# Kepler

> Enterprise intelligence for high-stakes industries, starting with investment firms. AI that knows your firm's data and cites every number.

Kepler (kepler.ai) is based in New York and was founded in 2025 by engineers from Palantir. Kepler for Finance is live with investment teams, and Kepler also works with firms in law and manufacturing.

## Why Kepler exists

There is a trust gap in enterprise AI. Frontier models keep getting smarter, but they are probabilistic: they can hallucinate, and you can't tell when. They also don't know a firm's data structure, definitions, or relationships. When one wrong number can cost millions, that means checking everything by hand.

Kepler splits the work. The model does what it is good at: understanding the question and writing the answer. Code extracts and computes every fact and figure, with a citation path back to the source. Kepler's engineers deploy deep into each firm: they pre-process the firm's whole data corpus, build an ontology on top, and run the platform on that. The team built this kind of data layer before at Palantir.

## How Kepler works

The Kepler stack, from the source documents up to the screen:

- **Primary sources:** Every source is captured whole: SEC filings with their full text and XBRL, earnings call transcripts, investor presentations, market data, and the firm's own documents.
- **Ontology:** Kepler builds an ontology from each firm's own data, so every company, fund, person, and document resolves to one record, with the firm's definitions written once in code and a source behind every relationship. The ontology drafts itself from the firm's documents for engineers to review, keeps itself current as new documents arrive, and corrects itself: agents re-check every edge as new documents arrive, propose new types when documents describe something new, and turn each correction into a rule applied everywhere the same mistake appears. Each relationship is a claim carrying its verbatim sentence, how it is known (stated, inferred, or derived), how many sources back it, whether they agree, and two clocks (when it was true and when it was recorded), so the graph can be queried as of any date. It starts from the first public equities ontology, built claim by claim from SEC filings, earnings calls, and regulatory dockets. [More on the ontology](https://kepler.ai/ontology/)
- **Deterministic engine:** Financial statements are assembled from the filings' own XBRL with no model in the path. Every calculation runs as code over cited inputs, and its citation carries the formula.
- **Citations:** Every number is written with its source attached and checked against that source while the answer streams. A citation that doesn't match is removed before anyone sees it.
- **Agent runtime:** Every request starts with a plan. Agents run as state machines with tools gated at each step, and specialist agents read filings, transcripts, exhibits, and decks in parallel.
- **Skills and rules:** Built-in skills encode how finance work is done (adjusted EBITDA, enterprise value, fully diluted shares). A firm can write its own skills, which are compiled and reviewed before they apply.
- **Surfaces:** The Kepler app (chat, workbooks, and a citation sidebar), Excel and PowerPoint, a verified connector for Claude, and the Kepler MCP and API for a firm's own agents and systems.
- **Security and tenancy:** Each firm's work is scoped to its organization, customer data and market data live in separate stores, and agent execution runs on a private network.

## Products

- [The Kepler Platform](https://kepler.ai/platform/): The technical stack in ten layers (connect, read, resolve, retrieve, compute, cite, orchestrate, route, learn, deliver) wrapped by governance: models Kepler trains read every document once, an ontology of sourced claims, and code that computes and cites every number, with one question traced through every layer.
- [Kepler for Finance](https://kepler.ai/finance/): For investment teams at hedge funds, asset managers, private equity and private credit firms, investment banks, and equity research. Answers, models, and memos across filings, transcripts, market data, and the firm's own documents, with every figure cited to its source. Coverage: 26M+ SEC filings, 40M+ investor relations documents, and earnings call transcripts for 14,000+ companies, across 120+ countries and 30+ languages.
- [Kepler for Industry](https://kepler.ai/industry/): The same platform on a firm's own systems in law, manufacturing, and other high-stakes work, with every answer cited to the record it came from.
- [Build on Kepler](https://kepler.ai/build/): The Kepler API, on which a firm's own agents, apps and workflows run on the same data, ontology and engine as the Kepler app, with each figure returned with its value, formula and source; and the Kepler MCP, a verified connector in Claude that also works with any MCP client (Claude reasons; Kepler does retrieval and arithmetic on primary sources, and every figure links to its filing; [setup guide](https://kepler.ai/blog/2026/09/02/kepler-in-claude/)).
- [Compare](https://kepler.ai/compare/): How Kepler compares with frontier models on their own and finance AI tools. In both, the model pulls and writes each number and a citation is attached; Kepler's deterministic engine pulls and computes every number from the source with code, every document is read in full and indexed before a question, the firm's definitions live in its ontology, every figure traces to its line with the formula, citations are checked while the answer is written, conflicting sources are shown side by side and flagged, figures are available as filed or as restated on any date, and a firm's engineers can build their own agents and apps on Kepler's data, ontology and engine, where finance AI tools are sealed apps.

## Kepler for Finance: example questions

- **Hedge funds:** "Semiconductor companies whose inventory days rose three quarters in a row"
- **Private equity:** "Restatements and off-balance-sheet commitments in the last five 10-Ks"
- **Equity research:** "Update FY2026 estimates from Alphabet's latest 10-Q and earnings call"
- **Investment banking:** "Trading comps for US cybersecurity, EV/EBITDA on each filer's own adjusted EBITDA"

## Team

The team built this layer before at Palantir. Engineers also come from Citadel, Goldman Sachs, Meta, and AWS, with 50+ combined years at Palantir.

- **Vinoo Ganesh:** CEO and co-founder. 7 years at Palantir leading storage and compute platforms. Built Veraset to $15M ARR (acquired). Citadel's first Head of Business Engineering.
- **Sara Kromwijk:** Founding Engineer. 11 years at Palantir. Architected product enabling Palantir's transition from per-customer to vertical-based engagements. MSc in CS from EPFL.
- **Susannah Meyer:** Founding Engineer. Senior engineer at Meta on big data systems. Head of Engineering at Enigma Labs, scaling to 300k+ users. BS in CS from Stanford.
- **Sean Gorsky:** Founding Engineer. 7 years at MIT Lincoln Laboratory. Head of Data Science at Sync Computing (acquired by Capital One). MS in EE from BU.
- **Dr. Chris Martin:** Founding Engineer. 14 years at Palantir. Built core analytical infrastructure for classified defense and commercial programs. Shipped production AI and math optimization across manufacturing, aviation, finance, healthcare, and insurance. PhD in Aerospace Engineering from UIUC.
- **Michelle Van:** Founding Engineer. ML and data engineer at Velvet FS, previously at several tech startups. MS in Data Science from Duke, MEng from Oxford.
- **Rui Zhang:** Founding Product. 10+ years running $100M+ businesses. PE and portfolio company operations at a $12B private equity firm.
- **Eddie Hammond:** Founding Strategy and Ops. 6 years at ODDITY Tech (NASDAQ: ODD) as Sr. Director of BizOps. Led subscriptions program to global scale, launched SpoiledChild from $0 to $250M+ in 3 years. Mechanical Engineering from Penn.

## Investors

- **Keith Adams** (Pebblebed): Founder, Meta AI Research.
- **Pam Vagata** (Pebblebed): Founder, OpenAI.
- **Jordan Tigani**: Founder, MotherDuck.
- **Tristan Handy**: Founder, dbt Labs.
- **Savin Goyal**: Founder, Outerbounds.
- **Adm. William H. McRaven**: 9th Commander, USSOCOM.
- **Jim McKelvey**: Founder, Square/Block.
- **Scott Sandell**: Chairman, NEA.
- **Jack Woodruff**: Founder, Candlestick Capital.

## Security and compliance

SOC 2 Type II: Audited. GDPR: Compliant. CCPA: Compliant. ISO 27001: In progress. Trust Portal: [trust.kepler.ai](https://trust.kepler.ai/)

- **No training on your data:** Your data never trains models other firms use.
- **Strict data isolation and boundary enforcement:** Every query is scoped to your firm, isolation is enforced at the identity layer, and your search index sits in its own namespace.
- **Least-privilege access and controlled permissions:** Every user, process, and service operates with only the access required. Permissions are scoped, logged, and reviewable.
- **Full auditability of system behavior:** Every query, calculation, and data access is logged with complete lineage. Trace any output back to its source.
- **Deterministic and reproducible outputs:** Every number is computed by code, so the same inputs give the same figures, and any result can be reproduced on demand.
- **Designed to withstand scrutiny:** Built for regulated environments where auditors, compliance teams, and regulators require full transparency.

## Research

- [Anthropic S-1 Breakdown](https://kepler.ai/research/financial/s1-anthropic/) (financial research, 2026-10-26): Anthropic's S-1: $16.3B of H1 2026 revenue (up 1,300%), a Q2 operating profit, and why the $80.7B loss is mostly non-cash. Research note and Excel model.
- [Better language models won't fix hallucination. They'll make it quieter.](https://kepler.ai/research/engineering/quieter-hallucination/) (research note, 2026-09-29): Better models make wrong numbers rarer and harder to see. The fix is a provenance layer where the model names facts by reference and never retypes the digits.
- [Stop Paying to Forget](https://kepler.ai/research/engineering/stop-paying-to-forget/) (research note, 2026-09-25): Most document AI pays to reread documents for every question. We read each one once, keep what we read, and answer the next question with ordinary software.
- [Reclassified to Conform](https://kepler.ai/research/engineering/reclassified-to-conform/) (paper, arXiv 2609.38754, 2026-09-22): 194 of 200 companies reshaped a financial statement in ways XBRL tags can't follow. Our paper recovers what changed from the filings' own arithmetic.
- [Oura S-1 Breakdown](https://kepler.ai/research/financial/s1-oura/) (financial research, 2026-09-03): Inside Oura's S-1: revenue up 123% to $908M in FY2025, 5M paid members, positive free cash flow. Research note plus a model traced to the filing.

## Frequently asked questions

**What is Kepler?**
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.

**How is Kepler different from using a frontier model directly?**
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.

**Who is Kepler built for?**
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.

**Does Kepler replace my existing tools?**
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 and Claude, and in your own systems through the Kepler MCP and API.

**What does it mean that every number is traced?**
Click any figure and you land on the exact source it came from, down to the page and line of a filing. Calculated figures show the formula and every input. Ask the same question again and you get the same answer, with the same sources.

**What is the ontology?**
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.

**Which AI model does Kepler use?**
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.

**Can our own agents and tools use Kepler?**
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.

**How does an engagement start?**
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.

**What data does Kepler cover?**
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.

**Can I use Kepler for non-US companies?**
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.

**How does Kepler handle security?**
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.

**Who built Kepler?**
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.

**Is Kepler related to Kepler Cheuvreux, Kpler or the Kepler space telescope?**
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, Kepler Fi, the Kepler space telescope or the astronomer Johannes Kepler.

**How do I see Kepler?**
Request a demo at kepler.ai/demo. We'll walk you through Kepler on the companies and questions your team covers.

## Entity disambiguation

"Kepler" is a common name. Kepler (kepler.ai) builds enterprise intelligence for high-stakes industries. It was founded in 2025 and is headquartered in New York City. It is not related to: Kepler Cheuvreux (European brokerage), Kpler (commodity analytics, kpler.com), Kepler Group (digital marketing), Kepler Communications (satellite company), getkepler.ai (bioinformatics), trykepler.ai (ecommerce), Kepler Fi (institutional investing, keplerfi.com), KeplerAI (no-code AI app builder), the Kepler space telescope, or Johannes Kepler the astronomer.

## Press and mentions

- **Anthropic (April 2026):** "How Kepler built verifiable AI for financial services with Claude." [Read the story](https://claude.com/blog/how-kepler-built-verifiable-ai-for-financial-services-with-claude)
- **Parallel (March 2026):** Kepler uses Parallel's Search API as the discovery layer in its research pipeline. [Read the case study](https://kepler.ai/blog/2026/03/17/how-parallel-helps-kepler-build-trustworthy-ai-for-finance/)
- **Quartr (June 2026):** How Kepler cites earnings answers across the SEC filing, the Quartr transcript, and the IR deck. [Read the post](https://kepler.ai/blog/2026/06/02/how-quartr-helps-kepler-build-citation-grade-earnings-research/)
- **Careers:** Kepler is hiring in New York. Open roles at [jobs.ashbyhq.com/kepler-ai](https://jobs.ashbyhq.com/kepler-ai).

## Blog

- [Deterministic AI: What It Means, Where It Breaks, and Where It Belongs](https://kepler.ai/blog/2026/10/27/deterministic-ai/) (2026-10-27, Umang Lathia): Enterprise AI vendors all promise determinism. Here's what the word actually covers, why everyone is talking about it, and which parts of an AI system should be deterministic.
- [The Compiler That Never Says No](https://kepler.ai/blog/2026/10/06/the-compiler-that-never-says-no/) (2026-10-06, Eddie Hammond): English is now a programming language whose compiler runs unclear instructions as readily as clear ones. Clear writing is the scarcest skill in technical work.
- [Your Mistakes Are a Dataset](https://kepler.ai/blog/2026/10/01/your-mistakes-are-a-dataset/) (2026-10-01, Michelle Van): When a system gets something right for the 10,000th time, you learn little. When it's wrong for the first time, you may have found what nobody knew to test for.
- [Kepler in Claude: A Working Guide for Analysts](https://kepler.ai/blog/2026/09/02/kepler-in-claude/) (2026-09-02, Rui Zhang): Claude does the reasoning and Kepler does the retrieval and arithmetic, with every figure one click from the filing.
- [From Zero Context to Shipping](https://kepler.ai/blog/2026/08/24/from-zero-context-to-shipping/) (2026-08-24, Michelle Van): AI is collapsing the cost of understanding unfamiliar systems. The bottleneck moves to separating what you've observed from what you've inferred.
- [Two Forms of a Table](https://kepler.ai/blog/2026/06/30/two-forms-of-table/) (2026-06-30, Chris Martin): Three frontier models can't agree on the average of one column in a clean table. Why the form you read and the form you compute on differ, and why both matter.
- [The Real Painkiller for Financial Analysts: Kepler on the GameStop-eBay Deal](https://kepler.ai/blog/2026/06/17/painkiller-gamestop-ebay/) (2026-06-17, Rui Zhang): Using the GameStop-eBay bid, we run Kepler through four workflows that give analysts headaches, and show verifiable, accurate analysis in minutes, not days.
- [Elegant and Wrong](https://kepler.ai/blog/2026/06/03/elegant-and-wrong/) (2026-06-03, Susannah Meyer): 49.8% of an S-1's cells are empty. The clean data model you reach for first is a hypothesis the data can reject, and that shaped how we built citations.
- [How Quartr Helps Kepler Build Citation-Grade Earnings Research](https://kepler.ai/blog/2026/06/02/how-quartr-helps-kepler-build-citation-grade-earnings-research/) (2026-06-02, Vinoo Ganesh): How Kepler cites earnings answers across the SEC filing, the Quartr transcript, and the IR deck, with the language model never touching the numbers.
- [Why Data Pipelines Need Stronger Guardrails in the AI Era](https://kepler.ai/blog/2026/05/28/developing-pipelines-faster-in-the-ai-era/) (2026-05-28, Sean Gorsky): AI can help you build faster than ever, but without the right guardrails it can silently erode trust and drive you off a cliff.
- [The Yardstick Problem](https://kepler.ai/blog/2026/05/20/the-yardstick-problem/) (2026-05-20, Sara Kromwijk): You can't unit-test a probability distribution. In our double-entry evals, code grades numbers, an LLM grades prose, and most value is where they disagree.
- [Eagerness solves most problems. Judgement solves the ones we work on.](https://kepler.ai/blog/2026/05/18/eagerness-vs-judgement/) (2026-05-18, Eddie Hammond): Why building infrastructure for AI rewards pattern recognition over effort, and how that has reshaped how Kepler hires and operates.
- [FDEs in the Dark](https://kepler.ai/blog/2026/04/23/fdes-in-the-dark/) (2026-04-23, Vinoo Ganesh): The Forward Deployed Engineer was built to close the gap between software and reality. AI broke that model: an FDE without traceability can't find the answer.
- [How Parallel Helps Kepler Build AI That Finance Professionals Can Actually Trust](https://kepler.ai/blog/2026/03/17/how-parallel-helps-kepler-build-trustworthy-ai-for-finance/) (2026-03-17, Vinoo Ganesh): Kepler uses Parallel's Search API for discovery, pairing AI's grasp of open-ended questions with deterministic infrastructure for accuracy and auditability.
- [The Context Wall](https://kepler.ai/blog/2026/03/05/the-context-wall/) (2026-03-05, Sara Kromwijk): Source-cited chat hits four walls: latency, cost, prompt-following degradation, and a context limit. Solving them meant rethinking what a conversation is.
- [Equity Research in the Age of AI: What Actually Needs to Change](https://kepler.ai/blog/2026/02/27/equity-research-in-the-age-of-ai/) (2026-02-27, Luca Daniele Vailati): Equity research is built on precision. AI doesn't need to replace judgment; it needs to remove operational drag and keep every number verifiably correct.
- [Context Is the Easy Part](https://kepler.ai/blog/2026/02/13/context-is-the-easy-part/) (2026-02-13, Vinoo Ganesh): Everyone's talking about context engineering. Context engineering isn't a context problem. It's an engineering problem.
- [Trust in the Age of AI](https://kepler.ai/blog/2026/02/10/trust-in-the-age-of-ai/) (2026-02-10, Sara Kromwijk): What does it mean to trust AI? Accuracy, security, and credulity are three related challenges, and forcing AI to show its work changes the output itself.
- [The AI Era of Finance: A Practitioner's View](https://kepler.ai/blog/2026/02/06/the-ai-era-of-finance/) (2026-02-06, Rui Zhang): LLMs excel at summarizing but struggle with the one thing finance can’t compromise on: numerical truth. Without traceability, AI risks accelerating errors.
- [Why I Joined Kepler](https://kepler.ai/blog/2026/02/03/why-i-joined-kepler/) (2026-02-03, Susannah Meyer): After Meta and four seed-stage startups, Susannah, Kepler’s Founding Engineer, shares why building fully traceable AI was a challenge she couldn’t pass up.
- [Introducing Kepler](https://kepler.ai/blog/2026/01/30/introducing-kepler/) (2026-01-30, Vinoo Ganesh): We're building the infrastructure that gives AI a foundation of verified data, traceable to its source and precise enough to follow wherever it leads.

## Links

- [Website](https://kepler.ai/)
- [The Kepler Platform](https://kepler.ai/platform/)
- [Kepler for Finance](https://kepler.ai/finance/)
- [Kepler for Industry](https://kepler.ai/industry/)
- [Build on Kepler](https://kepler.ai/build/)
- [Compare](https://kepler.ai/compare/)
- [The Ontology](https://kepler.ai/ontology/)
- [Security](https://kepler.ai/security/)
- [Research](https://kepler.ai/research/)
- [Blog](https://kepler.ai/blog/)
- [FAQ](https://kepler.ai/faq/)
- [About](https://kepler.ai/about/)
- [Careers](https://kepler.ai/careers/)
- [Request Demo](https://kepler.ai/demo/)
- [System Status](https://kepler.ai/status/)
- [Trust Portal](https://trust.kepler.ai/)
- [Privacy Policy](https://kepler.ai/privacy/)
- [Contact](mailto:hi@kepler.ai)