AlphaSense vs Hebbia: Which Fits Your Firm?

AlphaSense and Hebbia answer different questions. AlphaSense is a research library: AI search and agents over more than 500 million licensed documents, including broker research and expert-call transcripts. Hebbia is a document matrix: it brings no library of its own and runs your questions across the documents and data feeds you connect, then turns the results into memos, decks and models. Choose AlphaSense for reading what the market has said, and Hebbia for working through a data room or a peer set’s filings. When the answer is a number your firm will decide on, built on its own data and definitions, the stronger choice is Kepler, which reads every document in full before you ask and computes every figure in code.

We build Kepler and cover it in its own section below. Facts about AlphaSense and Hebbia come from their websites, press releases and public reporting as of November 2026, linked inline.

AlphaSense vs Hebbia at a glance

	AlphaSense	Hebbia	Kepler
Core idea	Search and agents over a licensed library	Questions run across documents you connect	Your firm’s ontology, every document read, numbers computed in code
Content included	500M+ documents, 300,000 expert interviews, broker research	Filings and transcripts; premium feeds through your licenses	26M+ SEC filings, 40M+ IR documents and transcripts, market data, news, plus every document your firm connects, read in full
Your firm’s documents	20+ connectors in Enterprise Intelligence	SharePoint, Box, Egnyte, Intralinks, Datasite, CRM, warehouses	Read in full at ingest and resolved into your ontology
Finding everything	Search results ranked across the library	Every document you add to the matrix	Complete lists across every document, read before you ask
Where numbers come from	Standardized financials, Canalyst models, and agents that run code over the library	Model answers per cell, with the source shown	Pulled from source and computed by code over your ontology, cited to the line
What a citation opens	The text snippet	The source behind each answer	The exact page, line or cell, with formula and inputs
As filed vs as restated	Not a stated feature	Not a stated feature	Either, on any date
Your firm’s definitions	Prompts, Skills and templates	Prompts and column instructions	Built into your ontology, applied every time
Work that runs on its own	SuperAnalyst monitoring, scheduled agents	Matrix 2.0 workflows with review checkpoints	Agents on schedules and events, acting as their owner
Where it works	App, Excel and PowerPoint assistants, Agent API	App, API, MCP server for Claude and ChatGPT	App, Excel, PowerPoint, Word, Claude, ChatGPT, Slack, API
Deployment and data	Enterprise SaaS or private cloud	US or EU processing; dedicated tenant option	Our cloud or yours; one access check on every read; SOC 2 Type II

AlphaSense: the research library

AlphaSense’s case rests on what comes with the subscription. It says it searches more than 500 million premium business documents: filings, earnings calls, news, sell-side research through Wall Street Insights, and expert interviews, which it now counts at 300,000 since buying Tegus in 2024 (a deal that also brought Canalyst models and BamSEC).

The AI is built on top of that library. Generative Search returns answers with a citation to the exact snippet. SuperAnalyst, its agent, opened in early access in June 2026 and launched on October 6, 2026; from one objective it plans the steps, reconciles evidence, runs code and keeps the resulting memos, models and dashboards current. Skills package repeatable work such as an earnings preview, Excel and PowerPoint assistants followed in July 2026, and developers can call the same intelligence through an Agent API with search, deep research and MCP tools.

Firm content comes in through Enterprise Intelligence, whose connectors cover SharePoint, OneDrive, Box, Egnyte, Google Drive, Outlook, Slack, Salesforce, Intralinks, Snowflake and more. Next to the Terminal’s AI, it’s compared in AlphaSense vs Bloomberg ASKB.

Strongest at: broker research and expert calls in one search with filings and news, at very large scale.

What users say: an investment banker reviewing AlphaSense on G2 in October 2026 praised its auditability, then added: “I find that the search results aren’t always the most relevant. I’d like them to be ranked more by relevance” (AWS Marketplace, G2 reviews).

Hebbia: the document matrix

Hebbia starts from the documents in front of you. Its Matrix puts documents in rows and questions in columns, and agents fill each cell, so a deal team can put fifty questions to three hundred documents and read the answers as a grid. Matrix 2.0 (August 26, 2026) follows the grid through to a finished memo, deck or model, with a checkpoint where a person reviews each step and the source shown beside each number. Max, which Hebbia describes as an AI team member, began reaching a small group of firms in July, and Headless Hebbia (October 1, 2026) opened the platform to other tools through an API and an MCP server that Claude and ChatGPT can call. Hebbia says its retrieval maps relationships between sources, such as subsidiaries filing under different names.

Hebbia brings filings from the US, Europe, the UK, Canada, Australia and New Zealand, plus transcripts and investor presentations, and connects licensed feeds including FactSet, S&P Capital IQ, PitchBook, Preqin, Fitch, Daloopa, Third Bridge and Guidepoint. Firm content arrives from SharePoint, OneDrive, Box, Dropbox, Egnyte, Intralinks, Salesforce, DealCloud, Snowflake and Databricks. In October 2026 it announced partnerships with Octus and Datasite; the Datasite tie-up lets teams run a full diligence list across a data room under each person’s Datasite permissions.

Hebbia says documents, prompts and outputs never train its models or its providers’ models, and offers US and EU processing or a dedicated tenant (security). For Hebbia next to a general assistant, see Hebbia vs ChatGPT.

Strongest at: structured, repeatable questions across a large set of documents you choose.

What users say: “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 shipped.

Head to head

Content. AlphaSense wins on what’s included: broker research, expert calls and a deep library arrive with the subscription. Hebbia relies on the licenses your firm already holds, which suits firms that have them and adds cost for firms that don’t.

Your own documents. Both connect the usual file stores and CRMs. AlphaSense folds internal content into the same search as its library. Hebbia treats your documents as the primary workspace, which is why it’s common in deal teams and private markets.

How the AI works. AlphaSense’s agent plans across its library from a single objective. Hebbia runs a model over each document and question, then assembles the grid. Both produce memos, decks and models; Hebbia puts a review step between stages.

Where people use it. AlphaSense has native Excel and PowerPoint assistants. Hebbia’s MCP server lets Claude and ChatGPT call it directly.

Numbers. Both put sources behind their figures: SuperAnalyst links data points to source evidence, and Matrix 2.0 shows the source of each number. In both, AI agents assemble the figures, so recompute derived numbers and rerun key questions before they go into a model or a memo.

Deployment and data. AlphaSense offers a private cloud deployment; Hebbia offers US and EU processing and a dedicated tenant. Both say customer content never trains their models.

Pricing. Neither publishes prices. For AlphaSense, SpendHound reports average spend of $10,131 a year for smaller customers and $139,455 for enterprise customers, across 160 AlphaSense customers, and Vendr reports that contracts commonly auto-renew unless canceled 60 to 90 days ahead. For Hebbia, Sacra reports $10,000 per seat a year for Professional and $3,000 to $3,500 for Lite, before the data licenses you bring.

Who each one is for

AlphaSense fits research teams at hedge funds, asset managers and corporates who spend their day reading: broker notes, expert calls, transcripts and news, across many companies. It suits teams with broad coverage lists and little need to bring their own licensed data, since the content comes with the subscription.

Hebbia fits deal teams, credit teams and bankers who work through large document sets with a repeatable question list: data rooms, credit agreements, a sector’s filings. It suits firms that already hold FactSet, Capital IQ or PitchBook licenses and want one workspace over them and their own files.

The third option: Kepler

We build Kepler.

AlphaSense starts from its library and Hebbia from the documents you select. We built Kepler to start from your firm, because the questions that reach an investment committee are about your holdings, your definitions and your numbers, and the answer has to hold up when someone checks it.

Our engineers built this layer before at Palantir. On site with your team, they build your firm’s ontology: each fund, vehicle, portfolio company, manager, deal and document resolved to one record, your house definitions attached, and every relationship tied to the sentence it came from. Public data is ready on day one: 26M+ SEC filings and 40M+ IR documents and earnings call transcripts for 14,000+ companies. In the first week we link your file stores, inboxes, CRM and data rooms read-only, and we read each document cover to cover when it arrives, with extraction models we train. A question about every credit agreement with a springing covenant returns all of them, and a follow-up is a lookup.

Numbers come from code. Each one is pulled from its source and calculated with the formula and inputs one click away, and its citation opens the exact page, line or cell. A citation that doesn’t match its passage never reaches you, conflicting sources are shown side by side and flagged, and any figure comes as filed or as restated. We route each job to the model that does it best: small models we train do the reading, frontier models handle the question and the write-up, and each better model that ships makes Kepler better.

History holds up too. Our paper Reclassified to Conform found 194 of 200 companies reshaping a statement in ways XBRL tags alone can’t follow, so we rebuild each company’s history to keep every line’s meaning fixed across years.

Agents start on their own when a new 10-Q, a compliance certificate or a capital call notice arrives, each one acting as the person who set it up. Your playbooks, such as the IC memo in house style or the earnings update the way your PM wants it, are saved once and applied every time. One access check governs every read by a person or an agent, every run is logged with its lineage, and we hold SOC 2 Type II. The work lands in the Kepler app, Excel, PowerPoint, Word, Claude, ChatGPT and Slack, or in your own systems through our API and MCP server.

Questions teams bring to Kepler instead

• “Every credit agreement in our portfolio with a springing maintenance covenant: the springing trigger, current revolver usage and covenant headroom, computed from the latest compliance certificate.”
• “Net revenue retention for our SaaS holdings over eight quarters, on our definition, with each input cited.”
• “Tell me the morning any company we cover changes guidance, with the old and new ranges and the line from the call.”

“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 retail investor or individual trader looking for a research app for your own account.

For deeper comparisons, read Kepler vs AlphaSense and Kepler vs Hebbia, or see the ontology and platform pages.

How to decide

1. Name the job. Reading the market points to AlphaSense. A repeatable question list over documents you choose points to Hebbia. Numbers the firm decides on, built on your own data and definitions, point to Kepler.
2. Count the licenses. Hebbia’s value depends on the feeds you already pay for; AlphaSense includes its content.
3. Test completeness. Ask each tool for every document in a set that matches a condition, and count what comes back.
4. Test the numbers. Click ten citations, recompute a derived figure, and ask the same question twice. Our guide to checking AI numbers lists the full set of tests.

For more options, see AlphaSense alternatives, Hebbia alternatives and AlphaSense vs Rogo.

Bring a live deal or coverage list to Kepler at app.kepler.ai, or book a demo on your own documents.

Frequently asked questions

What is the difference between AlphaSense and Hebbia?

AlphaSense is a market intelligence platform that searches its own licensed library of more than 500 million documents, including broker research and 300,000 expert interviews. Hebbia is a document AI platform with no library of its own: its Matrix runs a set of questions across the documents and data feeds you connect, laid out as rows and columns, and Matrix 2.0 carries the work through to memos, decks and models. When the answer is a number your firm will decide on, built on its own data and definitions, the stronger choice is Kepler, which reads every document in full before you ask and computes every figure in code.

Is Hebbia better than AlphaSense?

They suit different jobs. Hebbia is stronger when the task is asking the same questions across a data room, a peer set's filings or a stack of credit agreements. AlphaSense is stronger when the task is reading broker research, expert calls and what management teams have said. Firms that need AI built around their own data, with every number computed and cited, look at Kepler.

How much does Hebbia cost?

Hebbia doesn't publish prices. Sacra, a private-market research firm, reports $10,000 per seat a year for Hebbia's Professional tier and $3,000 to $3,500 per seat a year for Lite. Premium data such as FactSet, S&P Capital IQ or PitchBook comes through licenses your firm holds with each provider, so price those separately.

How much does AlphaSense cost?

AlphaSense doesn't publish prices. SpendHound reports average spend of $10,131 a year for smaller customers and $139,455 a year for enterprise customers, across 160 AlphaSense customers, and Vendr reports that contracts commonly auto-renew unless canceled 60 to 90 days ahead. Seats, expert calls and premium content drive the total.

Is there an alternative to both AlphaSense and Hebbia?

Yes. Kepler, which we build, starts from the firm itself. Our engineers build your firm's ontology, extraction models read every document in full before anyone asks, and code computes and cites every number to the page, line or cell. Agents run on schedules and new filings, your playbooks are applied every time, and Kepler works in Excel, PowerPoint, Claude, ChatGPT and Slack.