Pramaana Labs

Pramaana Labs: A Compiler for Verifiable AI

Pramaana Labs builds a verification "compiler" for mission-critical AI. It translates complex domain knowledge — tax, legal, healthcare, software, and policy rules — into machine-checkable logic, so that an AI system's answer can be proven against the rules rather than merely sounding right. The output is an auditable artifact that shows which rules, assumptions, and constraints were applied to reach a conclusion.

In domains where a wrong answer carries real liability, Pramaana's approach aims to replace "trust the model" with "check the proof."

Key Features

• Domain Formalizer that turns regulatory text into formal logic
• Machine-checkable proof artifacts
• Visibility into applied rules, assumptions, and constraints
• Formal primitives: type system, axioms, operators, boundaries
• Focus on high-stakes, regulated domains

Ideal Use Case

Pramaana Labs fits teams deploying AI in regulated, high-liability areas — tax, legal, healthcare, and policy — where outputs must be defensible and auditable. It suits organizations that cannot accept plausible-but-unverifiable AI answers.

How Pramaana Labs differentiates

Pramaana focuses on formal, machine-checkable verification of AI outputs against codified domain rules, rather than confidence scores or after-the-fact review. It raised $27M, with backers including Pushmeet Kohli of Google DeepMind and Sriram Rajamani of Microsoft Research.

FAQ

What is Pramaana Labs? A verification compiler that turns domain rules into machine-checkable logic so AI can produce provable answers.

What problem does it solve? It makes AI outputs in high-stakes domains auditable and provable rather than just plausible.

Which domains? Tax, legal, healthcare, software, and policy.

Who backs Pramaana Labs? A $27M round with backers including Pushmeet Kohli (DeepMind) and Sriram Rajamani (Microsoft Research).

tl;dr

Pramaana Labs builds a verification compiler that turns domain rules into machine-checkable logic for provable, auditable AI in high-stakes fields, backed by a $27M round with DeepMind and Microsoft Research leaders.