W-Knowledge
Document & knowledge extraction graph.
- →Document parsing
- →Entity extraction
- →Knowledge graph
- →Natural-language Q&A
UI previewW-Knowledge transforms unstructured documents — PDFs, Word files, web pages, research papers — into a structured knowledge graph you can query in natural language. It parses documents, extracts entities and relationships, links them into a traversable graph, and then lets you ask questions and get cited answers grounded in your own corpus. Built for research teams, knowledge management practitioners, and organizations drowning in documents they cannot effectively search.
Document parsing
W-Knowledge handles PDFs, DOCX, HTML, plain text, and scanned images (via OCR) in a single ingestion pipeline. Tables, figures, footnotes, and section hierarchy are preserved in the parsed output so extracted entities carry accurate positional context. Bulk ingest entire document repositories and let the pipeline run asynchronously — results appear in the graph as each document completes.
Entity extraction & relationship mapping
Named entity recognition identifies people, organizations, locations, dates, technical terms, and domain-specific concepts. Relationship extraction then maps how entities relate — "authored by", "subsidiary of", "mentions", "contradicts" — building a rich, traversable graph rather than a flat list of keywords. Custom entity types and relationship schemas let you tailor extraction to your domain.
Knowledge graph
All extracted entities and relationships are stored in a queryable graph database. Traverse the graph to discover connections across documents that would be invisible in a keyword search — find every document that mentions a specific organization in the context of a particular regulation, or surface all authors who have written about a topic and the papers that cite each other.
Natural-language Q&A
Ask questions in plain language and receive answers synthesized from the graph and the underlying document text, with citations linking back to the exact passages that informed the answer. The Q&A layer stays grounded in your corpus — it will not confabulate facts from outside your documents — making it suitable for high-stakes research and compliance use cases.
Use cases
- →Build a searchable knowledge base from thousands of internal policy and procedure documents
- →Extract competitor mentions and product relationships from a corpus of industry reports
- →Answer regulatory compliance questions grounded in your own policy library
- →Map research literature by surfacing entity co-occurrences and citation relationships
- →Onboard new team members by letting them ask questions answered from institutional documentation
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