Unize AI offers a platform that automatically ingests unstructured text—such as documents, emails, and research notes—and converts it into a searchable knowledge graph.
Funding
Funding not disclosed
Founders
Product
Problem
Organizations often have large volumes of unstructured text—documents, emails, research notes, and web content—that are difficult to search, relate, and reuse effectively. This hampers knowledge sharing, decision‑making, and the development of AI models that require structured inputs.
Solution
Unize AI provides a platform that ingests any amount of textual data and automatically extracts entities, relationships, and contextual information to build a knowledge graph. The system generates a schema directly from the input content, ensuring the graph reflects the domain’s concepts without manual modeling. Built‑in deduplication mechanisms identify and merge duplicate nodes, properties, and relationships, producing a clean, interconnected data set. The resulting graph is searchable and can be exported for downstream analytics, AI training, or integration with existing information systems, enabling teams to turn scattered knowledge into actionable, structured data.
Target Audience
Primary users are knowledge‑intensive teams such as research groups, product development units, and AI/ML engineers who need to organize and leverage large collections of unstructured information.
Features
- Automatic ingestion of arbitrary text volumes and conversion into a unified knowledge graph
- Schema generation that derives entity types and relationships directly from source content
- High‑precision entity and relationship extraction with contextual awareness
- Deduplication of nodes, properties, and edges to maintain graph integrity
- Scalable graph construction supporting small projects to enterprise‑level datasets
- Export options for integration with analytics tools, machine‑learning pipelines, and enterprise databases