Rogo Alternatives for Investment Firms

For hedge funds, private equity and credit funds, asset managers and allocators, the best Rogo alternative is Kepler: AI built around your own firm, where our engineers build your firm’s ontology, every document is read in full before you ask, and every number is computed in code and cited to the line. Rogo is built around the banker’s finished file; Kepler is built around your holdings, your definitions and figures an investment committee can trace.

Rogo is a serious product for bankers. The testimonials on its homepage come from investment banks, and the example agents on its site draft a CIM, a management presentation and a comparable companies analysis. A fund’s work looks different: portfolio monitoring, covenant tracking, look-through exposure, thesis tracking, and numbers an investment committee will pick apart.

We build Kepler, so it’s listed first and labeled. Everything we say about the other seven comes from their own sites, launch posts and public reporting as of November 2026, linked where it appears, and prices appear only where a vendor or a third party has published them.

Why firms look for a Rogo alternative

• The work is on the buy side. Rogo says it serves investment banks, asset managers and PE firms (Rogo, April 2026), and it offers agents for PE and equity research. Its product still grew up around the banker’s deliverable. Funds want an AI organized around their holdings, vehicles, managers and deals.
• The firm’s own definitions. A fund’s adjusted EBITDA, net IRR, peer sets and internal code names rarely match a data vendor’s standardized fields. Teams want those definitions applied the same way in every answer, without re-explaining them. What users say: one Wall Street Oasis user at a bank put it this way: “its a lot of coaching, only for it to miss a simple thing, or not present it in a way I want (despite asking explicitly)” (Wall Street Oasis).
• Numbers that reproduce. When a figure goes into an IC memo or an LP letter, the analyst needs to see the formula and the source line, and get the same figure if the question is asked again next week.
• Complete answers from private documents. “Every side letter with an MFN clause” needs every side letter, so the tool has to have read all of them.
• Cost visibility. Rogo doesn’t publish prices. Sacra estimates about $3,300 per seat per year on multi-year enterprise contracts, with specialized data access and paid features on top.

A note on other “Rogo alternatives” lists

Hebbia’s own Top 8 Rogo Competitors (October 2026) ranks for this search, V7 publishes its own list, and F2’s F2 vs Rogo (March 2026) frames the choice as buy-side underwriting against sell-side research. Each vendor appears on the list it wrote, and we appear on ours, so check every claim against the vendor’s latest announcements and its date. One example: Hebbia’s Hebbia vs Rogo page (July 2026) says Rogo offers pre-built agents only, with no custom workflows, while Rogo’s May 2026 update describes agents that teams build around their own templates and standards.

Rogo alternatives at a glance

Tool	Best for	Data it brings	How numbers are produced
Kepler	Buy-side firms that want AI built around their own data	SEC filings, IR documents, transcripts, market data, news; your documents, CRM and data rooms, resolved into your ontology	Pulled from the source and computed in code, cited to the line
Hebbia	Questions across large document sets	Filings and transcripts; FactSet, S&P, PitchBook, Preqin, some via your licenses	A model answers each Matrix cell, with citations
AlphaSense	Broker research and expert calls	500M+ documents, 300,000 expert interviews	AI search and agents over the library
Claude for Financial Services	A frontier assistant on data you license	Connectors to FactSet, S&P, PitchBook, LSEG, Moody’s and more	Model-written, linked to sources
ChatGPT for Financial Services	Firms standardizing on OpenAI	SEC filings, Quartr, PitchBook Essentials, LSEG News	Model-written, passage highlighted
V7 Go	Extraction from CIMs and data rooms	Your documents; 300+ integrations	Fields extracted per document
Datasite (Blueflame)	Deal teams already in Datasite	Data-room content, Grata market data	Agent answers with citations
F2	Private credit underwriting	Data rooms, borrower models and past deals; FactSet and PitchBook	An agent works in live Excel workbooks, per F2

The best Rogo alternatives

1. Kepler (our pick)

We build Kepler.

Best for: buy-side teams, from hedge funds, PE and credit funds to asset managers and allocators, whose AI has to know their own holdings and definitions and prove every figure it produces.

Frontier models improve every few months, and two problems stay put for an investment firm. A model writes numbers instead of pulling them, so it can hallucinate a figure and nothing on the page tells you which one. And every firm gets the same model, which knows nothing about your funds, your portfolio companies or how your team defines adjusted EBITDA.

We built Kepler to fix both. Our forward-deployed engineers built this layer before at Palantir. In the first week they connect your SharePoint, data rooms, CRM, email and research notes read-only and build your firm’s ontology: one living model in which every company, fund, manager, deal and document resolves to a single record, and every relationship points to the sentence that supports it. Public data arrives already loaded: 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. Extraction models we train read every page at ingest, scanned pages and tables included, so a request for every agreement with a change-of-control clause comes back as the full list.

Code pulls and computes every number, and the model never writes one. Financial statements assemble from each filing’s own XBRL, as filed or as restated, on any date. Every figure links to the page, line or cell it came from, and a calculated figure opens to its formula and each cited input. Rerun a question next month and the figure and its sources come back unchanged.

Why it’s our pick for teams leaving Rogo

• Your definitions, everywhere. Adjusted EBITDA, net IRR and internal code names carry one meaning in every answer, model and memo.
• Every document read before you ask. Lists are complete, and a follow-up question is a lookup.
• Citations checked while the answer is written. A citation that doesn’t match its source is stripped before you see it, and when two sources disagree you see both.
• Your playbooks, built in. The IC memo in house style, the quarterly portfolio review and comps on your own adjusted EBITDA, saved once and applied every time; corrections make the firm’s models better.
• Agents you can leave alone. They run on schedules and events (a new 10-Q, a guidance change, a capital call notice) and act exactly as the person who set them up.
• Kepler everywhere. The app, Excel, PowerPoint, Word, Claude, ChatGPT, Slack and email, plus an API, MCP server, Python SDK and webhooks for your own builds.
• The right model for each task. Small models we train read every page at ingest for roughly 1/700th the cost of a frontier model, which is why we can read everything; frontier models handle the question and the write-up, and we swap in better ones as they ship.
• Rules and audit. We hold SOC 2 Type II, check your firm’s own permissions on every read, log each query and calculation, and deploy in our cloud or yours (security).

Questions buy-side teams ask Kepler:

• “Which of our holdings restated a prior year or added off-balance-sheet commitments in their last five 10-Ks?”
• “Covenant headroom for every borrower in Fund III, each input cited to its compliance certificate.”
• “Alert me when any company in our long book changes guidance, and quote the line.”

“Every other AI tool I’ve tried misses what’s buried in the footnotes. Kepler has read every filing, so nothing slips past it.” (Managing Director, Multi-billion dollar fund)

Not the right fit if: you’re a coverage banker whose whole day is pitch books formatted from licensed market data.

Point it at one of your own deals at app.kepler.ai, or book a demo.

Kepler vs Rogo in brief

	Kepler	Rogo
Built around	Your firm’s ontology, built from your documents and systems	Agents and deliverables over licensed data and firm files
Where numbers come from	Pulled from the source and computed in code	Felix builds models and decks from connected sources
Firm context	Every relationship sourced to a sentence; as filed or as restated, on any date	Rogo Intelligence codifies firm expertise into reusable entities
Built mainly for	Hedge funds, PE, credit, asset managers, allocators	Investment banks, plus asset managers and PE
Where it works	App, Office, Claude, ChatGPT, Slack, email, API, MCP, SDK	Rogo app, Excel add-in, email to Felix

The full comparison, with a worked example, is in Kepler vs Rogo.

2. Hebbia

Best for: deal and research teams whose work is a stack of private documents to read the same way, from a data room to a peer set’s filings.

Where Rogo starts from licensed data and the finished deliverable, Hebbia starts from the documents. Matrix puts files in rows and questions in columns and runs each prompt down the grid. Matrix 2.0 (August 26, 2026) turns a finished grid into a model, memo, deck or email, pausing for a person to sign off before each next step. Max, Hebbia’s AI team member, reached a small set of firms in July 2026, and Headless Hebbia (October 1, 2026) opened an API and an MCP server that Claude and ChatGPT can call.

Filing coverage spans the US, Europe, the UK, Canada, Australia and New Zealand, next to transcripts and partners such as FactSet, S&P, PitchBook, Preqin, Daloopa, ICE, Third Bridge and Guidepoint. Some of those sources are switched on from day one and others run through your own subscription (Hebbia).

Compared with Rogo: Hebbia’s center is analysis across large private document sets; Rogo’s integration list is longer on licensed market data. Sacra reports Hebbia Professional seats at $10,000 a year and Lite seats at $3,000 to $3,500. See Rogo vs Hebbia for the head-to-head.

3. AlphaSense

Best for: research teams that read more than they build: broker research, expert calls and transcripts.

Rogo connects licensed feeds to an agent; AlphaSense owns a library. It says it searches more than 500 million premium business documents, and the 2024 Tegus acquisition brought an expert-interview library it now counts at 300,000 interviews, along with Canalyst models and BamSEC. Generative Search cites the snippet behind each answer, and SuperAnalyst, its research agent (early access in June 2026, launched on October 6, 2026), plans multi-step work and keeps memos, models and dashboards current.

Compared with Rogo: AlphaSense is for reading what experts, brokers and management said, where Rogo is for producing the banker’s file. AlphaSense doesn’t publish prices. Our AlphaSense alternatives guide covers it in depth.

What users say: “for the generative AI, for a period, I struggled with potential hallucinations,” a G2 reviewer wrote in October 2026, while praising its outside-in research (AWS Marketplace, G2 reviews).

4. Claude for Financial Services

Best for: firms that already pay for FactSet, S&P or PitchBook and want a frontier assistant working across those licenses.

Anthropic’s Claude for Financial Services arrived in July 2025 with connectors to FactSet, S&P Global, PitchBook, Morningstar, Daloopa, Box, Databricks and Snowflake. An October 2025 update added LSEG, Moody’s, Aiera (with a Third Bridge data feed), Chronograph, Egnyte and MT Newswires, plus Agent Skills for comps, DCF models, diligence packs and earnings analyses. The Excel, PowerPoint and Word add-ins reached general availability in May 2026.

Compared with Rogo: a general model with finance connectors, each premium one running on your own license with that provider, and the model learns your definitions only from what you connect. Kepler runs inside Claude, so a team can keep Claude and get Kepler’s cited numbers in the same conversation. Our Rogo vs Claude page compares the two directly.

5. ChatGPT for Financial Services

Best for: firms whose analysts already live in ChatGPT and want finance data added to it.

OpenAI launched ChatGPT for Financial Services on September 10, 2026, as its own plan for eligible financial institutions. Included data covers SEC filings, Quartr transcripts, PitchBook Essentials, Crunchbase, LSEG News and delayed Daloopa figures, and more than 50 connectors plus custom MCP apps reach the rest. OpenAI says answers trace a figure to the table or passage behind it, highlight the supporting text and link to cells in Excel, and business data isn’t used for training by default.

Compared with Rogo: a broader assistant with a lighter set of licensed feeds, which OpenAI describes as selected coverage. Kepler also runs inside ChatGPT. For the two assistants side by side, see ChatGPT vs Claude for financial analysis.

6. V7 Go

Best for: private markets, insurance and real estate teams pulling fields out of CIMs, data rooms and submissions.

V7 Go is built for document-heavy workflows, with use cases such as data room to IC memo, CIM to CRM deal record, and DDQ completion. It lists more than 300 integrations and MCP connections and says it never trains on customer data (V7 security). Pricing is a custom annual quote built from users and pages processed (V7 pricing).

Compared with Rogo: V7 works on the documents you bring and doesn’t center on licensed market data, so public comps and market figures come from somewhere else.

7. Datasite (Blueflame AI)

Best for: deal teams whose diligence already runs in Datasite data rooms.

Datasite acquired Blueflame in July 2025, and it now runs as Datasite’s AI agent for M&A, aimed at private equity firms, investment banks and private credit. It covers sourcing, research, diligence, modeling and monitoring, draws on Grata’s private-company data, and lets teams save custom workflows as Blueprints. Blueflame describes its outputs as source-grounded, with human review.

Compared with Rogo: strongest inside the Datasite room. Datasite also connects its rooms to Rogo (September 2026) and to Hebbia, so a Datasite customer can use the room with any of the three.

8. F2

Best for: private credit lenders, commercial banks and PE teams underwriting deals in Excel.

F2 calls itself the agentic operating system for global credit. It spun out of Arc Technologies in September 2025 (PR Newswire), and its deal agent, Adam, is assigned to each deal: it reads the full data room, builds models in live Excel workbooks and writes memos and decks on the firm’s templates. F2 also draws on a firm’s past IC memos, models and credit agreements, flags risks when a portfolio company’s actuals miss budget, and states zero data retention.

Compared with Rogo: F2 pitches itself as the buy-side counterpart to Rogo, and its own comparison names FactSet and PitchBook as its market-data integrations, a much shorter list than Rogo’s. The work centers on credit underwriting and deal execution rather than public-market research or pitch materials.

What Rogo does well

Felix turns a single prompt into client-ready decks, models and documents, takes work by email and sits inside the workbook through a native Excel add-in, and the Agent Library offers hundreds of pre-built agents alongside ones teams build on their own templates (Rogo, May 2026 update). Rogo’s integrations page lists 51 sources, from S&P Capital IQ, FactSet, LSEG and PitchBook to Preqin, Fitch and Octus, and Rogo Deal Room (August 2026), strengthened by the Rivanna acquisition, brings in whole data rooms with what Rogo calls fine-grained citations.

How to choose a Rogo alternative

Start with the job Rogo does for you today, then the job you wish it did:

• Pitch materials on licensed data for a banking team: Rogo is built for this, and Hebbia’s Matrix 2.0 also produces decks. Our best AI tools for investment banking covers that field.
• Diligence on one data room: Hebbia, V7 Go or Blueflame. Choose Kepler when the room should land inside your firm’s ontology, next to the portfolio and prior deals, with every document read in full.
• Credit underwriting: F2 is built around borrower models and credit memos. Choose Kepler when covenants, compliance certificates and borrower financials should sit in one model of the firm, computed and cited, across every loan you hold.
• Broker research and expert calls: AlphaSense.
• A general assistant across the firm: Claude or ChatGPT for Financial Services, with Kepler inside either one for figures you need to cite.
• Numbers that go to an IC or to LPs, your own definitions, and agents watching your book: Kepler.

Then run four tests in any trial, on a live deal or coverage list:

1. Ask for a complete list. “Every LPA in our 2019 to 2023 commitments with a key-person clause.” Count the results against your own records.
2. Click ten citations. Each should open the exact passage or cell, with the period and units visible.
3. Ask the same question twice. A figure that changes between runs isn’t ready for a memo.
4. Use your own definitions. Request a metric the way your firm calculates it, and name a portfolio company by the code name your team uses internally.

We expand these into a full checklist in how to check AI-generated numbers. For deal teams, the best AI tools for private equity due diligence covers the data-room side in more depth, and the best AI tools for investment research covers the wider field.

See how Kepler handles your own documents and definitions at app.kepler.ai, or book a demo and we’ll run it on a deal or portfolio you choose.

Frequently asked questions

What is the best Rogo alternative for the buy side?

Kepler, when the work depends on your firm's own documents, definitions and numbers that have to hold up in an IC memo or an LP report. Our engineers build your firm's ontology, we read every document in full before you ask, and code computes each figure with a citation to the exact page, line or cell. Agents watch your book on schedules and new filings, and the same answer comes back every time you ask.

What is Rogo AI?

Rogo is an AI platform for finance built by former bankers and investors. Its agent, Felix, turns a prompt into decks, models and documents, takes requests by email and works inside Excel through a native add-in. Rogo lists 51 integrations, including S&P Capital IQ, FactSet, LSEG, PitchBook, Preqin, Daloopa, SharePoint, DealCloud and data rooms from Datasite and Intralinks.

Who are Rogo's main competitors?

In AI built for finance: Kepler (AI built around a firm's own ontology, with every number computed in code), Hebbia (Matrix, for questions across large document sets), AlphaSense (a licensed research library with AI search and agents), Datasite's Blueflame, V7 Go and, in private credit, F2. Claude for Financial Services and ChatGPT for Financial Services compete as general assistants with finance connectors.

How much does Rogo cost?

Rogo doesn't publish prices. Sacra estimates about $3,300 per seat per year, sold as multi-year enterprise contracts, with additional revenue from specialized data access and paid features. Rogo also meters AI consumption through its Credit Center. Ask for a quote that separates seats, usage and any premium data.

Is Rogo good for hedge funds and private equity?

Rogo ships pre-built agents for private equity and equity research as well as banking, and in August 2026 it launched Rogo Deal Room and bought Rivanna for data-room diligence. Its homepage testimonials and example agents still center on banking deliverables such as CIMs, management presentations and comps. Buy-side teams that need AI on their own data model, with numbers computed in code, should look at Kepler, which is built for that work.