opposed to where it generates activity without outcomes.
Prospecting is the most obvious application and the one where VeraData's two-decade data advantage is most pronounced. Identifying which individuals outside an organization's current donor base have the highest statistical probability of making an initial gift, given their behavioral, demographic, and transactional characteristics, is a problem that machine learning solves with far greater precision than any manual segmentation approach. The difference in acquisition cost between a well-modeled prospecting campaign and a broadly targeted one compounds across the full donor file over time.
Lapse reactivation is the most underutilized opportunity in the sector. Most organizations treat lapsed donors as a homogeneous group and apply the same reactivation messaging uniformly. Behavioral modeling reveals that lapsed donors have widely varying reactivation probability based on how long they have been lapsed, what their giving history looked like, and what external signals correlate with renewed giving intent. Targeting reactivation spend on the highest-probability segment rather than the broadest one dramatically changes the economics of the program.
Major donor pipeline development is where AI creates the most long-term value and where most organizations remain almost entirely data-blind. Identifying mid-level donors who have the behavioral and demographic characteristics of eventual major donors, years before they make a transformational gift, allows organizations to invest in those relationships at the right time rather than discovering the opportunity after it has already matured.
"The data exists to answer all three of these questions with precision," Peterman says. "The organizations that build the infrastructure to use it well right now are going to look very different in five years from the ones that don't."
The Integration Advantage
What separates VeraData's AI platform from the point solutions entering the market in 2026 is that the data science does not operate in isolation. Through The VeraData Group, which includes Faircom New York, Teal Media, and Avalon Consulting, the predictive modeling outputs feed directly into creative strategy, channel selection, production, and campaign execution under a single accountable structure.
That integration matters because the most common failure mode in data-driven fundraising is not a bad model. It is a good model handed to a creative or production partner who has no visibility into what the data says and no incentive to act on it. When the entire chain from insight to outcome sits inside one enterprise, accountability is no longer diffuse and the gap between what the data recommends and what actually gets executed closes to near zero.
VeraData stands alone in the nonprofit sector with both SOC2 and HIPAA compliance, ensuring unparalleled data safety and integrity for clients across every vertical the company serves, including healthcare-adjacent and social services organizations that handle sensitive constituent data as a matter of course.
VeraData now serves more than 400 nonprofit organizations nationwide that collectively raise more than $1 billion in annual philanthropic giving. Peterman was named EY US Entrepreneur of the Year 2026 Florida Award winner and advances to the national competition at the EY Strategic Growth Forum in November 2026, where high-growth CEOs, Fortune 1000 executives, and investors converge to recognize the country's most consequential company builders.
About VeraData
VeraData is a data analytics company headquartered in Sarasota, Florida, specializing in AI and machine learning-enabled donor acquisition and retention for the nonprofit sector. Founded in 2007 by Michael Peterman, VeraData serves more than 400 nonprofit organizations nationwide and supports clients that collectively raise more than $1 billion in annual philanthropic giving. VeraData operates as The Donor Science Company. For more information, visit veradata.com.
About Michael Peterman
Michael Peterman is the Founder and CEO of VeraData and the originator of the Donor Science framework. He is the EY US Entrepreneur of the Year 2026 Florida Award winner and will compete at the national EY Entrepreneur of the Year program in November 2026. Peterman is based in Sarasota, Florida.
Contact Info:
Name: J. Farque
Email: Send Email
Organization: VeraData
Website: http://www.veradata.com
Release ID: 89203020
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