When Daniel Lenton completed his PhD in Machine Learning, he quickly realised that the hardest part of building AI products wasn’t the models, it was the infrastructure around them. That insight led him to found Unify, a platform designed to help teams deploy, scale, and manage AI systems in production.

In this episode, Daniel breaks down the realities of building a deep-tech startup in one of the most competitive fields today, including:

• How Unify was born out of the challenges of deploying ML models in the real world
• The process of validating a technical product with early users
• Lessons from finding product–market fit when your customers are developers and enterprises
• Why balancing speed of execution with reliability is crucial in AI infrastructure
• Fundraising insights in today’s AI-driven market
• The importance of developer communities and open source in building credibility
  

Tune in as Daniel shares the lessons, trade-offs, and insights from taking Unify from research to a venture-backed company, and what he’s learnt about building AI infrastructure for the future.

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