Decision models join LLMs to extract data from documents
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.
Hi Product Hunt! I'm Sergii, and I build ContextGem.
ContextGem launched here in April 2025, at version 0.2. It has since reached 1.0, and enough has changed to show it to you again.
Why it exists
Reliable extraction from documents usually means writing prompts, designing validation models, mapping the output back to the source and tracking usage across models. With ContextGem, you describe what you need from a document in plain words, and it does that work. It generates the prompts, validates the output into typed values, and traces every fact to the paragraph or sentence it came from, with a justification if you ask for one.
What's new since the first launch
Decision models next to LLMs. A decision model answers closed questions (yes or no, one of your labels, a level of a scale) with the probability of each possible answer, and writes no text. It is fast and low-cost for the questions you ask in volume, and you set the threshold.
The right model for each question. A model group sends the open questions to an LLM and the closed ones to a decision model, and any model can fall back on others.
Your files as they are. With Docling, ContextGem starts from PDF (scanned too), Office files, email, images, audio, video and more. Table rows keep their column headers, and the items from paged files come back with their page numbers.
Agentic workflows. Extract the metadata of documents as they arrive, call an extraction from an agent's tool, or let a decision model choose the next step.
Cloud or local. LLMs through LiteLLM (OpenAI, Anthropic, Google, Ollama, LM Studio and more), and decision models from TypeSafe, OpenAI, Microsoft Foundry or your own server.
What it is not
ContextGem reads one document in depth, every paragraph of it. It does not search across a corpus, which RAG frameworks do better.
It's free and open source (Apache 2.0):
uv add contextgem
I'd love your feedback. Which questions about your documents would you give to a decision model rather than an LLM? And what is missing for your use case?
“Decision models join LLMs to extract data from documents”
ContextGem 1.0 launched on Product Hunt on October 27th, 2026 and earned 82 upvotes and 3 comments, placing #19 on the daily leaderboard. 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.
On the analytics side, ContextGem 1.0 competes within Open Source, Developer Tools, Artificial Intelligence and GitHub — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how ContextGem 1.0 performed against the three products that launched closest to it on the same day.
Who hunted ContextGem 1.0?
ContextGem 1.0 was hunted by Sergii Shcherbak. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of ContextGem 1.0 including community comment highlights and product details, visit the product overview.
Sergii Shcherbak
Hi Product Hunt! I'm Sergii, and I build ContextGem.
ContextGem launched here in April 2025, at version 0.2. It has since reached 1.0, and enough has changed to show it to you again.
Why it exists
Reliable extraction from documents usually means writing prompts, designing validation models, mapping the output back to the source and tracking usage across models. With ContextGem, you describe what you need from a document in plain words, and it does that work. It generates the prompts, validates the output into typed values, and traces every fact to the paragraph or sentence it came from, with a justification if you ask for one.
What's new since the first launch
Decision models next to LLMs. A decision model answers closed questions (yes or no, one of your labels, a level of a scale) with the probability of each possible answer, and writes no text. It is fast and low-cost for the questions you ask in volume, and you set the threshold.
The right model for each question. A model group sends the open questions to an LLM and the closed ones to a decision model, and any model can fall back on others.
Your files as they are. With Docling, ContextGem starts from PDF (scanned too), Office files, email, images, audio, video and more. Table rows keep their column headers, and the items from paged files come back with their page numbers.
Agentic workflows. Extract the metadata of documents as they arrive, call an extraction from an agent's tool, or let a decision model choose the next step.
Cloud or local. LLMs through LiteLLM (OpenAI, Anthropic, Google, Ollama, LM Studio and more), and decision models from TypeSafe, OpenAI, Microsoft Foundry or your own server.
What it is not
ContextGem reads one document in depth, every paragraph of it. It does not search across a corpus, which RAG frameworks do better.
It's free and open source (Apache 2.0):
I'd love your feedback. Which questions about your documents would you give to a decision model rather than an LLM? And what is missing for your use case?
GitHub: https://github.com/shcherbak-ai/contextgem
Docs: https://contextgem.shcherbak.ai