Get structured data without manual setup
Describe what you want to know in plain English and DocuPrism generates the schemas, prompts, and extraction logic needed to process the documents.
Product
Turn messy documents into trustworthy, structured data and temporal knowledge graphs.
Users ask questions in plain English. DocuPrism generates the extraction schema, finds the right evidence, extracts fields and relationships, resolves duplicate entities, and returns trust-scored outputs with full provenance.
Documents in
Trusted data out
Structured fields and tables with confidence
Entity and relationship graph with timelines
Source evidence, coordinates, and review signals
What it helps teams do
Describe what you want to know in plain English and DocuPrism generates the schemas, prompts, and extraction logic needed to process the documents.
Every extracted value carries confidence, source evidence, and review signals so teams know what can be automated and what needs human review.
DocuPrism does not just retrieve chunks. It extracts fields, entities, relationships, timelines, and structured facts for analytics and automation.
Temporal graphs track active and historical states so teams can ask what was true at a specific date, not just what appears in the latest document.
Entity resolution and a canonical entity store help new documents update existing knowledge instead of creating duplicate records every run.
Product features
DocuPrism combines schema generation, confidence-aware extraction, spatial grounding, and temporal graph construction in one production-oriented pipeline.
Business users can describe the fields, relationships, checks, and questions they care about without hand-writing schemas or extraction prompts.
DocuPrism creates extraction schemas for new document types, reducing onboarding effort and dependency on data engineering teams.
Extract invoices, contracts, reports, logs, forms, and correspondence into usable JSON or CSV with confidence signals on each result.
Values can be traced back to source pages, spans, and PDF coordinates so reviewers can see exactly where an answer came from.
Handle line items, nested tables, multi-column layouts, and dense documents without brittle template rules.
Reduce headers, footers, boilerplate, and irrelevant content before extraction so the model focuses on the evidence that matters.
Convert documents into connected entities and relationships that understand time, history, and change across a collection.
Merge duplicate people, companies, projects, assets, and events across documents, even when names and formats differ.
Return provenance with extracted facts, including source evidence, reasoning notes, pages, spans, or bounding boxes.
How it works
The workflow is designed for repeatable document processing, not one-off prompt experiments.
01
Define what you need to extract, check, or understand using normal language instead of brittle rules.
02
DocuPrism turns the request into schemas, retrieval filters, field definitions, and validation signals.
03
The engine extracts fields, tables, entities, relationships, and timelines with confidence and source grounding.
04
New documents update the canonical entity store so knowledge compounds across batches and collections.
05
Teams inspect uncertain outputs, export trusted data, and connect results to APIs, workflows, and AI systems.
Confidence Passport
The Confidence Passport combines document quality, retrieval relevance, and extraction confidence with source evidence. Reviewers can inspect uncertain results while high-confidence outputs move into downstream systems.
Review signals
Beyond search
Extract the values, line items, dates, parties, obligations, and checks your business systems need.
Connect people, companies, projects, assets, incidents, and events across many files.
Track when relationships began, changed, ended, or became true for point-in-time analysis.
Production fit
Run with private models and keep sensitive documents inside controlled infrastructure where required.
Built for repeatable processing, batch runs, validation, evaluation, and operational review.
Connect through upload flows and APIs, with support for sources such as S3, SharePoint, and Google Drive.
Demo
Bring a representative sample. We will show the extracted fields, source evidence, graph relationships, and confidence signals so you can judge where automation is ready and where review is still needed.
Schedule a demo