Extract Entities (NER)
Pull the who, when, and how-much out of free-form text — people, organizations, locations, dates, and amounts.
What it is
Named-entity recognition over your documents — the who, when, and how-much pulled out of free-form text like contracts, emails, and reports, without writing regexes.
Why it's painful
The facts you need are buried in prose. Hand-rolled regexes are brittle, miss edge cases, and break on the next phrasing or format.
What Xberg does
The enrichment step extracts named entities — people, organizations, locations, dates, and monetary amounts — and groups them, ready to use.
- People, organizations, locations, dates, and monetary amounts.
- Grouped by type for easy consumption.
- Runs as part of the enrichment step — no separate model to host.
- Great for populating fields, search facets, or a knowledge graph.
More use cases
RAG Pipeline Ingestion
Turn a pile of PDFs, Office docs, and HTML into clean, chunked, embedded data for your vector database — in one call.
Document-Reading Agents
Give your AI agents one tool to read any document — 100+ formats, structured output, every framework.
Replace Legacy IDP
Swap brittle, template-based processing for one API that returns schema-mapped JSON — no templates to maintain.