How Intelligible works

01

Connect your data: Bring in your structured data. No preprocessing pipelines or prompt engineering required.

02

Automatically generate components: Intelligible Summand analyzes your dataset and extracts components.

03

Build structured context: Behind the scenes, components are translated into natural language statements that preserve their structure.This creates a compact context that can be passed into standard LLM APIs.

04

Reason with your components: When you ask a question, the model reasons over components instead of raw data.This gives you: more consistent answers, clearer explanations, reduced token usage. In Summand, you can see this directly through the chat interface.

05

Reuse across workflows: Components are persistent and reusable.They can be used across queries, applications, and AI systems. This turns one-time analysis into reusable infrastructure.

Why This Works

LLMs are good at reasoning over language, but not at parsing raw tables. Components translate structured data into a form that AI models can reason over efficiently, while preserving the underlying data relationships.

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