Make unstructured content answerable
Most of what your organisation knows is in documents, and most retrieval projects treat those documents as a pile of text to embed. TopQuadrant tags content against your governed vocabularies and links it to the entities it discusses, so a question about a product, a study, or a counterparty returns the documents that are actually about it.
Trusted to govern the data behind the world's most regulated decisions.
Retrieval is only as good as the vocabulary behind it
Embeddings find text that looks similar. They do not know that two documents refer to the same entity under different names, that one supersedes the other, or that one is restricted. Tagging content against governed concepts gives retrieval something to be precise about, and gives you an audit trail for what the model was allowed to read.
Connectors reaching structured and unstructured sources, from document stores and DAMs to wikis and email archives.
Prebuilt taxonomies and ontologies to tag against, including industry-standard vocabularies.
One graph holding your documents, your entities, and the relationships between them.
Tagging against governed vocabularies
Tag to concepts that carry their own meaning
Content is tagged against SKOS concepts that carry synonyms, translations, broader and narrower terms, and deprecation history. A search for one term finds content tagged with its narrower concepts and its former labels.
- Concept-based tags. A tag resolves to a governed concept with its own definition and owner.
- Synonym and label handling. Alternate labels and past labels resolve to the same concept automatically.
- Hierarchy-aware retrieval. Query a broad concept and get everything tagged beneath it.
Machine tagging with human governance
Let the model suggest, let the steward decide
Automated tagging handles the volume. Governance handles the accuracy. Suggested tags arrive with a confidence score, land in a review queue where thresholds and rules decide what needs a human, and every accepted tag records how it was produced.
- Suggestions with confidence. Machine tags are proposals until they clear the threshold you set.
- Vocabulary-bounded. Suggestions are constrained to governed concepts, so the model cannot invent a term.
- Provenance on every tag. Machine or human, which model, which version, which reviewer.
Entity linking
Connect the document to the thing it is about
Named entities in content are resolved to the governed records for those entities, so a document about a subsidiary is findable from the parent, and a document about a molecule is findable from the product it became.
- Resolve to master records. Mentions link to the governed entity, not to a string.
- Traverse relationships. Find content via ownership hierarchies, product families, or study relationships.
- Disambiguation on context. Same name, different entity, resolved using the surrounding graph.
Rights, retention & readiness
Know what the model is allowed to read
Access, licensing, retention, and provenance metadata travel with the content. When a retrieval system or an agent asks for a document set, the answer respects those constraints, and you can produce the evidence.
- Constraints as data. Rights, licence terms, retention, and classification are governed fields, not free text.
- Filtered retrieval. A request returns only the content the requester and the use case permit.
- Provable exclusions. Show which content was withheld from a corpus and on what basis.