Structure contracts, filings, and case documents
Convert contracts, court filings, legal memoranda, and correspondence into searchable, structured Markdown. Extract key terms, dates, party names, and clause summaries.
Legal professionals deal with enormous document volumes: contracts, court filings, discovery materials, deposition transcripts, regulatory submissions, and client correspondence. A single M&A transaction can involve thousands of pages of due diligence documents. Litigation discovery routinely produces hundreds of thousands of pages that must be reviewed, categorized, and analyzed.
The legal industry has been slow to adopt AI-powered document processing because accuracy requirements are non-negotiable — a missed clause or misread date can have serious legal consequences. PaperAI addresses this with its human-in-the-loop review interface: AI handles the heavy lifting of conversion and extraction, while legal professionals verify every output against the original before approval.
- Manual contract review is slow and inconsistent across reviewers
- Scanned court documents are difficult to search and reference
- Key terms and dates are buried in dense legal prose
- Discovery document volumes overwhelm manual processing capacity
- Paper archives are unsearchable and deteriorating
Ingest legal documents
Upload contracts, filings, and correspondence in any format. PaperAI handles scanned court documents, digitally-signed PDFs, and even photographed exhibits.
Set up your legal document settings
Tell PaperAI what details to pull out: party names, effective dates, term lengths, governing law, key obligations, and termination clauses. Use premium models for complex multi-party agreements.
AI-powered conversion
PaperAI converts documents to structured Markdown preserving section headers, numbered paragraphs, and cross-references. Extracted fields are pulled simultaneously.
Attorney review
Legal professionals review the side-by-side comparison. Verify extracted terms against the original. For legal documents, we recommend always requiring manual approval.
Organize and export
Organize approved documents into matter-specific folders. Export your data for contract management systems or legal databases.
PaperAI automatically pulls out these details from your documents, organized and ready to use:
| Field | Type | Example |
|---|---|---|
| Contract Title | string | Master Services Agreement |
| Effective Date | date | 2024-01-15 |
| Party A | string | Acme Corp. |
| Party B | string | GlobalTech Solutions Inc. |
| Term Length | string | 24 months |
| Governing Law | string | State of Delaware |
| Termination Clause | string | 30 days written notice |
| Auto-Renewal | boolean | Yes |
0x faster document review
AI pre-extracts key terms so attorneys focus on analysis, not data entry — reviewing extracted data is far faster than reading every page.
Consistent extraction
Every contract is processed with the same extraction rules, eliminating the inconsistency of manual review by different team members.
Searchable archive
Convert paper archives into searchable, structured digital records — making prior art research and precedent lookup dramatically faster.
Every document is converted to clean, readable text and organized data you can use in other tools. Here's an example of what PaperAI produces for a typical legal document processing document:
Sample Output for: Legal Document Processing
## Master Services Agreement
**Effective Date:** January 15, 2024
**Parties:** Acme Corp. ("Client") and GlobalTech Solutions Inc. ("Provider")
### Key Terms
- **Term:** 24 months from Effective Date
- **Governing Law:** State of Delaware
- **Termination:** Either party may terminate with 30 days written notice
- **Auto-Renewal:** Yes, for successive 12-month periods
### Obligations
1. Provider shall deliver monthly status reports by the 5th business day
2. Client shall remit payment within 30 days of invoice date
3. Both parties agree to mutual NDA terms per Exhibit BLaw firms, corporate legal departments, paralegals
Further Reading
Related Use Cases
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