Current generative AI systems produce answers without systematic verification, leading to hidden contradictions, untraceable assumptions, and regulatory risk for users in scientific research, policy analysis, medical safety, and financial modeling. Decision makers lack audit‑ready evidence that AI claims are logically sound, which hampers adoption in high‑stakes environments. Pramaana delivers an AI platform that subjects every generated claim to a formal proof engine before it is emitted.
Funding
$27M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
PIUFounders
Product
Problem
Current generative AI systems produce answers without systematic verification, leading to hidden contradictions, untraceable assumptions, and regulatory risk for users in scientific research, policy analysis, medical safety, and financial modeling. Decision makers lack audit‑ready evidence that AI claims are logically sound, which hampers adoption in high‑stakes environments.
Solution
Pramaana delivers an AI platform that subjects every generated claim to a formal proof engine before it is emitted. The system translates natural‑language inputs into a logical representation, applies automated theorem‑proving and consistency checks, and returns only statements that can be traced to a minimal set of validated premises. By enforcing self‑consistency, minimality, predictive constraints, and full verifiability, the platform produces audit‑ready outputs where each assertion is linked to its underlying assumptions. Results are delivered through a secure web dashboard and a programmable API, enabling seamless integration into existing workflows and downstream analytics. This approach reduces the need for manual fact‑checking, supports regulatory compliance, and builds trust in AI‑driven insights across regulated domains.
Target Audience
Primary customers are regulated enterprises and research organizations in pharmaceuticals, finance, government policy, and clinical safety that require provable, accountable AI insights.
Features
- Formal proof substrate that converts user queries into first‑order logic and runs automated theorem proving to validate each claim.
- Self‑consistency engine that detects and eliminates hidden contradictions across multi‑step reasoning chains.
- Minimality analyzer that isolates the essential assumptions required for a conclusion, discarding extraneous data.
- Predictive constraint generator that derives testable consequences from verified statements for scenario planning.
- Audit‑ready reporting module that attaches provenance metadata and proof traces to every output, exportable in PDF or JSON.
- RESTful API and SDKs (Python, Java) for enterprise integration, supporting batch processing and real‑time inference.
- Domain extensions for scientific inference, policy modeling, medical safety analysis, and financial scenario evaluation, each pre‑tuned with sector‑specific ontologies.