Turakhia Puts $30M Of Own Money Into AI Work Platform Neo
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- Bhavin Turakhia is personally funding Neo with $30 million of his own money, bootstrapping the venture as he has with past companies including Directi, Radix, Titan, and banking software firm Zeta rather than bringing in outside investors.
- Neo launched internally in April as a model-agnostic enterprise work platform combining project management, documents, file storage, and AI into a single product designed from the ground up for the AI era.
- Turakhia argues incumbents face a structural disadvantage retrofitting AI onto pre-generative-AI software, drawing an analogy: "If you want to build an iPhone, you can't take the parts of a Nokia and somehow convert it into an iPhone."
- Neo was built in three months with AI used extensively in development — work Turakhia estimates would have taken more than a year with a much larger pre-generative-AI engineering team.
- The Bengaluru-based startup currently has about 45 employees including 18 engineers and plans to grow to roughly 100 by year-end, with most new hires focused on AI and software engineering.
- Neo is rolling out to mid-sized businesses in the coming months, initially targeting knowledge workers in technology, consulting, and professional services firms; Turakhia says even 2–5% of the global enterprise AI market would be "larger than anything I've built so far."
- Turakhia acknowledges the crowded field, noting that Microsoft, Google, Salesforce, Anthropic, OpenAI, Notion, and Superhuman are all racing to embed AI into workplace software — and points to Chamath Palihapitiya's enterprise AI coding venture 8090, self-funded before raising $135 million this week, as a comparable bet.
Why it matters: In a market where Microsoft, Google, and Salesforce are embedding AI into workplace software, Turakhia is self-funding a $30M AI-native alternative with claims that even 2–5% of global enterprise AI spend would surpass his previous ventures. Neo shipped its initial platform in three months with 18 engineers, setting a concrete benchmark for how leanly an AI-native stack can be built.
