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AI that shows its work

Give your AI a grounded foundation. Intelligible makes the evidence visible, reusable, and available to any AI.

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Two product families

SummandAI on your data

Chat with your data. Upload a CSV, or connect a database on Enterprise, and ask questions. Summand answers from the models it fitted and the statistics it computed, and agents can call the same results over MCP.

Try Summand free →

ElbiFor data science teams

Elbi is open source and runs on your machine. Pull in your sources, write derivations in Python or wrap a model from MLflow, and serve them to agents over MCP as cached tools. Its chat agent attaches a verification verdict to every answer.

uv tool install elbiTry it now →

Why people pick Intelligible

Benchmarked better results: We take Claude Opus from 74% to 100% on data science reasoning tasks, and on datasets built so the obvious reading of the rows is the wrong one, we get three times as many verdicts right as a frontier model working alone. See the benchmark →
Semantic and empirical reasoning: A semantic layer holds the rules your team wrote down, like how revenue or churn is defined. An empirical layer holds what the data actually does: distributions, effects and fitted models computed from the rows. We reason with both, so answers follow your definitions and stay grounded in your data.
Validation and verification: Answers are built from verified components, like computed statistics and fitted models, not generated from scratch, so the AI has far less room to make things up. You can trust what it tells you, and when someone in a meeting asks where a number came from, you have the component to point to.