DataLime – Document intelligence made easy

Document intelligence that Behaves Like Software

Built for ugly documentslayout driftsreal workflowstechnical teamsproduction use

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Built for technical teams

Give developers the control they need to build document processing workflows that hold up beyond the demo.

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Break free from classification first step

Create pipelines tailored to your workflow

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Produce structured outputs your systems can trust and use directly, with no manual fixing downstream.

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Rules meet flexibility

Pure AI extraction often performs well in demos but struggles once deployed in production. Outputs may vary between runs, models can fabricate fields, and updates quietly introduce drift. DataLime avoids this by pairing LLM flexibility with deterministic guardrails. Every extraction remains structured, validated against schemas, and backed by confidence signals and a clear audit trail.

Composable pipelines

Blend AI and non-AI methods in the same pipeline so you can handle messy documents without giving up control, predictability, or clean output.

Nested and Repeated Content Extraction

Handle repeated blocks, nested tables, and layout-heavy document structures without flattening everything into one brittle global grid. The evaluation notes explicitly mention nested tables, repeaters, and treating table surfaces as first-class extraction units.

Extensible Engine

Extend the system with custom logic where needed while keeping execution explicit, typed, and controlled instead of turning pipelines into script soup. The notes support JS and C# with typed contracts and safe extension boundaries.

Parameterized Execution

Write extraction logic once, then customize it with parameters. Prompts, field mappings, and thresholds can be injected at runtime, allowing the same pipeline to handle many document variations without duplicating configuration.

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