AI document processing for long, complex files
Run extraction, summarization, and review across long PDFs and scanned documents using long-context models. Configure batch agents to process document sets and return structured output.
What this looks like in practice
Contract and policy review
Configure agents to read contracts, MSAs, NDAs, and policy documents end-to-end. Surface clauses, obligations, dates, and counterparties for analyst review. Models flag terms that match your defined criteria, such as auto-renewal, indemnity, or liability caps. Output is structured for downstream workflow tools, with the source passages cited for human verification.
Structured data extraction
Define a JSON schema and let the agent extract fields from invoices, forms, statements, or filings. Function calling enforces the output shape, so each document returns the same keys. Handles native PDFs, scanned files via OCR, and mixed-layout documents. Results route to spreadsheets, databases, or downstream automation.
Multi-document research and summarization
Group related files into a knowledge base and ask cross-document questions. Retrieval pulls relevant passages, then a long-context model reasons across them and returns answers grounded in cited sources. Useful for diligence, regulatory research, literature review, and synthesizing findings across hundreds of files.
Why yno.ai for Document Processing
- Long-context models read documents up to hundreds of thousands of tokens in a single pass
- Structured output via function calling returns JSON that conforms to a schema you define
- Batch agent runs process document sets in parallel with retries and per-file logs
Recommended models
Key concepts
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Frequently asked
- How accurate is AI contract review compared to human review?
- AI is a review aid, not a replacement for qualified legal review. Models can locate clauses, summarize obligations, and flag terms against your criteria, which speeds up first-pass triage. Final interpretation, risk assessment, and any binding determinations should remain with qualified reviewers. yno.ai surfaces source passages so a human can verify every flagged item.
- Can AI extract structured data from PDFs and scanned documents?
- Yes. Native PDFs are parsed directly, and scanned PDFs or images run through OCR before extraction. You define the target schema, and function calling constrains the model's output to that shape. Results vary with document quality and layout complexity, so production setups typically include validation rules and a human review step for low-confidence fields.
- Which AI model handles long documents best?
- Long-context models from Anthropic, OpenAI, and Google currently support context windows in the hundreds of thousands of tokens, which covers most single-document workloads. For document sets larger than the context window, retrieval-augmented generation chunks and indexes content, then feeds relevant sections to the model. yno.ai lets you pick the model per workflow and switch as new versions ship.
- Can yno.ai process documents in bulk via batch jobs?
- Yes. You can submit a folder, an upload, or a connected source and run an agent across every file with shared instructions and a shared output schema. Each run produces structured results plus per-file logs, and failed items can be retried independently. Throughput depends on the model provider's rate limits and the document size.