Why ContextGem?

ContextGem is a framework for extracting structured data and insights from documents using LLMs and decision models.

Describe, don’t prompt. You describe what to extract in natural language, and ContextGem handles how — prompt construction, data modeling, output parsing, source reference mapping, justifications, and pipeline orchestration are all built in.

🧩 What Document Extraction Requires

Building reliable structured-extraction workflows from documents typically involves solving a stack of recurring problems:

📝 Prompting and Modeling

• Crafting extraction prompts for each new task and adapting them when requirements change

• Defining data models with validation logic for every output shape

• Embedding few-shot examples that calibrate extraction behavior

🔧 Document and Pipeline Plumbing

• Mapping outputs back to precise locations in the source document

• Handling nested context (e.g. document > sections > paragraphs > entities)

• Orchestrating multi-step, multi-model workflows

• Tracking usage and costs across providers

• Configuring concurrent I/O to keep extraction fast

💎 The ContextGem Approach

ContextGem provides a “batteries included” answer to each of these problems, with simple, declarative syntax:

• Prompting and modeling. ContextGem auto-generates dynamic prompts and validated data models from your natural-language extraction targets, with optional few-shot calibration via attached examples.

• Document and pipeline plumbing. Aspects map back to precise paragraph- or sentence-level locations in the source, and so can the concepts that an LLM extracts. Nested context (document → aspects → sub-aspects → concepts) is handled automatically. The whole workflow is a single declarative, reusable, serializable pipeline. Concurrent I/O, fallback/retry, and cross-provider usage and cost tracking are built in.

• The right model for each question. In a model group, each aspect and concept goes to the model that suits it: an LLM where it takes reasoning, or a decision model where a fast, low-cost answer is enough, and any model can fall back on others. A decision model answers closed questions (yes/no, one of your labels, a level of a scale) with the probability of each possible answer, so that you set the threshold.

For details on each capability, see the relevant feature pages in the sidebar.

🎯 Focused Approach

ContextGem is built for in-depth analysis of individual documents. There is no search step that picks the chunks that look relevant, as in RAG: the model reads every paragraph of the text it extracts from, so no fact is missed because a search ranked its passage low (see How it works).

The trade-off: ContextGem does not query across documents or search a corpus. For these, RAG frameworks (e.g. LlamaIndex, Haystack) remain more appropriate.