Ur AI provides an enterprise-grade, locally deployable AI stack that transforms complex documents into structured, queryable data for high-stakes workflows. Its foundation, Nebula, converts charts, tables, and contracts into layout-preserved Markdown and JSON, enabling auditable, model-agnostic AI applications. The company targets regulated industries requiring security certifications like SOC 2 Type 2 and GDPR compliance.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises struggle to extract reliable, structured knowledge from complex documents like contracts, filings, and charts, which often contain critical business information trapped in unstructured formats. Generic AI models are insufficient for high-stakes workflows because they lack the precision, auditability, and security required for regulated environments, forcing organizations to rely on manual interpretation and error-prone processes.
Solution
Ur AI provides a full-stack, locally deployable AI platform designed to turn enterprise documents into machine-native information systems. The foundation, Nebula, is a document intelligence engine that converts complex files into layout-preserved Markdown and structured JSON, making the content accessible for AI reasoning. This is complemented by an enterprise model layer, evaluation and integration harnesses, and workflow applications like Specter for due diligence. The platform emphasizes ownership and control, allowing enterprises to run fine-tuned LLMs for the majority of their AI workloads while maintaining full auditability and security. By separating document understanding, semantic representation, knowledge access, and reasoning, Ur AI enables dependable autonomy and inspectable information operations.
Target Audience
Primary customers are enterprises in regulated industries such as finance, legal, and construction that handle complex documents and require secure, auditable AI systems for high-stakes decision-making.
Features
- Nebula document intelligence engine that converts charts, tables, contracts, and filings into layout-preserved Markdown and structured JSON
- Locally deployable, enterprise-specific fine-tuned LLM stack designed for roughly 80% of AI workloads
- Multi-path knowledge access supporting exact match, BM25, vector search, SQL, and graph traversal for different query types
- Agent-based control plane that coordinates tools, assembles context, and verifies results within defined authority boundaries
- SOC 2 Type 2, ISO 27001:2022, and GDPR compliance with 75 continuously monitored controls
- Workflow applications like Specter for high-stakes due diligence processes