Bridging LLM Flexibility with Deterministic Logic

To address the reliability gap in enterprise AI, Pramaana Labs is building a system that pairs the generative capabilities of Large Language Models (LLMs) with a deterministic verification layer. While LLMs excel at natural language processing and complex reasoning, they are prone to hallucinations, which makes them unsuitable for high-stakes environments where errors carry significant legal, financial, or health consequences. Pramaana’s architecture uses the LLM as an engine for problem-solving while offloading the final output verification to a formal system.

Applying Formal Verification via LEAN

The core of Pramaana’s approach is the use of formal verification, a method typically used to mathematically prove the correctness of software or hardware. Specifically, the company leverages the open-source LEAN programming language to codify domain-specific rules. By creating a "codified version" of complex rule sets—such as tax codes or regulatory requirements—the system can mathematically verify that the AI's reasoning adheres to established constraints. This mirrors the methodology of projects like France’s CATALA, which formalizes legislative text into executable, verifiable code.

Domain-Specific Implementation

Pramaana is focusing on highly sensitive verticals where accuracy is non-negotiable. To ensure the integrity of their verification models, they are collaborating with domain experts to oversee the formalization process:

• Tax Law: Working with former IRS commissioner Danny Werfel.
• Cybersecurity & Drug Discovery: Overseeing by professors from IIT Delhi, IIT Madras, and UC Berkeley.

By formalizing the rules of these domains, Pramaana aims to transform "unsolvable" enterprise problems into deterministic, verifiable workflows.