Questions we hear
before a first call

Kepler builds enterprise intelligence for high-stakes industries, starting with investment firms. Our engineers deploy on your firm's own data and build an ontology from it. Code pulls the data, runs the calculations and cites every number, while the model handles the question and the write-up.

A frontier model on its own reads the few passages it retrieves, writes its own numbers and doesn't know your firm's definitions. On Kepler, every document has been read in full, your definitions live in an ontology, and code pulls and computes each number. Ask the same question twice and you get the same answer, with the same sources.

Investment firms first: hedge funds, asset managers, private equity, private credit, investment banks and equity research. The same platform runs for legal, manufacturing, insurance and healthcare teams.

No. Kepler works on top of the data you already have, from filings and market data to your own research and systems, and its answers land in Excel, PowerPoint, Claude and, through our API, your own systems.

Click any figure and you land on the filing, page and line it came from. Calculated figures show the formula and every input. Ask the same question again and you get the same answer, with the same sources.

The ontology is the map of your firm's world: companies, funds, people, documents, your definitions and how they all connect. It resolves every spelling of a company to one record and ships with an ontology of public companies built from SEC filings. When you ask about adjusted EBITDA or Q3, Kepler uses your firm's definition.

The right one for each job. Small models we train read every document and pull out its facts, and a frontier model reads your question and writes the answer. We swap in better models as they ship. Either way, code pulls and computes the numbers, not the model.

Yes. Through our API and MCP, your agents and systems work from the same data, definitions and citations, and Kepler runs inside Claude as a connector. Outputs also go straight into Excel and PowerPoint.

With a working session with our engineers on your workflows. Public data is ready on day one. In the first week we connect your own sources, index every document and build your firm's ontology on top.

26M+ SEC filings, 40M+ investor relations documents and earnings call transcripts for 14,000+ companies, plus market data and news, across 120+ countries and 30+ languages. Our engineers then connect your firm's own sources, such as internal research, models, your CRM, SharePoint and drives.

Yes. Alongside SEC filings for US companies, Kepler covers annual reports and exchange disclosures across 120+ countries, in 30+ languages, with every figure traced to its source document.

Kepler is SOC 2 Type II audited and GDPR and CCPA compliant. Each firm's data is isolated from every other customer's, every query and calculation is logged with its lineage, and your data never trains models other firms use. Our security documentation is at trust.kepler.ai.

Engineers who built this layer before at Palantir. Kepler is led by CEO and co-founder Vinoo Ganesh, who spent seven years at Palantir and was Citadel's first Head of Business Engineering. The team also comes from Citadel, Goldman Sachs, Meta and AWS, and we're backed by the founders of Meta AI Research, OpenAI, dbt Labs, MotherDuck and Square.

No. Kepler (kepler.ai) builds enterprise intelligence for high-stakes industries and is based in New York. It is not related to Kepler Cheuvreux, Kpler, the Kepler space telescope or the astronomer Johannes Kepler.

Request a demo at kepler.ai/demo. We'll walk you through Kepler on the companies and questions your team covers.