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Extract structured data from documents using custom schemas

Gain targeted insights from complex documents, with granular citations and confidence scores

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  • 1B+

    Documents processed

  • 25M+

    package downloads a month

  • 300k+

    LlamaParse users

Why LlamaExtract

Extract the right data — with visibility into how and why

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Field-level confidence scores

Quantify extraction certainty by individual columns

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Citations & traceability

Trace data to the source with explanations

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Multi-field extraction

Capture names, dates, numbers, decisions, and more

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Layout + context-aware

Uses reasoning for superior accuracy extraction

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Iterative schema development

Define your own schema or auto detect fields from your source document

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Granular control

Different parsing modes to optimize for the right balance of cost and accuracy

Use cases

Works with any document. Solves for every workflow.

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01

Insurance claims

Extract policy ID, claim reason, outcome

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02

Invoices

Extract total, due date, vendor

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03

Contracts

Extract parties, dates, obligations

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04

Research

Extract key findings, conclusions, authors

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05

Call transcripts

Extract sentiment, speaker quotes, decisions

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Designed for developers. Documented for clarity

Live notebooks + examples UI API TypeScript SDK Python SDK
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Go even further with workflow automation

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Trusted by leading AI builders and enterprise teams

We’ve helped leading teams go from prototype to production with real-world results.

Extract with confidence. Build with clarity.

Start using LlamaExtract to transform your documents into precise, actionable data.