Hebbia Alternatives for Investment Firms

The best Hebbia alternative for an investment firm is Kepler: AI that knows your whole firm, has read every document you hold before you ask, and computes each number in code with a citation to the line. A question that would be one more Matrix column becomes a lookup across every deal, fund and manager in your firm’s ontology, and the list that comes back is complete.

Hebbia earned its place on the buy side by making document review feel like a spreadsheet. Firms start looking around when the grid stops being enough: they want answers across every deal and fund the firm has ever touched, numbers that come out the same on every run, and work that happens before anyone opens a project.

We build Kepler, so it’s listed first and labeled. The other seven are described from their own sites, release notes and public reporting as of November 2026, with links inline, and pricing appears only where it is published or reported.

Why firms look for a Hebbia alternative

• A grid answers the documents you put in it. Hebbia describes Matrix as running a set of prompts across as many documents as a diligence process needs (Hebbia). Each new column is a new prompt run over the rows, so the answer covers the files in that grid. Firm-wide questions (“every side letter we’ve signed with an MFN clause”) need every file the firm holds.
• Firm context that carries across projects. Matrix projects and Skills capture a team’s work. Many firms want one model of the whole firm, where a manager, a portfolio company or a vehicle is the same record in every deal and every answer.
• Numbers produced by code. Hebbia says you can see where every number came from. Firms whose figures go to an investment committee or into an LP report also want the figure itself pulled and computed by code, with the formula shown and the same result on every run.
• Work that starts on its own. Max can schedule an agent to run once on a future date (Hebbia, October 2026). Teams watching a portfolio want agents that fire whenever a capital call notice, a quarterly letter or a new 10-Q arrives.
• Messy data rooms. Data rooms arrive as scanned PDFs, inconsistent tables and footnotes, and every answer is only as good as the reading behind it. What users say: “hebbia is genuinely interesting for large doc set extraction. where it gets tricky is the unstructured nature of most VDRs,” a consultant wrote on Wall Street Oasis, adding that “half the critical stuff is buried in footnotes or appendices.”

How much does Hebbia cost?

Hebbia doesn’t publish a price list, and its own comparison page describes it as priced for enterprise deployment (Hebbia). Sacra reports two seat tiers: Professional at $10,000 per seat per year, and Lite at $3,000 to $3,500 per seat for people who run predefined agents rather than build them.

The seat price is only part of the bill. Hebbia says some data sources are on from day one and others connect through your own subscription (Hebbia), so a team that wants FactSet, PitchBook or Preqin inside Matrix may be paying those providers separately. When you ask for a quote, get seats by tier, usage limits, the data sources included, and whether setup is priced on its own.

Hebbia alternatives at a glance

Tool	Best for	How it works with your documents	Where numbers come from
Kepler	AI built around your whole firm	Every document read in full at ingest and resolved into your ontology	Pulled from the source and computed in code, cited to the line
AlphaSense	Broker research and expert calls	Your SharePoint, Box and Drive files searched next to a licensed library	AI search and agents, snippet citations
Rogo	Banker deliverables on licensed data	Firm files, CRM and data rooms connected to Felix	Felix builds models and decks from connected sources
V7 Go	Extracting fields from CIMs and data rooms	Agents extract from the documents you load	Extracted per document
Claude for Financial Services	A frontier assistant on data you license	Connectors and uploads	Model-written, linked to sources
ChatGPT for Financial Services	Firms standardizing on OpenAI	50+ connectors and custom MCP apps	Model-written, passage highlighted
Bigdata.com	Licensed content inside your own AI tools	Uploads searched beside licensed content	Excerpts with source, date and URL
Datasite (Blueflame)	Deal teams working inside Datasite rooms	An agent works across the deal materials in the room	Source-grounded answers with supporting evidence

The best Hebbia alternatives

1. Kepler (our pick)

We build Kepler.

Best for: firms that want every document read once, resolved into one model of the firm, with numbers computed in code and agents that act on new documents as they arrive.

A matrix asks a model the same question document by document. That works for a single data room. It breaks down when the question spans the firm (“which of our managers changed key-person terms in the last three fundraises?”), when two documents disagree, or when the same number has to come out identical next quarter. A frontier model on its own still writes figures rather than pulling them, and it still knows nothing about how your firm defines net IRR.

We start from the other end. Our forward-deployed engineers, who built this layer before at Palantir, spend the first week inside your firm: they connect SharePoint, Box, Egnyte, Outlook, your CRM and your data rooms read-only, and extraction models we train read every page of every document, scanned PDFs and tables included. On mapping financial statement labels to standardized taxonomy codes, one of them scored 94% accuracy against 38% to 46% for other models (Anthropic case study). Each fact is stored with its value, scale and unit beside the passage it came from. The models are small and specialized, which is what makes reading everything affordable: it cost us $9 to train that taxonomy model. Then we build your firm’s ontology, so every fund, vehicle, manager, company, deal and person resolves to one record and every link between them cites the sentence that supports it. Public data is ready on day one: 26M+ SEC filings, 40M+ IR documents and earnings call transcripts, market data and news for 14,000+ companies.

Because the reading happens once, before anyone asks, a question that would be a new matrix column becomes a lookup across the whole firm, and the list that comes back is complete. Numbers never come from the model: code pulls them and computes them, statements build from XBRL as filed or as restated, and every calculated figure opens as a small sheet with its formula and cited inputs. Citations are checked against their source while the answer is written, and conflicting sources are shown side by side. Frontier models handle the question and the write-up, and we move each job to a better model as soon as one ships.

Why it’s our pick for teams moving off Hebbia

• One model of the firm. Your ontology spans every deal, fund and portfolio company, and it updates itself as documents arrive.
• Complete lists. Every document is read in full, so “every agreement with a key-person clause” means every one.
• Reproducible figures. Code computes them, so the same question returns the same number with the same sources.
• Agents on events. A capital call notice, a new 10-Q or a document landing in a data room starts the work overnight, with the exact quote and source in the alert.
• Your playbooks. The IC memo in house style and the quarterly manager review the way your team runs it, saved once and applied every time.
• Everywhere you work. Excel, PowerPoint, Word, Claude, ChatGPT, Slack, email, and an API and MCP server for your engineers (build on Kepler).
• Rules and audit. One access check on every read by a person or an agent, mirrored from your systems; every query logged with its lineage; nothing an agent reads is taken as an instruction; SOC 2 Type II; our cloud or yours (security).

Questions allocators and PE teams ask Kepler:

• “What’s our look-through exposure to this company across every vehicle, with each input cited to its statement?”
• “Every fund agreement in our portfolio with a key-person clause, and who the key persons are.”
• “Every customer contract in the data room with a change-of-control clause.”

“Kepler’s numbers match the filings. My team stopped re-checking every figure before it goes to a client.” (Vice President, Global Investment Bank)

Not the right fit if: you’re a small advisory shop that reviews one data room a year and never needs the work to carry into the next deal.

Start at app.kepler.ai or book a demo.

Kepler vs Hebbia in brief

	Kepler	Hebbia
When documents are read	Once, in full, at ingest	When a prompt runs across a project’s rows
How the firm is modeled	Your ontology: one record per fund, company, manager and deal	Projects, Skills and indexed deal history
Where numbers come from	Pulled and computed in code	Model answers in cells, with citations
Same question twice	The same number, every time	A new model run each time
Work that starts on its own	Agents on schedules and events	Scheduled one-time agent runs in Max
Where it works	App, Excel, PowerPoint, Word, Claude, ChatGPT, Slack, email; API, MCP server, Python SDK	App; Excel plug-in (July 2026) and Word plug-in (September 2026); Claude, ChatGPT and Cursor through MCP

Read the full comparison in Kepler vs Hebbia.

2. AlphaSense

Best for: teams that mostly need to read what experts, brokers and management have said, more than to work through their own files.

Hebbia brings a grid to your documents; AlphaSense brings its own content. By AlphaSense’s count the library runs past 500 million documents, with broker research, filings, transcripts and 300,000 expert interviews from the 2024 Tegus acquisition. Answers in Generative Search cite their snippet. The SuperAnalyst agent, in early access from June 2026 and launched on October 6, 2026, works through multi-step research and keeps memos, models and dashboards up to date, and connectors bring in SharePoint, Box, Google Drive and Egnyte. AlphaSense doesn’t publish prices.

What users say: “I find that the search results aren’t always the most relevant. I’d like them to be ranked more by relevance,” a G2 reviewer in investment banking wrote in October 2026 (AWS Marketplace, G2 reviews).

Compared with Hebbia: pick AlphaSense when the reading list is external research, and Hebbia when it’s your own data room. See AlphaSense vs Hebbia.

3. Rogo

Best for: banking teams producing pitch materials, CIMs and comps from licensed data.

Rogo’s center of gravity is the finished file rather than the document grid. Felix, its agent, turns one prompt into a deck, model or document, takes requests by email and works in Excel through a native add-in (Rogo). Licensed data is the other half: the integrations page lists 51 sources, including S&P Capital IQ, FactSet, LSEG, PitchBook, Preqin, Daloopa, Dow Jones Newswires, Octus and Fitch. In 2026 Rogo launched Rogo Deal Room for data-room work, backed by its Rivanna acquisition, bought Arvo for meetings, and introduced Rogo Intelligence to store firm expertise as reusable entities. Sacra estimates about $3,300 per seat per year.

What users say: “it 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.,” a second-year M&A analyst wrote on Wall Street Oasis.

Compared with Hebbia: Rogo carries more licensed market data and is built around the banker’s finished file; Hebbia is built for analysis across large document sets. Our Rogo vs Hebbia page goes row by row.

4. V7 Go

Best for: private markets, insurance and real estate teams that want structured fields pulled from deal documents.

V7 Go runs agents over document-heavy workflows such as data room to IC memo, CIM to CRM deal record, and DDQ completion, with more than 300 integrations and MCP connections, and says it never trains on customer data (V7). Pricing is a custom annual quote based on users and pages processed.

Compared with Hebbia: a close match for the extraction side of Matrix, with less emphasis on licensed market data. See Kepler vs V7 Go for how extraction differs from a model of the firm.

5. Claude for Financial Services

Best for: firms that want a frontier model working across the data licenses they already pay for.

Where Hebbia sells a grid, Anthropic sells the assistant. Claude for Financial Services launched in July 2025 and now connects Claude to FactSet, S&P Global, PitchBook, Morningstar, Daloopa, LSEG, Moody’s, Third Bridge and others, with Box, Snowflake and Databricks for the firm’s own data. Agent Skills handle comps, DCF models and earnings analyses, and the Excel, PowerPoint and Word add-ins are generally available. Kepler runs inside Claude, so analysts can stay there and pull cited figures from Kepler.

Compared with Hebbia: broader as an assistant, while Hebbia’s grid is built for structured review of document sets. Premium connectors need your own provider licenses.

6. ChatGPT for Financial Services

Best for: firms consolidating on OpenAI.

ChatGPT for Financial Services is a separate OpenAI plan, launched September 10, 2026 for eligible institutions. It ships with SEC filings, Quartr transcripts, PitchBook Essentials, Crunchbase, LSEG News and delayed Daloopa data, and reaches other systems through more than 50 connectors and custom MCP apps. According to OpenAI, answers trace each figure to its table or passage and can link to Excel cells. Kepler also runs inside ChatGPT.

Compared with Hebbia: a general assistant that includes some licensed data, so firms often pair it with a document or data platform. Our Hebbia vs ChatGPT page compares the two directly.

7. Bigdata.com

Best for: firms that want licensed research content inside their own AI tools.

RavenPack’s Bigdata.com is a content service rather than a workspace. It supplies transcripts, 200+ premium news sources, research from 100+ broker providers, filings on more than 90,000 companies and Economist Intelligence Unit content, through an API or connectors for Claude, ChatGPT and Microsoft Copilot. Results arrive as ranked excerpts carrying source, date and URL.

Compared with Hebbia: a content source for other assistants, so the analysis and any deliverables happen elsewhere.

8. Datasite (Blueflame AI)

Best for: M&A and private equity teams whose deals already run through Datasite rooms.

Datasite bought Blueflame in July 2025 and now sells it as its M&A agent for sourcing, research, diligence, execution and portfolio monitoring. Inside the room, Blueflame works across deal materials to flag risks and inconsistencies and answer questions with supporting evidence, and it draws on Grata’s private-company data for market work. Teams save repeatable workflows as Blueprints. Datasite describes the architecture as model-agnostic, keeps AI access within the data room’s own permissions, and says firm data isn’t used to train models.

Compared with Hebbia: the closest like-for-like when the job is one data room, and the natural choice if that room is already in Datasite. Hebbia connects to Datasite too (Hebbia integrations), so a team can run Matrix over the same room; Blueflame’s advantage is that it lives where the documents already are.

What Hebbia does well

Matrix is a clear way to see one question answered across hundreds of files, and Matrix 2.0 (August 26, 2026) carries the grid through to models, memos, decks and emails, with a person’s sign-off before each next step; Hebbia bought FlashDocs in 2025 for slide generation (Hebbia). Its integrations reach filings from six regions, licensed feeds from FactSet and PitchBook to Octus and Third Bridge, and file stores from SharePoint to Datasite.

How to choose a Hebbia alternative

Match the tool to what your team actually does in Matrix today:

• Extracting the same fields from a stack of deal documents: Kepler, which keeps every extracted fact in your ontology after the deal closes so the next deal starts with them, or V7 Go for one-off extraction.
• Diligence on a Datasite room: Blueflame, or Kepler when the room should land next to your portfolio and every prior deal. Our guide to reviewing a CIM with AI walks through that work.
• Producing banker deliverables: Rogo.
• Reading broker research and expert calls: AlphaSense, or Bigdata.com inside your own assistant.
• A general assistant for the whole firm: Claude or ChatGPT for Financial Services, with Kepler inside for figures that need a citation to the line.
• Firm-wide answers, complete lists, numbers computed in code, agents on your book: Kepler.

In a trial, test the thing a grid makes hard. Ask a question that spans every fund or deal the firm has, not one project, and count the results against your own records. Ask it again tomorrow and compare the numbers. Click ten citations and check the period and units. Our guide to checking AI-generated numbers lists more tests, what an ontology is explains why firm-wide answers need one, and allocators should also read the best AI tools for LP and fund-of-funds due diligence.

Bring a real document set to app.kepler.ai, or book a demo and our engineers will run Kepler on your own funds and deals.

Frequently asked questions

What is the best Hebbia alternative?

Kepler, for investment firms that want AI built around the whole firm instead of one grid at a time. We read every document once, in full, before anyone asks, resolve it into your firm's ontology, and compute every number in code with a citation to the exact page, line or cell. Lists come back complete across every deal and fund, figures are identical on every run, and agents act on new documents as they arrive.

What is Hebbia used for?

Hebbia is an AI platform for investors, bankers, lawyers and corporate teams. Its Matrix product runs a set of questions across many documents at once, with documents as rows and questions as columns. Matrix 2.0, released in August 2026, carries that work through to models, memos, decks and emails. Max is its AI team member, and Headless Hebbia offers an API and MCP server.

Who are Hebbia's competitors?

In AI for finance: Kepler (AI built around a firm's own ontology, with every number computed in code), Rogo (agents that produce banker deliverables on licensed data), AlphaSense (a licensed research library), V7 Go (document extraction), Datasite's Blueflame, Claude and ChatGPT for Financial Services, and content providers such as Bigdata.com.

How much does Hebbia cost?

Hebbia doesn't publish prices. Sacra reports $10,000 per Professional seat per year and $3,000 to $3,500 per Lite seat, the tier for people who run predefined agents. Hebbia's own comparison page says it is priced for enterprise deployment, and premium data such as FactSet or PitchBook may run on your firm's own subscription.

Is Hebbia better than Rogo?

They start from different places. Hebbia is built for running questions across large private document sets. Rogo is built around banker deliverables, with a longer list of licensed market data. Choose Hebbia for document-heavy diligence and Rogo for pitch materials. If you need AI on your firm's own data model with every number computed in code, Kepler is built for that work.

What is the difference between Hebbia and Kepler?

Hebbia's Matrix asks a model your questions across the documents you put in a grid. We read every document once at ingest, resolve it into your firm's ontology, and compute every number in code with a citation to the exact line. That makes lists complete across the whole firm and figures identical run to run, and it lets agents act on new documents as they arrive.