Unstructured and Elastic Partner to Deliver Powerful GenAI and Agentic Search Systems
Unstructured and Elastic have partnered to help developers build intelligent systems that retrieve, interpret, and act on enterprise data. By integrating Unstructured with Elasticsearch’s real-time, hybrid search engine, this collaboration enables scalable GenAI workflows—from ingestion to vector search—on top of high-fidelity document content.
Using Unstructured, teams can transform PDFs, DOCX, HTML, and image-based files into structured, semantically enriched content, complete with layout metadata, named entities, table structure, and image captions. This data is then embedded and indexed into Elasticsearch for fast, accurate retrieval—supporting everything from RAG pipelines to agentic systems that need precise document-grounded answers.
This integration is available today via Unstructured’s fully managed platform.
Unlocking Search-Ready Structure from Complex Documents
Modern AI systems depend on the quality and structure of their input data. Traditional ingestion pipelines often flatten documents and miss crucial layout cues or semantic markers—leading to incomplete or inaccurate results.
Unstructured solves this by parsing complex files into meaningful chunks and enriching them with key metadata: named entities, tables, image content, and layout structure. These enriched outputs are ideal for indexing in Elasticsearch, which supports vector search alongside traditional keyword and metadata filtering. This hybrid approach means users can search by concept, exact term, or structured field—all in one engine.
The result is a high-performance, context-aware data layer built for intelligent assistants, RAG workflows, and domain-specific copilots.
Built for Teams Shipping Context-Aware AI
The Unstructured × Elastic integration supports mission-critical GenAI workflows, including:
- Agentic systems that query, filter, and reason over documents in real time
- Semantic and hybrid search that combines vector similarity with field-based filters
- Retrieval-augmented generation pipelines that use documents as grounding data
- Enterprise copilots for legal, healthcare, public sector, and financial use cases
Whether you're building a public-sector chatbot, internal knowledge navigator, or AI-powered analytics stack, this integration gives you the precision of Unstructured’s parsing with the reach and responsiveness of Elasticsearch.
Available Today
Explore the integration now via:
- Unstructured platform – Enterprise-grade, no‑code UI + API pipeline (see Unstructured API & Platform docs)
- Elasticsearch integration Docs – Technical reference on Elastic connectors and ingest pipeline
Want help getting started? See our documentation or reach out to discuss your project.
