Agentic applications rely on accurate, structured inputs to make decisions. Tensorlake enables this by extracting structured document data that agents can reason over—no guesswork or prompting required. This turns your documents into action-ready inputs for any AI agent framework.
Try parsing a research paper and comparing OpenAI Agent analysis of the PDF with OpenAI Agent analysis of Tensorlake reesults to see the benefits of extracted data.

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Why This Matters

  • Agents often rely on brittle LLM outputs or hallucinated parsing
  • Static documents need to become dynamic, queryable objects
  • You want agents to answer: “Was this signed?”, “Who’s the buyer?”, “What’s the effective date?”

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How Tensorlake Helps

  • Outputs structured JSON per schema
  • Enables field-based decision-making (e.g., if buyer_signed == false)
  • Supports orchestration, triggers, and context injection for agent nodes
  • Works with LangGraph, Zapier, webhooks, and custom handlers
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