How Pramaana Labs is Building a Compiler for Mission-Critical AI

Pramaana Labs, building the verification layer of AI for accelerated trust and super-intelligence, led by Ranjan Rajagopalan, Krishnan Raghavan, and Sanjay Ganapathy, has raised $27 million in seed funding led by Khosla Ventures, with participation from Accel, Boldcap, Nexus Venture Partners, Premji Invest, and Unbound.

Closing AI's Trust Gap.

AI can generate answers, but in high-stakes domains such as tax, law, healthcare, finance, and governance, accuracy alone is not enough. The ability to verify, justify, and prove why an answer is correct remains a critical challenge.

This creates a fundamental trust and accountability gap between AI-generated outputs and real-world decision-making. Pramaana was founded to address that challenge, building systems designed not only to produce answers, but also to provide the evidence, reasoning, and verifiability required for decisions where correctness matters most.

The Accountability Gap in AI.

The first wave of AI was defined by creation and fluency. Advances in transformer-based models enabled systems that can write, code, reason, and converse at unprecedented speed, unlocking major gains in productivity and accelerating the adoption of AI across industries.

Yet fluency is not the same as truth. In high-stakes fields such as tax, law, healthcare, finance, and governance, decisions require more than plausible answers, they require verifiable ones. Today's AI systems can generate convincing outputs, but they often cannot prove why those outputs are correct or assume responsibility when they are not.

This creates a fundamental accountability gap. Human experts remain responsible for reviewing diagnoses, legal briefs, financial filings, and other critical outputs, not because AI lacks speed, but because liability and trust still rest with people. As a result, AI frequently serves as an assistant rather than an autonomous decision-maker in the domains where its impact could be greatest.

Pramaana was founded to address this challenge. The company's core belief is that the next phase of AI will not be defined by generating answers more fluently, but by making those answers provable, trustworthy, and accountable enough for real-world decisions.

From Generation to Verification.

Experience at the frontier of large language models revealed a fundamental limitation: reducing hallucinations is not the same as eliminating them. In high-stakes domains, confidently incorrect answers are not merely product flaws, they represent a deeper research challenge that cannot be solved through incremental improvements alone.

This realization became the foundation for Pramaana. The company's thesis is that AI has reached a pivotal moment similar to other transformative technologies throughout history, where breakthrough ideas must be paired with rigorous proof before they can be trusted at scale. Just as intuition alone was insufficient for scientific and mathematical advances, AI-generated outputs must be verifiable before they can underpin critical decisions.

The question driving Pramaana is therefore not what AI can generate, but what it can reliably prove. By focusing on the structural limitations of current AI systems, the company aims to build the verification layer needed to make AI trustworthy in domains where correctness is non-negotiable.

Verifying AI With Mathematical Certainty.

Pramaana is building a verification layer for AI, applying formal verification techniques to high-stakes domains where correctness is critical. The company's approach is similar to a software compiler: instead of accepting outputs that merely appear correct, the system checks every claim, calculation, or AI-generated answer against the underlying rules that govern it and either verifies the result or identifies precisely where it fails.

To achieve this, Pramaana translates complex regulatory frameworks, policies, and domain-specific rules into formal representations that can be reasoned about mathematically. Questions and AI outputs are then converted into formal statements and evaluated through a proof engine capable of producing machine-verifiable evidence of correctness.

The goal is to move AI beyond plausible answers toward provable ones. By refusing to certify outputs that cannot be verified, Pramaana aims to reduce the need for humans to act solely as review and liability layers, enabling AI systems to operate with a level of trust and accountability that current architectures cannot provide.

Built by Experts in AI Reliability and Verification.

Pramaana was founded by a team of IIT Madras alumni with deep experience building and scaling AI systems at some of the world's leading technology companies. The founders have worked on challenges ranging from maintaining the accuracy of large-scale information systems and combating AI hallucinations to developing frontier AI models and real-world reasoning capabilities.

The broader team brings together researchers and engineers from organizations including Google DeepMind, Meta, Microsoft, Uber, and the University of California, Berkeley.

Their shared belief is that the accountability gap, the inability of AI systems to reliably prove their outputs, is both one of the most significant unsolved challenges in artificial intelligence and one of the largest opportunities for the next generation of AI infrastructure.

Backed by Leaders in AI and Formal Verification.

Pramaana's early supporters include some of the most respected figures in formal verification and AI research. Backers and advisors include Pushmeet Kohli and Sriram Rajamani, both recognized for their contributions to verification, reasoning systems, and advanced AI research.

The company also collaborates with a research network spanning the Indian Institute of Technology Delhi, Indian Institute of Technology Madras, and the University of California, Berkeley. In the tax domain, Pramaana is advised by Danny Werfel, providing expertise on regulatory and compliance challenges where verifiable AI systems could have a significant impact.