Struct4Search

Search meaning,
not just words

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.

You hold tens of thousands of documents —
so why is the right one so hard to find when it matters?

Knowledge locked in documents

Real knowledge — tables, drawings, results — is trapped in unstructured files and never even reaches search.

Data you can’t send out

Security blocks external AI,
leaving you stuck with weaker alternatives.

Weak in-house RAG

Text-only scraping drops tables and drawings entirely, so it ends up unused.

Knowledge buried in documents,
usable with a single question

  1. 01

    Split into source units

    We parse PDFs and internal documents to extract body text, tables, and headings,
    then split them into searchable, citable source units.

  2. 02

    Expand into a knowledge graph

    We extract entities and relations to build a knowledge graph,
    combining 18 metadata types to generate search expressions that reach the source.

  3. 03

    Return evidence and sources

    We search by keyword and semantic vector at once to select source passages,
    and return document and page sources alongside the answer.

Core strengths that go
beyond keyword suggestions

Multimodal document parsing and a knowledge graph surface knowledge that search never reached.

Search coverage

Multimodal parsing structures text, tables, graphs, and drawings
and reflects them all in search.

Reasoned search over meaning, relations, conditions

Grounded in metadata and the knowledge graph, it performs reasoning-based search
that reflects meaning, relations, and conditions.

Down to domain terminology

Built on domain-adapted NER and the knowledge graph,
it expands technical terms and synonyms in search.

Self-evolving search

A hypothetical-query self-improvement mechanism
automatically advances search quality.

The more you compare, the clearer it gets.

Search scope, security, cost — Struct4Search leads on every measure.

Cloud AIIn-house RAGStruct4Search
On-premisesExport requiredOO
Non-text searchPartialNo (tables/drawings dropped)Full table/drawing/graph support
Terminology & acronymsGenericXAutomatic domain learning
Answer evidence & sourcesHard to verifyUnclear or generated citationsSource-only citations, doc/page links
Domain auto-adaptationLimitedManual tuning neededSelf-improving mechanism

AI that knows structure, relations, and context. Meet Paramita — the AI that truly knows your knowledge.