In this episode of The Vertical SaaS Podcast, Gavin Savage sits down with Nitish Mutha, Co-founder and CTO of Genie AI, to unpack how the company is building the next generation of legal infrastructure, a true agentic legal stack that combines retrieval, reasoning, and trust.
Nitish shares his journey from engineer to founder, and how Genie evolved from a two-person experiment in Barclays Ventures’ Eagle Labs to one of the UK’s most ambitious AI startups. He discusses how LLMs transformed Genie’s speed of innovation, why retrieval and context are core to reliability, and how the team defines real-world ROI in a market crowded with hype.
The conversation dives deep into product-led growth, defensibility in vertical AI, hiring for adaptability, and how Genie is creating the foundation for a world where AI handles not just contracts but legal reasoning itself.
`Takeaways
- Nitish’s engineering background shaped Genie’s product philosophy and technical depth.
- Genie AI evolved from serving lawyers to empowering business users and teams.
- Retrieval, context, and reliability are at the heart of Genie’s legal reasoning engine.
- The rise of LLMs unlocked Genie’s product velocity and scale.
- The company’s 7-year R&D head start led to a £13.3M Series A from GV and Khosla.
- Product-led growth remains key to sustained adoption in legal AI.
- Hiring centers on curiosity, adaptability, and ownership.
- Trust, defensibility, and measurable ROI define success in legal AI.
- The legal industry is shifting from manual workflows to intelligent systems.
- Genie AI’s long-term vision: the core legal stack for modern businesses.
Chapters
00:00 – Introduction to Genie AI and Nitish’s background
02:39 – Early challenges and the first product iterations
05:24 – Shifting focus from lawyers to business users
08:20 – The impact of LLMs on product speed and accuracy
11:26 – Finding early market validation
14:02 – The funding journey: from bootstrap to GV and Khosla
17:08 – Product-led growth and differentiation in legal AI
20:08 – Driving adoption and defining ROI
22:50 – Advice for building vertical AI products
24:11 – Reliability, hallucinations, and real-world guardrails
29:07 – Ensuring quality and trust in AI systems
31:30 – Inside Genie’s technical stack: retrieval and reasoning
38:33 – Scaling post-LLM: product, team, and culture
43:26 – Hiring and leadership in an AI-first startup
46:25 – The future of agentic legal and Genie’s long-term vision
Keywords
Genie.ai, Legal Tech, Artificial Intelligence, LLMs, Agentic AI, Contract Management, Startup Funding, Product-Led Growth, Hiring Strategies, Legal Automation, Technology Innovation
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