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1-10
United States , 2025
Delaware
Pramaana Labs is building the proof layer for AI. Today’s AI systems are fast and creative, but they hallucinate and trust is less with decisions involving law, finance, or critical code. Pramaana is solving that by converting AI-generated answers into machine-checkable proofs using formal methods like Lean, Catala, and SMT solvers. Think of it as turning every AI answer into something a computer can prove correct The technology sits between AI output and real-world decisions. When an AI answers a question about U.S. tax law, contract terms, or code translation, Pramaana formalizes the problem, checks it against laws, rules, or logic, and outputs a verified answer. This “Verifier API” plugs into any LLM stack and acts like a calculator of truth, it doesn’t generate text, it proves whether text is correct. Early implementations already work across taxation rules, statutory computation, and mathematical reasoning. The team comes with deep conviction and industry credibility. Founded by IIT Madras alumni who later worked at Google on large-scale AI reliability, tool-use, and reinforcement learning systems, the team is backed by Nexus Venture Partners and other top investors. They believe verification is AI’s final bottleneck. Pramaana’s long-term goal: make the world’s rules, laws, and logic executable so AI can reason over them safely, and humans can trust the outcome.
The team comes with deep conviction and industry credibility. Founded by IIT Madras alumni who later worked at Google on large-scale AI reliability, tool-use, and reinforcement learning systems, the team is backed by Nexus Venture Partners and other top investors. They believe verification is AI’s final bottleneck. Pramaana’s long-term goal: make the world’s rules, laws, and logic executable so AI can reason over them safely, and humans can trust the outcome.
Artificial Intelligence where every word is weighted. Every claim provable.
Europe, India
Maturity
B2B2C, C2C
NA
Are all the founders full-time on this project?
Yes
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