Knowledge, rebuilt for models.
Enterprise knowledge was written for people: pages, chapters, tables, slides, recordings. DataSeq restructures it into the form a model reads natively — semantically complete units, linked into a network, each one traceable to where it came from.
From documents to knowledge units
- Multimodal parsing.Layout analysis, cross-page merging, deep understanding of tables and figures. A table that spans three pages arrives as one table.
- Semantically complete units.Documents, sections, tables, paragraphs, images and video segments become units that stand on their own — each carrying its position in the source.
- Document trees.The logical hierarchy of a document is restored, so a unit is never retrieved without its context.
From units to a knowledge network
Units are linked to the things the business actually talks about — core business objects, business events, knowledge points and reusable knowledge components — forming a network an agent can traverse rather than a pile it must search.
LLM-first knowledge
Knowledge is kept in two complementary forms: explicit — modular, graph-structured, inspectable; and implicit — parameterised into the models that serve it. Production (documents, reports, systems, training material) flows through automatic decomposition into the network; consumption (search, Q&A, recommendation, analysis, creation, decision) assembles a scene-specific sub-graph as context.
produce → decompose → network → assemble → consume
Provenance
Every unit keeps its origin: page and block coordinates for documents, timestamps for audio and video. An answer can always be opened at the exact place it came from.
Where it is used
- Knowledge bases and Q&A over internal documents
- Research and analysis over reports, filings and meeting records
- Grounding for agents built on AgentSeq
Delivery
API, billed per page · Self-hosted licence.