AI that proves it's right.
Kepler is verifiable AI for finance. Every number is traced to its filing, page, and line item, and you get the same answer every time you ask.
26M+ SEC filings14,000+ companies120+ countries30+ languages
Revenue grew 15.1% to $402.8B. Operating margin was 32.0%, versus 32.1% a year earlier.
AI that's probablyprovably right.
Every AI platform gives you its best guess. For the decisions that define careers, move markets, and stand up in court, guessing isn't good enough.
Built for the people who
have to defend the number
“Our analysts used to spend more time verifying AI outputs than generating them. With Kepler, every number is already traced to source. They focus on insight, not fact-checking.”
“I love there's no black box with Kepler. I can see exactly where every number and conclusion comes from, which means I can defend the work.”
“I can't put a number in front of a client if I can't show where it came from.”
Why finance teams
choose Kepler
Alphabet's revenue was $402.8B in FY2025.
metric: operating_margin company: Alphabet Inc. (GOOGL) period: FY2025
Operating margin was 32.0% in FY2025.
Operating margin was 32.0% in FY2025.
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Every number, traced to source
Click any figure to see the filing, page, and line item it came from.
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AI handles the language. Code handles the data.
Deterministic code retrieves the data and runs the math, so a model never guesses a number.
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Same question, same answer
Ask twice and you get the same number with the same citation, reproducible on demand.
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Your rules, encoded permanently
Correct a treatment once and it sticks for the analyst, the desk, and the firm.
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Works where you already work
Use Kepler as an add-in in Excel and PowerPoint, as a verified connector inside Claude, or through our API from your own agents and systems.
Everyone else has a pitch.
We have a stack.
Every AI vendor in finance says accurate, trusted, verified. Those are adjectives. This is the architecture: seven layers from the filing to your screen, and not one of them trusts a model with a number.
Primary sources
We read the filing. Not a summary of it.
Everyone else retrieves chunks of whatever text they scraped.
Kepler captures every source whole: each SEC filing with its full text and XBRL, earnings call transcripts, investor presentations, market data, and your own documents. Nothing downstream works from a paraphrase.
How it's built- SEC filings stored complete: primary document plus XBRL
- Earnings call transcripts, investor presentations, and market data
- Your own documents from your firm's document stores, or by upload
Ontology
We know who every filing is about.
Everyone else matches strings and hopes it's the right company.
Companies, subsidiaries, funds, people, and filers are resolved into one graph. Every relationship in it is a claim: one filer's statement, with the verbatim sentence attached.
How it's built- Names resolved to SEC company records: NVDA, NVIDIA, and NVIDIA CORP are one company
- Every relationship carries its filer and source sentence, checked before it counts
- Relationship types follow FIBO, the financial industry's standard ontology
Deterministic engine
Code does the math. Every time.
Everyone else lets a language model do arithmetic.
Financial statements are assembled from the filings' own XBRL with no model in the path. Every calculation runs as code over cited inputs and produces a derived citation that carries its formula.
How it's built- Every displayed number is a value a cited filing reported, or a sum of them
- As-filed or as-restated, with point-in-time history for any period
- Ratios, growth, and multiples computed in code, never by the model
Citations & verification
No receipt, no number.
Everyone else attaches a link and calls it a citation.
Every number is written with its source attached and checked against that source while the answer streams. A citation that doesn't match is stripped before you ever see it.
How it's built- Fourteen source types: filings, exhibits, transcripts, slides, workbooks, your documents
- Anchored to the exact XBRL fact, cell, or verbatim sentence
- Quotes must match the source word for word, or they are dropped
Agent runtime
A plan, not a prompt loop.
Everyone else lets an agent improvise until it stops.
Every request starts with a plan. Agents run as state machines that unlock only the tools each step needs, and specialist agents read filings, transcripts, exhibits, and decks in parallel.
How it's built- Plan, research, execute, present: tools gated at every step
- Specialists for 10-Ks, transcripts, exhibits, investor decks, workbooks
- Search over each firm's own index; regression-tested against baselines
Skills & rules
Your definitions, enforced in code.
Everyone else treats your instructions as a suggestion.
Built-in skills encode how finance work is done. Your desk writes its own, and Kepler compiles, reviews, and guards each one, so it can change a definition but never the rules of evidence.
How it's built- Built-in skills for adjusted EBITDA, enterprise value, fully diluted shares
- Write a skill from a short brief; it is compiled, reviewed, then applied
- Every skill records who authored it: you, or your firm
Surfaces
One engine, wherever you work.
Everyone else ships a chat box and calls it a platform.
The Kepler app for research, workbooks, and company pages, a verified connector that brings the same engine, with the same receipts, into Claude, and an API for your own agents and systems.
How it's built- Chat, workbooks, and a citation sidebar in the Kepler app
- A verified connector for Claude, with secure sign-in
- Workbooks export to Excel with formulas and sources intact
Security & tenancy
Isolation by architecture, not by policy.
Everyone else promises it in a PDF.
Each firm's work is scoped to its organization, market data and customer data live in separate stores, and the systems that run agents are never exposed to the internet.
How it's built- Org-scoped projects and per-firm search indexes
- Customer data and market data kept in separate databases
- Execution runs on a private network behind the API
From question to
defensible deliverable
Building Google's 3-statement historical model now.
- Identify Alphabet / Google
- Build 3-statement model (last 3 fiscal years)
Alphabet's 3-statement model is ready: Income Statement, Balance Sheet, and Cash Flow Statement across FY2023, FY2024, and FY2025, each cell cited to its source 10-K filing.
The first public equities ontology,
built from source data
Kepler reads SEC filings and extracts the companies, subsidiaries, filers, and relationships they disclose, then links each one back to the documents it came from. Ask about NVIDIA and Kepler already knows that NVDA, NVIDIA, and NVIDIA CORP are the same company, who discloses it, and what it owns.
Explore the ontologyBuilt for regulated finance,
secure by design
- No training on your data
- Strict data isolation and boundary enforcement
- Least-privilege access and controlled permissions
- Full auditability of system behavior
- Deterministic and reproducible outputs
- Designed to withstand scrutiny