Analysts manually review thousands of documents in data rooms under...
Due diligence that sees the connections others miss
M&A due diligence means analyzing thousands of documents under time pressure while deal data must stay confidential. Context builds a knowledge graph across data room documents, mapping entity relationships and surfacing risks with citations -- all on your infrastructure.
Book a Demo10x
FASTER DOCUMENT REVIEW ACROSS DATA ROOMS
85%
OF KEY RISKS SURFACED WITHIN FIRST 48 HOURS
100%
ON-PREMISE -- DEAL DATA NEVER LEAVES YOUR NETWORK
3x
MORE ENTITY RELATIONSHIPS DISCOVERED VS. MANUAL REVIEW
The Problem
Why this is hard without a knowledge graph
Analysts manually review thousands of documents in data rooms under tight deal timelines
Entity relationships across contracts, org charts, and financial documents are invisible without manual mapping
Deal-sensitive information cannot be processed by cloud-based AI tools
Key risks and obligations are buried in document footnotes, appendices, and cross-references
The Solution
How Context solves it
01
Knowledge graph across the data room
Context ingests thousands of documents and builds an entity graph mapping people, companies, contracts, obligations, and financial relationships. See connections that manual review misses.
02
Citation-backed risk discovery
Query the data room in natural language. "What change-of-control provisions exist across all customer contracts?" -- Context returns every relevant clause with document citations.
03
Entity relationship mapping
Visualize connections between entities -- subsidiaries, key personnel, contractual relationships, and financial obligations. Identify undisclosed related-party transactions and hidden liabilities.
04
Deal data stays confidential
Context deploys on-premise so deal documents, financial models, and analysis never leave your network. No deal data is used for model training. Full audit trail for privilege protection.
Beforeraw signals
Entity relationships across contracts, org charts, and financial documents are...
Deal-sensitive information cannot be processed by cloud-based AI tools
Key risks and obligations are buried in document footnotes, appendices,...
Analysts manually review thousands of documents in data rooms under...
Entity relationships across contracts, org charts, and financial documents are...
Deal-sensitive information cannot be processed by cloud-based AI tools
Key risks and obligations are buried in document footnotes, appendices,...
Afterstructured outcomes
01
Knowledge graph across the data room
02
Citation-backed risk discovery
03
Entity relationship mapping
04
Deal data stays confidential
How It Works
From deployment to answers
01
Ingest data room documents
Connect to your data room or upload documents directly. Context processes contracts, financials, org charts, IP portfolios, and regulatory filings.
02
Entity graph builds automatically
Context extracts entities (people, companies, contracts, obligations) and maps their relationships. The graph reveals connections that spreadsheet analysis misses.
03
Query for specific risks
"What are the material litigation risks?" "Which contracts have non-compete clauses?" -- Get comprehensive answers with citations to specific document sections.
04
Generate diligence reports
Export findings with full citations and entity relationship maps. Every claim traces back to a specific document, page, and clause.
05
Iterate as new documents arrive
As the data room updates, Context re-indexes and surfaces new findings. The knowledge graph grows with every addition.
FAQ
Frequently asked questions
How does Context help with M&A due diligence?
Context ingests data room documents and builds a knowledge graph that maps entity relationships, contractual obligations, and financial connections. Analysts can query in natural language and get citation-backed answers, dramatically accelerating the review process.
Can Context handle thousands of documents?
Yes. Context is designed for enterprise-scale document processing. It can ingest and index thousands of documents -- contracts, financials, regulatory filings, org charts -- and make them queryable within hours.
How does the entity relationship mapping work?
Context automatically extracts entities (people, companies, contracts, obligations, dates) from documents and maps their relationships. This reveals connections like undisclosed related-party transactions, conflicting contractual obligations, and key personnel dependencies.
Is deal data safe with Context?
Context deploys entirely on your infrastructure. Deal documents, financial models, and analysis results never leave your network. No data is used for model training. Full audit trails support privilege and confidentiality requirements.
How does Context compare to traditional due diligence tools?
Traditional tools use keyword search across documents. Context builds a knowledge graph that understands entity relationships and can answer complex questions spanning multiple documents with citations. It surfaces risks that keyword search misses.
See Context in action
30-minute technical walkthrough. No slides -- just a live demo tailored to your environment and use case.
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