Extract data from documents
Combine decision models with LLMs for document data extraction.
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Extracts document data using LLMs and decision models that return probabilities for closed questions.
Product brief
New in 1.0: decision models next to LLMs. They answer closed questions with the probability of each answer, fast and at a low cost, and a model group sends each question to the model that suits it. Also new: Docling for your files, and tools for agents.
Why we selected it
Offers a technically differentiated approach to document extraction: decision models return answer probabilities for closed questions, while model groups route questions to suitable models. Docling integration and agent,
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Combine decision models with LLMs for document data extraction.
Decision models answer closed questions with a probability for each answer.
A model group sends each question to the model suited to it.
Version 1.0 adds Docling for working with files.
Version 1.0 includes tools intended for agent use.
Best-fit use cases
FAQ
ContextGem 1.0 combines decision models and LLMs to extract data from documents.
The decision models answer closed questions and provide a probability for each answer.
A model group sends each question to the model suited to that question.
Version 1.0 adds decision models alongside LLMs, model-group question routing, Docling for files, and tools for agents.