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Kepler is an AI financial research agent built on a simple idea: an answer is only useful if you can prove it is right. It reads primary sources, SEC filings (XBRL), earnings call transcripts, and market data, and returns answers and spreadsheet models where every figure traces back to the document it came from. The difference is between AI that is probably right and AI that is provably right. Ask a question in natural language and Kepler does the work: finding the right filings, extracting the numbers, building the model, and showing its sources. Use it in the Kepler app, or bring it into your AI assistant through the MCP connector.
The Kepler app home, where you ask financial research questions in natural language

Quickstart

Run your first research question and read a citation-backed answer.

MCP connector

Use Kepler from Claude, ChatGPT, Cursor, and other MCP clients.

Connect a client

Add the Kepler connector to your assistant in a few steps.

Tools reference

The full set of tools the connector exposes to your assistant.

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What you can ask

  • Company financials: “What was NVIDIA’s data center revenue last quarter, and how fast is it growing?”
  • Earnings recaps: summaries of an earnings call against the filed results and prior guidance.
  • Financial models: “Build a 3-statement model for Apple for the last three fiscal years.”
  • Comparisons: side-by-side fundamentals across two or more companies, every figure cited.

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Why citations matter

Kepler is built for work that has to be right. Every number in an answer links to the filing or transcript it came from, and each result includes a sourcing report so you can see what was cited and what wasn’t. The model is auditable through the links, not a black box.