Founders

From Machinio to $50 Million in Revenue: How Dan Pinto Built Fingerprint for the AI Fraud Era

Fingerprint co-founder and CEO Dan Pinto has transformed an open-source device-identification project into a fraud-prevention business reporting more than $50 million in annual recurring revenue. As AI agents reshape online activity, the company’s next challenge is distinguishing legitimate automation from digital threats.

By Jasmin Watson | NOV 9, 2026 | 6 min read | Last updated NOV 14, 2026

Dan Pinto, CEO and co-founder of Fingerprint, a device intelligence and fraud prevention company. — Dan Pinto, co-founder and CEO of Fingerprint, a device-intelligence company specializing in fraud prevention and digital identity. Photo: Fingerprint

For Dan Pinto, building a technology company has increasingly meant solving a fundamental problem of the digital economy: determining whether an online visitor can be trusted.

The entrepreneur, who previously helped build and sell industrial equipment marketplace Machinio, now leads Fingerprint, a device-intelligence company serving financial institutions, e-commerce businesses and technology platforms.

In September 2026, Fingerprint announced that it had surpassed $50 million in annual recurring revenue, marking a significant commercial milestone for a business operating at the intersection of cybersecurity, digital identity and artificial intelligence.

The milestone also raises a larger question for investors and enterprise executives: can device intelligence become an essential layer of internet infrastructure as autonomous AI agents increasingly interact with websites and financial systems?

From Machinio to a New Digital Identity Business

Pinto’s entrepreneurial career began well before Fingerprint.

In 2014, he co-founded Machinio, a specialized search engine connecting buyers and sellers of used industrial machinery. As co-founder and chief technology officer, he helped develop a marketplace serving a fragmented industry where buyers often struggled to locate equipment across numerous suppliers.

Machinio was acquired by publicly traded Liquidity Services in 2018, providing Pinto with experience in building and exiting a technology business.

His subsequent venture addressed a different market: helping companies recognize devices and detect suspicious online activity.

Fingerprint grew out of an open-source browser-identification project into a commercial software platform designed to provide consistent device intelligence across online interactions.

The underlying business opportunity was straightforward. Passwords, email addresses and conventional security checks do not always reveal whether multiple accounts originate from the same device or whether an apparently legitimate visitor is involved in coordinated fraudulent activity.

Fingerprint sought to address that visibility gap.

Fingerprint Surpasses $50 Million in Annual Recurring Revenue

Fingerprint’s September 2026 announcement provided a clearer picture of its commercial development.

According to the company, annual recurring revenue exceeded $50 million following nearly 200% revenue growth over the preceding two years. During that period, its customer base expanded by 93%, while its geographic presence reached 56 countries.

The company reported serving more than 6,000 organizations, identifying over one billion unique devices monthly and processing more than 80 million real-time API events daily.

These figures are company-reported and should not be interpreted as independently audited financial results.

Earlier, in February 2026, Fingerprint disclosed 65% annual recurring revenue growth, 36% customer growth and 128% net revenue retention.

The retention figure is particularly relevant to investors. Net revenue retention above 100% generally indicates that revenue expansion among existing customers exceeds the combined effect of contraction and customer losses.

However, retention alone does not establish profitability, cash generation or the company’s overall financial health. Fingerprint has not publicly disclosed a complete audited income statement in the sources reviewed.

The company has also raised approximately $77 million in disclosed funding, including a $33 million Series C announced in October 2023. Its investors include Nexus Venture Partners, Craft Ventures and Uncorrelated Ventures.

The combination of recurring revenue, enterprise adoption and institutional financing illustrates Fingerprint’s transition from an engineering-led product into a commercially scaled software business.

FOUNDERS NEWSWIRE There's More to This Story Continue exploring the full article below. Continue Reading

How Fingerprint Makes Money—and Why Scale Matters

Fingerprint sells device-intelligence capabilities through software subscriptions and usage-based API access.

Its technology analyzes multiple technical signals to help businesses recognize returning devices, detect suspicious activity and support fraud-prevention decisions.

Potential applications include identifying fraudulent account registrations, detecting account takeovers, limiting payment abuse and recognizing coordinated automated activity.

Customers can integrate these signals into existing security and risk-management systems rather than replacing their entire fraud-prevention infrastructure.

The commercial significance lies in repeated usage. A customer deploying device intelligence across account creation, login, checkout and other interactions may generate additional API activity as its business grows.

That creates opportunities for expansion within existing enterprise accounts, although revenue growth also depends on pricing, customer retention, competition and usage patterns.

For investors, the relevant questions extend beyond headline revenue. Customer concentration, gross margins, acquisition costs, infrastructure expenses and long-term retention will ultimately influence the durability of the business.

AI Agents Create a New Challenge for Digital Trust

The rapid adoption of autonomous AI agents is changing the assumptions behind traditional online security.

Historically, businesses often tried to distinguish human visitors from bots. That distinction is becoming less useful as legitimate software agents begin conducting research, accessing services and completing authorized tasks for individuals and organizations.

A company may need to distinguish between a malicious automated attack and a properly authorized AI assistant performing a transaction.

Fingerprint has responded by developing capabilities intended to identify authorized AI agents, detect automated assistants and provide more detailed intelligence about suspicious traffic.

Its September announcement described Authorized AI Agent Detection alongside AI Assistant Detection and an Automation Intelligence API preview.

The company says these technologies help businesses recognize automated traffic while reducing unnecessary obstacles for legitimate users.

However, identifying an AI agent is not equivalent to proving that every action it performs is authorized. A verified agent may still operate outside its intended permissions, while sophisticated malicious automation may attempt to imitate legitimate behavior.

This creates a broader market opportunity for identity verification, authorization systems and behavioral risk analysis.

Other companies are pursuing related infrastructure. As Founders Newswire reported in its coverage of Baselayer’s $35 million Series A financing for AI-agent identity infrastructure, financial institutions are increasingly examining how to establish which automated systems are authorized to act on behalf of businesses.

The two approaches address complementary aspects of digital trust: recognizing suspicious devices and automation, and establishing the identity and authority of agents conducting transactions.

The Competitive and Privacy Challenges Ahead

Fingerprint’s growth does not eliminate the commercial and regulatory challenges facing device-intelligence providers.

The market includes established fraud-prevention companies, identity-verification platforms, bot-management specialists and cybersecurity vendors developing overlapping capabilities.

Enterprise customers must evaluate not only detection accuracy but also integration complexity, false positives, operating costs and the consequences of incorrectly blocking legitimate users.

Privacy presents another important consideration.

Device identification can help businesses recognize fraud, but it also raises questions about transparency, data collection and the potential use of persistent identifiers for tracking.

Organizations deploying such technology must assess applicable privacy requirements, data governance practices and whether their security objectives justify the information being collected.

These considerations become more complex when services operate across multiple jurisdictions.

For Fingerprint, long-term success will depend on demonstrating that its technology can reduce measurable fraud losses without creating excessive friction or undermining user trust.

The company says its technology has helped customers prevent more than $1 billion in fraud losses annually. That figure represents a company-reported estimate rather than an independently verified financial outcome.

What Dan Pinto’s Next Chapter Means for Investors

Pinto’s entrepreneurial journey reflects a shift from building a specialized online marketplace to developing infrastructure used across digital businesses.

Machinio addressed inefficiencies in finding industrial equipment. Fingerprint addresses a different form of uncertainty: whether digital interactions originate from trustworthy sources.

Its reported $50 million recurring-revenue milestone suggests meaningful commercial demand for that capability.

Yet the next stage may be more demanding than the first.

As AI agents become increasingly capable of browsing, communicating and transacting independently, businesses will need security systems that evaluate identity, authorization and behavior together.

Fingerprint has an established customer base, recurring-revenue model and growing range of products with which to pursue that opportunity.

Whether it can convert those advantages into sustained growth will depend on its ability to remain technically effective, commercially competitive and trusted by enterprise customers.

For founders and investors, the larger lesson is that some of the most consequential AI businesses may not be the companies building the agents themselves, but those developing the infrastructure required to make autonomous digital activity secure and commercially viable.