Polymer
A distributed document intelligence platform that turns large unstructured document collections into queryable knowledge graphs of entities and their relationships.
The Problem
Document-heavy teams were stuck with manual extraction and fragmented analysis workflows. Unstructured content was difficult to operationalize, which delayed downstream insights and increased costs. Teams needed scalable processing with better retrieval and context.
Keyword search was the ceiling. Organizations accumulate contracts, reports, filings, and records by the million, but without automated entity identification and relationship mapping across documents, questions like "which organizations appear alongside this person" simply cannot be answered. Legal discovery and due diligence stayed slow, expensive, manual efforts.
What We Built
We built an end-to-end document intelligence platform for ingestion, extraction, structuring, and search-ready outputs. The system handles large volumes through distributed processing and consistent pipeline stages. It turns raw files into usable, queryable knowledge assets for operations and analysis.
Processing is parallelized across a distributed cluster, so throughput scales with compute rather than with analyst hours. Extracted entities and their co-occurrences populate a graph structure, which makes relationship questions a direct query instead of an inference someone has to make by reading.
Distributed document processing
Parallel ingestion and text extraction across a compute cluster handles a wide range of file formats, so large collections process in a single job rather than sequential batches.
Automated entity extraction
NLP identifies many entity types—people, organizations, locations, dates, monetary amounts—across every document, producing structured output from unstructured text.
Knowledge graph construction
Entities, documents, and their co-occurrence relationships populate a graph, turning "who appears with whom, and where" into a query rather than a manual cross-reference exercise.
Graph query API
A service layer exposes graph retrieval in a format ready for interactive network visualization, so analysts explore relationships visually instead of reading result lists.
How It Works in Practice
A firm holding thousands of client contracts that needs to identify every organization mentioned and surface overlapping relationships for conflict-of-interest review.
- 1
Submit the collection
The document set is pointed at the platform as a single job—no per-file handling, no manual queuing.
- 2
Process in parallel
Workers across the cluster extract text from every document simultaneously and run entity recognition, producing tens of thousands of extracted entities from the collection.
- 3
Build the graph
Entities and the documents they appear in become nodes and edges, with co-occurrence relationships captured so connections across separate documents are explicit.
- 4
Query for relationships
Analysts query for clients whose contracts share mentioned entities, surfacing potential conflicts that keyword search would not reveal—then visualize the connections directly.
Engineering Approach
Throughput scales with the cluster
Text extraction and entity recognition are parallelized across distributed workers, so processing a larger collection is a matter of adding compute rather than extending the project timeline.
A graph because the questions are relational
Entities and their co-occurrences are stored as nodes and edges rather than rows, because the valuable questions are about connections between documents—something a relational scan answers poorly and a graph answers directly.
Query results shaped for visualization
The API returns graph data in a structure ready for interactive network rendering, so exploration is visual and immediate instead of requiring a separate transformation step per query.
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