Kepler vs Rogo

For hedge funds, private equity and credit funds, asset managers and allocators that need AI on their own data, with numbers they can defend, Kepler is the stronger choice. We build a living model of your firm, read every document you hold, and compute every number in code with a citation to the line. Rogo is built for a sell-side team whose day is pitch books produced from licensed data and firm files.

We build Kepler. Facts about Rogo come from Rogo’s own site and public reporting as of November 2026, linked inline.

The short version

• Kepler is enterprise intelligence for investment firms. Our engineers build your firm’s ontology from your own documents and systems, extraction models read every page before you ask, and a deterministic engine computes each figure with its formula and sources.
• Rogo is an AI platform built by former bankers and investors. Felix, its agent, turns a prompt into client-ready decks, models and documents, and its 51 integrations put S&P Capital IQ, FactSet, LSEG, PitchBook and other feeds behind it.
• The choice comes down to what the AI should know best: the market data a banker packages, or your firm, its holdings and its definitions, with numbers you can defend.

Kepler vs Rogo side by side

	Kepler	Rogo
Built around	Your firm’s ontology: one record per fund, company, manager and deal	Felix and agents that produce banker deliverables
Where each number comes from	Pulled from the source and computed in code; the model never writes one	Felix builds models from connected data sources
What a citation opens	The exact page, line or cell, checked before you see it	Linked sources; fine-grained citations in data rooms, per Rogo
Your firm’s definitions	Written into your ontology and applied in every answer	Rogo Intelligence and agent templates capture firm conventions
Your documents	Every page read in full at ingest, scans and tables included	20+ firm-system integrations; data rooms through Rogo Deal Room
Public and licensed data	26M+ SEC filings with XBRL, 40M+ IR documents and transcripts, market data, news	51 integrations, including S&P Capital IQ, FactSet, LSEG, PitchBook, Preqin
Complete lists	Every matching document across the whole firm	Queries across connected files and data rooms
Same question twice	The same number from the same sources	Ask in a trial
As filed vs as restated	Either, on any date	Ask in a trial
Work that runs on its own	Agents on schedules and events, acting as their owner	Agents triggered by news, files, earnings and email
Models	Small models we train read; frontier models reason and write	Model Broker routes tasks across providers
Where it works	App, Excel, PowerPoint, Word, Claude, ChatGPT, Slack, email	Rogo app, Excel add-in, Copilot in Excel, email
Build on it	API, MCP server, Python SDK, webhooks	Custom agents; connect your own MCP servers
Permissions and audit	Your permissions mirrored on every read; every query and calculation logged; SOC 2 Type II; our cloud or yours	Watchtower governs spend and permissions

How Rogo works

Rogo’s homepage pitches “AI for the most ambitious firms in finance.” Felix, its agent, takes a prompt or an email and returns a finished deck, model or document; the May 2026 update added a native Excel add-in, memory of each user’s conventions, and autonomous agents set up by email. The agents page shows pre-built workflows that draft a CIM, a management presentation and a comparable companies analysis, and proactive agents that fire on news, data-room files, earnings and emails.

Rogo has expanded through acquisitions: Subset for spreadsheet agents (2025), then Offset, Plux, Rivanna for data-room diligence and Arvo for meetings (2026). Rivanna now strengthens Rogo Deal Room, launched in August 2026 to give each deal one governed home where agents update models and diligence responses as new information arrives. Rogo Intelligence stores a firm’s expertise as reusable entities, Model Broker routes each task to a suitable model, and Watchtower governs spend and permissions. Rogo says it never trains on customer data (Rogo security), and Sacra estimates its pricing at about $3,300 per seat per year. For Rogo next to a frontier assistant, see Rogo vs Claude.

What users say: a second-year M&A analyst on Wall Street Oasis wrote that Rogo “still makes so many errors, you literally tell it to extract specific info of a deal from a link, and it will take the wrong date, wrong EV etc.”

How Kepler works

A frontier model on its own has two gaps an investment firm can’t work around. It writes numbers instead of pulling them, so it can hallucinate and you can’t tell when. And it knows nothing about your firm: your funds, your portfolio, your adjusted EBITDA, your view.

We built Kepler to close both, and our engineers built this layer before at Palantir. They work on site with your team as forward-deployed engineers. Public data is ready on day one: 26M+ SEC filings across 20+ form types, 40M+ investor relations documents and earnings call transcripts, market data and news, for 14,000+ companies in 120+ countries and 30+ languages. In the first week we connect your sources read-only (SharePoint, Box, Egnyte, Outlook, Slack, the CRM, data rooms, research notes and models) and our extraction models read every page, keeping each fact with its value, scale and unit beside the passage it came from.

From that we build your firm’s ontology, which we call Kepler Orbit: every company, fund, vehicle, manager, deal and person resolves to one record, every link cites its sentence, and it knows time, so it can answer as filed, as restated or as of any date. It keeps itself current as documents arrive.

Numbers come from the engine. Code pulls and computes every figure; financial statements assemble from the filings’ own XBRL with no model in the path; every calculation shows its formula and cited inputs; and every citation is checked against its source while the answer is written. Model choice is our job: we train compact models for extraction, call frontier models for reasoning and prose, and move each task to a better model as soon as one ships.

On top of that run your playbooks and your agents. The IC memo in house style, the earnings update the way your PM likes it, comps on your own adjusted EBITDA, all saved once. Agents work overnight on schedules and events, acting exactly as their owner and never steered by what they read. None of it requires leaving the tools your team already opens: the Kepler app, Office, Claude, ChatGPT, Slack and email all reach the same ontology and engine, and so does the Kepler API and MCP server. Kepler is SOC 2 Type II audited, and you choose our cloud or yours. More on the stack is on our platform page.

“I can’t put a number in front of a client if I can’t show where it came from.” (Managing Director, Multi-billion dollar fund)

Not the right fit if: you run a sell-side coverage team whose output is pitch books and CIMs formatted to bank templates.

The same request, two ways

Take a request both products would accept: “Trading comps for US cybersecurity, EV/EBITDA on each company’s own adjusted EBITDA, last twelve months. Flag anyone who restated, and show which of these we own, by fund.”

In Rogo, a comparable companies agent and Felix assemble the analysis from connected data, such as S&P Capital IQ, FactSet or Daloopa, into an Excel file formatted to the team’s template. In a trial, check whose adjusted EBITDA each multiple uses (the company’s own reconciliation or a standardized vendor figure), what a click on a cell opens, and how the agent treats a restated quarter. Rogo would need the fund’s holdings connected to answer the last part.

In Kepler, the peer set resolves through the ontology, so each company is one record with its tickers, subsidiaries and filings attached. Each company’s adjusted EBITDA comes from the non-GAAP reconciliation in its own earnings release (furnished as Exhibit 99.1 to the 8-K), summed over the last four quarters in code, each quarter cited to the line. Enterprise value builds in code from share count, price, debt and cash, each input cited. Restatements surface because we hold every figure as filed and as restated; our paper Reclassified to Conform found 194 of 200 companies reshaping statements in ways XBRL tags alone can’t follow. The ontology already links each company to the funds that hold it, so the last question is a lookup. The sheet opens in Excel with every cell traceable, and an agent refreshes it the hour a peer reports. Our guide to building trading comps with AI walks through each step.

When to choose Kepler

• Your firm is on the buy side: a hedge fund, PE or credit fund, asset manager, LP, fund-of-funds or family office.
• Numbers go to an investment committee, a client or LPs, and every one has to trace to its source line with the formula shown.
• Your firm defines its own metrics (adjusted EBITDA, net IRR, peer sets) and wants them applied the same way in every answer.
• You need complete lists from your own documents: every side letter with an MFN clause, every credit agreement with a change-of-control trigger.
• Your knowledge lives in SharePoint, data rooms, the CRM and email, and you want one model of the firm across all of it.
• You want agents watching the book overnight: new filings, guidance changes, capital call notices and documents landing in a data room.
• You care whether a figure is as filed or as restated, and on which date.
• Your analysts already work in Claude or ChatGPT and you want cited numbers inside those conversations.
• Your engineers want to build their own agents and apps on Kepler’s data, ontology and engine through an API and SDK, instead of spending 17 months rebuilding a data platform.
• You want engineers on site who built this layer before at Palantir, starting in week one.
• Compliance needs one access check on every read, by a person or an agent, with every query and calculation logged (security).

When Rogo may fit

• A sell-side banking team whose daily output is pitch books, CIMs and comps built on licensed data, formatted to bank templates.
• A bank that wants a single vendor to carry many licensed feeds (S&P Capital IQ, FactSet, LSEG, PitchBook) behind one agent.

Next steps

If you’re comparing more than two tools, our Rogo alternatives guide covers seven, Rogo vs Hebbia compares the two most-searched banker and document tools, and Kepler vs Hebbia covers the document-grid approach. The best AI tools for investment banking covers the sell-side field. For the category itself, read what enterprise intelligence is, and for a broader set of comparisons see compare.

Ask Kepler a question on your own coverage at app.kepler.ai, or book a demo and we’ll build the comps above on your peer set.

Frequently asked questions

What is the difference between Kepler and Rogo?

Kepler is enterprise intelligence for the buy side: we model your firm as an ontology built from its own documents and systems, read every page before anyone asks, and produce every figure in code, cited to its page, line or cell. Rogo is built around banker deliverables: its agent, Felix, turns a prompt into decks, models and documents using licensed data and firm files.

Is Kepler or Rogo better for hedge funds and private equity?

Kepler. It is built for the buy side: portfolio monitoring, data-room diligence where every document is read, IC memos in house style, covenant tracking and look-through exposure, all on the firm's own definitions with every number computed and cited. Rogo is strongest for banking teams producing pitch materials, CIMs and comps from licensed data.

Does Kepler work in Excel like Rogo?

Yes, and beyond Excel. Analysts use Kepler in Excel, PowerPoint and Word, from Claude or ChatGPT, and in Slack or email; engineers reach it through the API, MCP server, Python SDK and webhooks. Every figure Kepler fills into a workbook traces to its source line, and calculated cells show their formula and cited inputs. Rogo offers an Excel add-in for Felix.

Can Kepler work on my firm's own documents and data rooms?

Yes. Our forward-deployed engineers connect SharePoint, OneDrive, Box, Egnyte, Google Drive, Outlook, Slack, your CRM and your data rooms read-only, read every document in full, and build your firm's ontology in the first week. Permissions mirror your own systems, every access is logged, and your data never trains models other firms use.

Does Kepler have licensed data like Rogo?

Kepler ships with more than 26 million SEC filings across 20+ form types, more than 40 million investor relations documents and earnings call transcripts, market data and news, covering 14,000+ companies in 120+ countries and 30+ languages. Financial statements build from the filings' own XBRL. Your firm's documents and systems connect alongside that public data.