Document intelligence that Behaves Like Software​

Extract structured data from any document with accuracy you can verify and audit. AI flexibility wrapped in deterministic guardrails — ready for production.

Built for ugly documentslayout driftsreal workflowstechnical teamsproduction use

The hybrid document extraction platform

AI flexibility meets production reliability

Pure AI extraction gets you 80% of the way there—then breaks in production. Outputs vary between runs. Models hallucinate fields. Updates cause silent drift. Sensible solves this with a hybrid approach: LLM flexibility wrapped in deterministic guardrails. Schema enforcement, confidence signals, and full audit trails built into every extraction.

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The hybrid document extraction platform

AI flexibility meets production reliability

Pure AI extraction gets you 80% of the way there—then breaks in production. Outputs vary between runs. Models hallucinate fields. Updates cause silent drift. Sensible solves this with a hybrid approach: LLM flexibility wrapped in deterministic guardrails. Schema enforcement, confidence signals, and full audit trails built into every extraction.

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Automate your document workflow

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Automate your document workflow

Automate your document workflow tyighuhodrturpuhpup rstjupituhpgihdrpiu

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

Composable Pipelines

Build once, reuse everywhere—standardize extraction logic across teams and document types.

Config as Code

Define pipelines in readable, exportable config so teams can version, review, reuse, and ship document logic like software, not hidden UI state.

Transform Inputs

Preprocess and transform inputs at any level functions (deskew, cleanup, transforms) so downstream extraction is more accurate and stable.

Integrate Anywhere

Integrate with your stack without rebuilding everything around the tool.

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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.