Pramaana Labs has raised $27 million in seed funding in a round led by Khosla Ventures, as it develops AI systems designed to make answers more reliable in high-stakes sectors.
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The round also saw participation from Accel, BoldCap, Nexus Venture Partners, Premji Invest and Unbound. Early backers include Pushmeet Kohli, vice-president at Google DeepMind, and Sriram Rajamani, corporate vice-president at Microsoft Research.
Founded in 2025 by Ranjan Rajagopalan, Krishnan Raghavan and Sanjay Ganapathy Subramaniam, Pramaana is building what it describes as a verification layer for AI. The startup said its technology converts complex human knowledge into machine-checkable logic, allowing claims made by AI systems to be traced, tested and proved.
It will use the raised capital to train formalisation and prover models, hire AI researchers and expand its network of domain experts across regulated areas including tax, medical diagnosis, cybersecurity and financial compliance.
Pramaana’s approach combines large language models with formal verification, a branch of computer science used to check whether systems behave as intended.
It converts the rules of a domain, such as tax codes, clinical guidelines, legal rules and safety constraints, into machine-checkable logic that machines can reason over.
That formal layer is designed to sit above a conventional large language model, giving the system the ability to answer natural language questions while checking whether its conclusions follow the rules of the relevant domain.
“It’s like math in the sense that you have a lot of rules that you need to abide by. Once you have a codified version of it, the reasoning on top of it starts becoming deterministic,” Ranjan Rajagopalan said, referring to the tax code.
Pramaana is drawing on tools including Lean, an open-source proof assistant used to verify mathematical statements and software.
Its approach could help reduce hallucinations and make AI outputs more accountable in areas where errors can have serious consequences.
Pramaana is entering a growing field of companies working on formal reasoning, AI verification and machine-checkable systems, as enterprises look for ways to move AI beyond pilot projects and into sensitive business operations.