Knowledge locked in documentsReal knowledge — tables, drawings, results — is trapped in unstructured files and never even reaches search.
Struct4Search
We parse internal PDFs, reports, and design documents into a knowledge graph,
and answer questions with the source passages and pages the answer is based on.

Knowledge locked in documentsReal knowledge — tables, drawings, results — is trapped in unstructured files and never even reaches search.
Data you can’t send outSecurity blocks external AI,
leaving you stuck with weaker alternatives.
Weak in-house RAGText-only scraping drops tables and drawings entirely, so it ends up unused.
We parse PDFs and internal documents to extract body text, tables, and headings,
then split them into searchable, citable source units.
We extract entities and relations to build a knowledge graph,
combining 18 metadata types to generate search expressions that reach the source.
We search by keyword and semantic vector at once to select source passages,
and return document and page sources alongside the answer.
Multimodal document parsing and a knowledge graph surface knowledge that search never reached.
Search coverageMultimodal parsing structures text, tables, graphs, and drawings
and reflects them all in search.
Reasoned search over meaning, relations, conditionsGrounded in metadata and the knowledge graph, it performs reasoning-based search
that reflects meaning, relations, and conditions.
Down to domain terminologyBuilt on domain-adapted NER and the knowledge graph,
it expands technical terms and synonyms in search.
Self-evolving searchA hypothetical-query self-improvement mechanism
automatically advances search quality.
Search scope, security, cost — Struct4Search leads on every measure.