Turning Unstructured Data Into Knowledge Your AI Can Trust

Unstructured in, knowledge graph out — a live demo of extraction, entity resolution, and provenance for enterprise AI.

Roughly 80% of what your enterprise knows lives in PDFs, contracts, transcripts, slide decks, and meeting notes — and none of it is queryable until someone extracts what's inside. A spreadsheet declares what it means. A 132-page contract does not.

The common shortcut is to chunk that content, embed it, and let similarity search find whatever sounds like the answer. It works for simple lookup and fails everywhere else: accuracy plateaus near 80%, entity identity is thrown away at ingestion, cross-document connections never form, and nobody can explain why an answer is right.

In this live session, Fluree CEO Brian Platz and Senior Solutions Architect Andrew Johnson show a different path: unstructured documents in, a knowledge graph out.

What you'll see

• Extraction that lands as a graph, not chunks. Watch real PDFs, Word docs, and decks become sections, tables (cell by cell), retrieval chunks, and embeddings — then entities, classifications, and typed relationships grounded in your own ontology. The language model can add to your vocabulary, but it can't rename or invent it, and every extracted fact keeps its evidence.
• One identity across structured and unstructured data. A CRM export and a set of meeting notes mention the same person. We'll show them resolve to a single node, then ask questions that span both sources and walk each answer back to the exact paragraph, page, and source file it came from.
• A cost model built for scale. Under the hood is a deterministic parser that ranks first among 17 engines on a public benchmark, runs in about 8 milliseconds per document on a CPU, and calls a model only when a page needs one — more than half of documents never do. It's open source under Apache 2.0, and it runs locally with your own models or fully managed on Fluree AI.

Who should attend

Data and AI leaders, architects, and platform teams with document corpora their AI initiatives can't yet use reliably — particularly in financial services, insurance, life sciences, manufacturing, legal, and the public sector. If your RAG pilot stalled on accuracy, provenance, or the cost of running models over every page, this session is for you.

Can't make it live? Register anyway and we'll send the replay!