Rogo vs Hebbia: A Buyer's Comparison

Rogo and Hebbia both sell AI to finance teams, and they start from opposite ends. Rogo is built around the banker’s finished file: its agent, Felix, turns a prompt into decks, models and documents on top of a long list of licensed data. Hebbia is built around documents: its Matrix runs your questions across hundreds of files at once and now carries the analysis through to a memo or model. Choose Rogo for banking output on licensed data and Hebbia for document-heavy diligence. If your numbers end up in front of an investment committee or LPs, the stronger choice is Kepler: AI built around your own firm that computes every figure in code and cites it to the line.

We build Kepler, which is that third option, so we compare Rogo and Hebbia only from their own published materials and public reporting as of November 2026, linked inline, and keep Kepler in a separate section.

Rogo vs Hebbia at a glance

	Rogo	Hebbia
Starting point	Agents that produce banker deliverables	A grid that runs questions across documents
Flagship products	Felix (agent), Agent Library, Rogo Intelligence, Rogo Deal Room	Matrix and Matrix 2.0, Max (AI team member)
Who it says it serves	Investment banks, asset managers, PE firms	Investors, bankers, lawyers, consultants, corporates
Licensed data	51 integrations, including S&P Capital IQ, FactSet, LSEG, PitchBook, Preqin, Daloopa, Dow Jones	FactSet, S&P, PitchBook, Preqin, Daloopa, ICE, Fitch, Octus, Third Bridge, Guidepoint
Data rooms	Datasite, Intralinks, Ideals, Entropia	Datasite, Intralinks
Office	Excel add-in; Copilot in Excel launch partner	Excel and Word plug-ins; cell citations in Excel
Building on it	Custom agents; connect any MCP server	API and MCP server (Headless Hebbia)
Models	Model Broker routes each task across providers	Frontier models including Claude Opus 5 and GPT-5.6
Reported price	About $3,300 per seat per year (Sacra)	$10,000 Professional, $3,000 to $3,500 Lite (Sacra)

How Rogo works

Rogo was built by former bankers and investors, and it reads that way. Felix, its agent for finance, takes a single prompt (or an email) and returns a client-ready deck, model or document, and the Agent Library holds hundreds of pre-built workflows; the examples on Rogo’s agents page draft a CIM, a management presentation and a comps analysis. A native Excel add-in puts Felix in the workbook, and Rogo’s May 2026 update added memory, a slide annotator and autonomous agents that users set up by email.

Its other strength is data. The integrations page lists 51 sources, from S&P Capital IQ, FactSet, LSEG, Morningstar and Quartr to PitchBook, Preqin, Gain, Moody’s Orbis, Fitch, Octus, Revelio and two newswires, plus firm systems such as SharePoint, DealCloud, Salesforce, Slack and Granola. In 2026 Rogo acquired Offset (model updating), Plux (European coverage), Rivanna (data-room diligence) and Arvo (a meeting assistant), launched Rogo Deal Room in August as a governed home for each transaction, and introduced Rogo Intelligence, which it calls the firm’s context layer. Our Rogo vs Claude page sets Rogo beside Anthropic’s assistant.

What users say: “The more I’ve used Rogo though the worse it’s seemed to get,” a banker who uses it wrote on Wall Street Oasis, describing a tool that “wildly overcomplicates tasks and asks.”

How Hebbia works

Hebbia, founded in 2020, made its name with Matrix: documents in rows, questions in columns, and an agent filling each cell with a cited answer, so a team working through a data room sees every file’s answer to the same question in one column.

Matrix 2.0 (August 26, 2026) turns a reviewed grid into a finished model, memo, deck or email, and a person signs off before each next step. Setup is hands-on: Hebbia’s former investors and bankers index the firm’s deal history and processes. Max, launched in July 2026, is a chat-based AI team member that works over email and, since September 2026, keeps memory across conversations (Hebbia, October 2026). Headless Hebbia (October 1, 2026) exposes Hebbia through an API and an MCP server, and its September release notes added Claude Opus 5 and GPT-5.6 to the models available in Hebbia. Our Hebbia vs ChatGPT page compares it with a general assistant.

Where they differ

Deliverables or documents

Rogo optimizes for the output a banker sends: the deck, the model, the profile. Hebbia optimizes for reading a large set of files the same way, then turning the grid into a deliverable. A coverage banker building a pitch is closer to Rogo’s design; a deal team working through a data room is closer to Hebbia’s.

Data

Rogo’s integration list is longer on licensed market and company data. On filings, Hebbia lists the US, Europe, the UK, Canada, Australia and New Zealand, and Rogo lists SEC filings, UK Companies House, Japan’s EDINET and other international filings. Hebbia says some partner sources connect through your own subscription (Hebbia). Both reach SharePoint, Box, Egnyte, DealCloud, Salesforce, Datasite and Intralinks.

Citations

Each vendor describes its own. Hebbia says Matrix shows where every number came from, and its Excel plug-in puts citations on individual cells as Office notes. Rogo says Rivanna lets teams query tens of thousands of files with fine-grained citations (Rogo). Hebbia’s own comparison claims sentence-level citations for Hebbia and response-level for Rogo. Neither has published an independent test, so click through ten citations on your own documents in a trial.

Agents and automation

Rogo’s agents respond to triggers such as breaking news, data-room files, earnings and emails, and Felix runs scheduled tasks set up by email. Hebbia’s Matrix columns can now run only when a prior column meets a condition, and Max can schedule an agent to run once on a future date (Hebbia, October 2026).

Pricing

Neither publishes prices. Sacra estimates Rogo at about $3,300 per seat per year on multi-year contracts. Sacra reports Hebbia seats at $10,000 (Professional) and $3,000 to $3,500 (Lite). Both add licensed data such as FactSet or PitchBook, which can require your firm’s own subscription with that provider.

What users say about both

Forum reviews of the two tend to land in the same place. “Much much easier with tools like Rogo/Hebbia to get up to speed on a space or a particular deal if you’ve been away for a bit, but I don’t think it really moves the needle because it doesn’t actually produce anything I can submit to a client or a partner,” an investment banking analyst wrote on Wall Street Oasis in 2025, before Matrix 2.0 and Rogo Deal Room shipped. Both vendors have since added deliverables, so test whether today’s output is ready for a client without a rewrite.

Who each one is for

Rogo fits investment banks and advisory teams that turn licensed data into pitch materials, CIMs and comps every day, and want one vendor to carry the data feeds, the agent and the Excel workflow.

Hebbia fits PE deal teams, credit investors and research teams that work through large sets of private documents and want each answer laid out in a grid they can review before it becomes a memo.

Reading the vendors’ own comparisons

Many comparison pages in this category are written by a vendor, including Hebbia’s own Hebbia vs Rogo page (July 2026). It is detailed and useful, and it is a vendor document. It says Hebbia has no native FactSet integration, while FactSet appears on Hebbia’s own integrations page. It also says Rogo has no API or MCP support, while Rogo’s May 2026 update added custom MCP so teams can bring their own MCP server. Products here change monthly, so check each claim against the vendor’s latest release notes.

The third option: Kepler

We build Kepler.

Rogo and Hebbia both put frontier models to work on market data and documents, and each now keeps some of a firm’s context. We start from the firm: we build its own data model before anyone asks a question, and we split the work so that models read and write while code produces every number. For a buyer choosing between the two, Kepler changes three things: what the AI knows about your firm, whether a list is complete, and whether a number will hold up when an investment committee asks where it came from.

Our forward-deployed engineers built this layer before at Palantir. In the first week they connect your documents, CRM, email and data rooms read-only and build your firm’s ontology: one living model where each company, fund, vehicle, manager and deal is a single record, your definitions of adjusted EBITDA or net IRR are written down once, and every relationship cites the sentence it came from. Extraction models we train read every page at ingest, so lists are complete across the whole firm. Public data is already loaded: 26M+ SEC filings, 40M+ investor relations documents and earnings call transcripts, market data and news across 14,000+ companies in 120+ countries.

Then code does the arithmetic. Every number is pulled from its source and computed in code, statements build from XBRL as filed or as restated, and each figure clicks through to its page, line or cell. Citations are checked while the answer is written. We route each task to the model best suited to it. The firm’s playbooks (the IC memo in house style, comps on your own adjusted EBITDA) are saved once and applied every time. One access check governs every read by a person or an agent, every query is logged, and we hold SOC 2 Type II and deploy in our cloud or yours.

Agents run on schedules and events, such as a new 10-Q or a capital call notice, act exactly as the person who set them up, and never take instructions from what they read. Analysts get the results in Excel, PowerPoint, Word, Claude, ChatGPT, Slack and email, and engineers build their own tools on the same ontology and engine through the API, MCP server and Python SDK.

Here is what Kepler does with the work Rogo and Hebbia are usually bought for:

• Diligence that carries forward. A PE team asks for every customer contract in the data room with a change-of-control clause and gets the full list, each clause cited. The answers stay in the ontology, linked to the company, so the portfolio review and the next deal start with them.
• Comps you can defend. EV/EBITDA on each company’s own adjusted EBITDA, every multiple traced to a line item, and the same figure on every run.
• Monitoring nobody has to start. Covenant headroom recomputed from each new compliance certificate, and look-through exposure across every vehicle updated the day a quarterly report lands, each input cited to its statement.

Our extraction models read 99.4% of tagged facts at the right scale across 40 held-out filings (Stop Paying to Forget), and our CEO, Vinoo Ganesh, spent seven years at Palantir and was Citadel’s first Head of Business Engineering.

Not the right fit if: your desk wants a terminal for live prices, market chat and order flow; Kepler is built for the research and decisions that sit behind those trades.

	Rogo	Hebbia	Kepler
Built around	Banker deliverables	Document grids	Your firm’s ontology
Your firm’s definitions	Rogo Intelligence and templates	Skills and indexed deal history	Written into the ontology, applied in every answer
How a question is answered	An agent works over connected sources	A model answers each cell of the grid	A lookup over facts read at ingest
Where numbers come from	Agents build models from connected data	Model answers in cells, with citations	Pulled from source, computed in code
What a citation opens	Linked sources, per Rogo	The source sentence, per Hebbia	The exact page, line or cell, checked before you see it
Work that starts on its own	Triggered and scheduled agents	Conditional columns, one-time scheduled runs	Agents on schedules and events, acting as their owner
Models	Model Broker routes across providers	Frontier models such as Claude Opus 5 and GPT-5.6	Small models we train read; frontier models reason and write
Permissions and audit	Watchtower governs models, spend and permissions	Access set by role, team, project and artifact, per Hebbia	One access check on every read; every query and calculation logged
Where it works	App, Excel, email (per Rogo)	App, Excel, Word, API, MCP	App, Excel, PowerPoint, Word, Claude, ChatGPT, Slack, API, MCP, SDK
Main buyers	Banks first, plus asset managers and PE	Funds, banks, law firms, corporates	Hedge funds, PE, credit, asset managers, allocators

See the full comparisons in Kepler vs Rogo and Kepler vs Hebbia.

How to decide

1. Name the output. If the job ends in a pitch book, start with Rogo. If it ends in a diligence grid, start with Hebbia. If it ends in a number an investment committee or an LP will question, start with Kepler.
2. Test completeness. Ask each tool for every document in a set that matches a clause, and count against your own records.
3. Test reproducibility. Ask for the same figure twice, a day apart.
4. Test your definitions. Ask for a metric the way your firm defines it.

For the wider field, see Rogo alternatives, Hebbia alternatives, AlphaSense vs Rogo and the best AI tools for investment research.

Put your own Rogo-or-Hebbia test question to Kepler at app.kepler.ai, or book a demo and we’ll answer it from your firm’s documents.

Frequently asked questions

What is the difference between Rogo and Hebbia?

Rogo is built around the banker's finished work: its agent, Felix, turns a prompt into decks, models and documents using 51 integrations, many of them licensed data. Hebbia is built around documents: Matrix runs a set of questions across hundreds of files, and Matrix 2.0 carries that analysis through to memos, decks and models. Rogo leans toward banking; Hebbia toward document-heavy diligence. If the numbers end up in front of an investment committee or LPs, the stronger choice is Kepler, which computes every figure in code and cites it to the line.

Is Rogo or Hebbia better for private equity?

For data-room diligence, Hebbia's Matrix was built for the job, and Rogo moved in with Rogo Deal Room and its Rivanna acquisition in August 2026. Both connect Datasite and Intralinks. PE firms that also want portfolio monitoring, IC memos in house style and numbers computed in code across every deal the firm has done should look at Kepler, which is built for that whole job.

How much do Rogo and Hebbia cost?

Neither publishes prices. Sacra estimates Rogo at about $3,300 per seat per year on multi-year contracts. Sacra reports Hebbia Professional seats at $10,000 per year and Lite seats at $3,000 to $3,500. Both add licensed data such as FactSet or PitchBook, which can require your firm's own subscription.

Which has better citations, Rogo or Hebbia?

Each describes its own. Hebbia says Matrix shows where every number came from, and its Excel plug-in puts citations on individual cells. Rogo says its Rivanna acquisition lets teams query tens of thousands of data-room files with fine-grained citations. Neither has published an independent test, so click through ten citations on your own documents in a trial and check the period and units on each.

Is there an alternative to both Rogo and Hebbia?

Yes, Kepler. Where Rogo starts from the deliverable and Hebbia from the document set, we start from the firm itself: our engineers build your firm's ontology from your documents and systems, we read every document in full at ingest, and code computes every number with a citation to the exact line. Agents act on new filings and documents as they arrive, and the same answers reach Excel, PowerPoint, Claude, ChatGPT and your own systems through an API.