Daniel’s journey into startups began at an early age. Born in the UK and later moving to Australia, he was exposed to technology through his father, who worked in IT. While still in school, Daniel started tinkering with software, first automating repetitive tasks in video games, then moving on to building apps that quickly went viral. One of his early projects, a background music app, reached a million users in nine months,while another app tied to Pokémon Go scaled to six million users.

Despite his early success, Daniel initially saw building products as a hobby rather than a career. It wasn’t until he joined an accelerator program in Sydney that he realized the potential of turning his passion into a full-fledged business. He and his high school friend, Jackie, had worked together on multiple projects, and their complementary skill sets—Daniel in product and engineering, Jackie in AI and machine learning—laid the foundation for Relevance AI.

The initial idea behind Relevance AI was to leverage machine learning to automate business processes. Early on, Daniel and Jackie identified inefficiencies in traditional workflows—tasks that required human intervention but were ripe for automation. Their first challenge was building infrastructure that could support AI-driven automation. While vector embeddings and machine learning models existed, there was little infrastructure to apply them effectively. Rather than waiting for the market to mature, they built their own AI-powered automation system.

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However, the road to success wasn’t linear. Like many startups, Relevance AI had to pivot multiple times. Initially focused on machine learning infrastructure, they quickly realized that companies needed end-to-end automation, not just AI tools. Their big breakthrough came with the rise of transformer models like GPT-3.5, which enabled more sophisticated automation. This shift reinforced their belief that AI should move beyond co-pilots (which assist humans) to full automation—what Daniel describes as “autopilot for business.”

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Scaling Relevance AI meant making critical decisions on fundraising and hiring. Unlike many startups that rush to raise capital, Daniel and Jackie initially bootstrapped, wanting to refine their product before bringing in investors. When they did raise funding, they focused on investors aligned with their long-term vision. They also kept the team lean,hiring only when absolutely necessary to maintain speed and agility.

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Daniel emphasizes that a strong co-founderrelationship is crucial for startup success. He and Jackie’s ability to challenge each other, split responsibilities, and remain aligned on vision has been a key factor in Relevance AI’s growth. He also highlights the importance of adaptability—being willing to pivot while staying true to the broader mission.

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Looking ahead, Daniel sees AI-driven automation as the future of work. While co-pilots (like ChatGPT) improve productivity, the real breakthrough will come when AI can execute tasks autonomously. Relevance AI is positioning itself as the go-to platform for businesses looking to deploy AI agents at scale, reducing reliance on human labor for repetitive tasks.

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Daniel’s advice to founders: Have a clear vision, stay resilient, and surround yourself with the right people. Success in AI isn’t just about technology—it’s about execution, adaptability, and building the right team to turn an idea into reality.