Pramaana Labs announced $27 million in seed funding on Wednesday, June 17, 2026, to build AI systems that use formal mathematical verification to reduce errors in high-stakes industries such as law, drug discovery, and tax preparation.
The round was led by Khosla Ventures, with participation from Accel, BoldCap, Nexus Venture Partners, Premji Invest, and Unbound.
The startup was co-founded by Ranjan Rajagopalan, who serves as CEO. Pramaana’s approach layers a deterministic verification system on top of a conventional large language model (LLM), using the open source LEAN programming language — a tool originally developed to verify mathematical proofs. The company draws on a precedent set by France’s CATALA project, which formalizes the country’s tax and benefit rules into executable code.
For each target domain, Pramaana plans to build its own LEAN-style verification system overseen by subject-matter experts. Former IRS commissioner Danny Werfel is advising on the tax law application, while professors from IIT Delhi, IIT Madras, and UC Berkeley are overseeing cybersecurity and drug discovery work.
Rajagopalan described the appeal of rule-heavy domains like tax law to TechCrunch: “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.”
The company’s focus on sensitive verticals reflects a broader challenge enterprises face when deploying AI: hallucinations and errors that may be tolerable in general applications carry significant consequences in legal, medical, or financial contexts. Pramaana’s model aims to preserve the flexibility of an LLM while adding a verification layer to check its outputs against formalized rules.
“The world’s hardest problems are not unsolvable. They are unformalized,” Rajagopalan said.
Source: TechCrunch