From Shazam to Cognigy: Nenad Marovac on 20 Years of AI Investing Before the Hype

The artificial intelligence hype cycle since 2022 has produced a familiar pattern: a flood of capital into anything labelled "AI," followed by the inevitable reckoning as investors distinguish substance from speculation.

For Nenad Marovac and DN Capital, this cycle is not new. The firm has been investing in artificial intelligence for two decades, long before large language models captured the public imagination. That long-term perspective has informed an investment thesis that Marovac calls "applied AI": backing companies that use artificial intelligence to solve real problems, rather than companies selling AI as a product in itself.

Shazam: AI Before It Was a Buzzword

DN Capital's AI investing history begins most notably in 2004 with Shazam, the audio recognition application that would become one of the most downloaded mobile apps in history.

At the time, few would have described Shazam as an "AI company." The technology, which could identify songs from brief audio samples captured through a mobile phone, relied on sophisticated audio fingerprinting and pattern-matching algorithms. Today, however, we would recognise these as machine learning techniques.

DN Capital invested at Series A and remained with Shazam through a turbulent journey that included more than five CEOs and five CTOs over fourteen years. The company faced multiple near-death experiences, but the underlying technology proved transformative. In 2018, Apple acquired Shazam for US$400m. The application has since been installed more than one billion times.

"When we invested in Shazam in 2004, nobody called it an AI company. But the technology, taking audio input and using algorithms to identify patterns to match to songs, was exactly what we now call applied AI. We've been investing in this space for twenty years."
Nenad Marovac, DN Capital

Endeca: Enterprise Intelligence Before Big Data

DN Capital's 2000 investment in Endeca represents another early bet on what we would now call enterprise AI.

Endeca developed search and data analytics technology for enterprise applications, helping large companies make sense of complex, unstructured data. The technology-powered search and navigation for major e-commerce platforms and enterprise applications.

The investment came just before the dot-com crash, and Endeca faced severe market headwinds in its early years. DN Capital stayed with the company through the downturn, helping it restructure and find its market. In 2011, Oracle acquired Endeca for more than US$1bn. The underlying technology still forms the basis for Oracle's e-commerce and enterprise search solutions today.

The Applied AI Thesis: Technology as Enabler, Not Product

DN Capital's long experience with AI investments has shaped a distinctive philosophy. Marovac doesn't look to invest in companies that position "AI" as their primary product. Instead, he looks for companies that use AI to dramatically improve the delivery of a specific product or service.

The distinction matters. Companies selling "AI" as a product must compete with rapidly improving foundation models and face the risk of commoditisation. Companies using AI to solve specific problems in specific industries can build defensible positions through domain expertise, proprietary data, and customer relationships—and in the enterprise space, this expertise and deep understanding it critical for success.

"We're not looking for companies selling AI. We're looking for companies using AI to solve real problems better than anyone else. The AI is the enabler, not the product."
Nenad Marovac, DN Capital

Cognigy: Europe's Largest AI Exit

The 2025 acquisition of Cognigy by NiCE for US$955m validated DN Capital's applied AI thesis in dramatic fashion.

DN Capital led Cognigy's Series A in 2019, years before ChatGPT and the large language model boom. The Düsseldorf-based company had developed an agentic AI platform for enterprise customer service, allowing businesses to automate complex customer interactions while maintaining quality and compliance.

The timing was significant. By investing before the AI hype cycle, DN Capital helped Cognigy build its market position without the valuation inflation that would later affect AI investments. When the exit came, it was Europe's largest AI acquisition, a validation of both Cognigy's technology and DN Capital's early conviction.

The Current Applied AI Portfolio

DN Capital's applied AI thesis has produced a portfolio of companies using artificial intelligence to transform specific industries.

Incode, a DN Capital portfolio company, uses AI for biometric identity verification. The company has become a unicorn by helping enterprises verify customer identities through facial recognition and document analysis—technology with applications from banking to border security.
Hawk, a Munich-based company backed by DN Capital, applies machine learning to anti-money laundering and fraud detection. The company's AI-powered platform catches financial crimes at seventeen times the industry average rate, a dramatic improvement over traditional rule-based systems.
Sanas has developed a foundational speech-to-speech AI model that allows call centre agents to modify their accents in real time, improving understanding and customer satisfaction in cross-border service operations.
Unique, which raised a 30-million-dollar Series A led by DN Capital in 2025, is building what the firm calls "the agentic workforce for financial services"—AI agents that can handle complex processes like KYC, due diligence, and compliance.

What Marovac Looks For in AI Investments

Two decades of AI investing have given Marovac clear criteria for evaluating opportunities in the space:

First, he looks for companies with real enterprise customers paying meaningful prices. Early revenue from sophisticated buyers validates that the technology solves a genuine problem.
Second, he evaluates the defensibility of the company's position. This usually comes from proprietary data, deep domain expertise, or customer relationships that would be difficult for competitors to replicate.
Third, he assesses the founding team's background. The most successful applied AI companies are typically built by founders with deep expertise in the industry they're serving, not just technical AI skills.
Fourth, he considers timing. The AI market moves quickly, and companies that are too early or too late often fail regardless of their technical quality.

2026 and Beyond: Data Integrity and AI Governance

Looking ahead, Marovac sees significant opportunities in what DN Capital calls "data integrity management": technology that helps organisations ensure the quality, security, and compliance of the data that feeds AI systems.

As AI becomes embedded in critical business processes, organisations will need tools to verify data provenance, detect manipulation, and ensure compliance with emerging AI regulations. This infrastructure layer, less glamorous than consumer-facing AI applications but potentially more valuable, represents a major investment theme for DN Capital.

AI governance is a related opportunity. As regulators in Europe, the United States, and elsewhere develop frameworks for AI oversight, companies that help enterprises navigate compliance requirements will be well-positioned.

"The current AI hype will fade, but the underlying technology will transform every industry. The investors who succeed will be those who can separate genuine applied AI opportunities from the noise."
Nenad Marovac, DN Capital

For investors seeking exposure to artificial intelligence, DN Capital's track record offers a blueprint: focus on companies using AI to solve real problems, invest at sensible valuations, and maintain the patience to see investments through multiple market cycles.