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ContextGem 1.0

Decision models join LLMs to extract data from documents

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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.

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PH · launched Oct 27, 2026 · 79 upvotes (top 2%)

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Could you build this?

Partial The LLM extraction and file-parsing layers are easy to wrap, but the new decision models that output calibrated probabilities for closed questions need real ML training and evaluation work beyond what vibe coding produces.

What it would actually take: Build a Python library that wraps LLM extraction with Docling-based document parsing and a model router. The decision models are classifiers that return calibrated per-answer probabilities, so they need labeled training data, fine-tuning or distillation, and calibration evaluation. That requires ML engineering expertise and a labeled dataset for each answer type.

Competitors

Other products that read as similar to this one : 130 launches clear the similarity bar, closest 8 shown.

Attention rank: #11 of 131 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).

Launched 362 days after the earliest competitor.

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