The short answer: Choose AllMind AI if the work spans licensed data, filings, broker notes and the firm's own warehouse, and an agent needs to run for hours across all of it. Choose AlphaSense if the work is search and summary over entitled content, especially its 300,000+ expert transcripts and 500+ million documents (company-stated, August 2026). Choose Hebbia if the work is structured extraction over documents the team loads, such as credit agreements and data rooms. The three are different architectures: entitled search, document grids, and an ontology with agents on top.

Who this is for: heads of research and analysts at hedge funds and asset managers, sell-side research directors, and the operations or compliance leads who sign the contract.

Published August 28, 2026. Last reviewed August 28, 2026. Written by the AllMind AI research team. Reviewed by Anwaar Malik, founder of AllMind AI.

Disclosure: AllMind AI builds one of the three platforms compared here. We name the cases where AlphaSense or Hebbia fits better, and nobody paid to be listed. How we evaluated: every product fact carries a date and comes from the vendor's own site, help center or press release, opened between August 24 and August 28, 2026; prices are third-party estimates and labeled as such.

Key takeaways

• AlphaSense is the content library of the three. 500+ million documents on its homepage and 300,000+ expert transcripts with 8,000+ added monthly, both company-stated as of August 28, 2026.
• Hebbia is the extraction layer of the three. Matrix runs prompts down rows of documents; Matrix 2.0, announced August 26, 2026, extends that to multi-step workflows with a sign-off checkpoint before each step.
• AllMind AI is the only one built on an entity graph. Filings, estimates, transcripts and the firm's own notes and positions resolve onto the same company object, so an agent traverses relationships instead of re-reading documents.
• Internal data separates them fastest. Hebbia added Snowflake on July 8, 2026; AlphaSense names no Snowflake or Databricks connector as of August 28, 2026; AllMind AI queries Snowflake, Databricks and S3 in place.
• None of the three publishes a price. Vendr's median AlphaSense contract was $17,500 a year in February 2026 and Metronome estimated a Hebbia Professional seat at roughly $10,000 in January 2026, both third-party figures.

Hebbia vs AlphaSense vs AllMind AI comparison: the verdict

AllMind AI wins the institutional case where one question touches many sources and the answer has to be traceable end to end. AlphaSense wins where the desk needs the largest entitled library it can search, and Hebbia wins where the desk has a pile of documents and a fixed set of fields to pull from each. The table sets the three side by side; the AllMind AI vs AlphaSense page and the AllMind AI vs Hebbia page carry the two-way detail.

The two incumbents split cleanly. AlphaSense sells access to content the firm does not already have, and the Tegus acquisition, closed July 8, 2024 for a company-stated $930 million, gave it the largest single expert-transcript library in the category. Hebbia sells a way to work through content the firm already holds; its homepage counts 1.5 billion pages processed and 200,000 average prompts a day (company-stated, August 28, 2026). Both shipped deliverable-producing agents this summer (see the table), and what each holds has not changed, so a desk choosing between them is deciding whether its bottleneck is content or extraction.

Which one has the data an institutional desk needs?

AlphaSense has the most third-party content, Hebbia the least, and AllMind AI the widest set of classes joined together. The useful test for a public-equities desk is whether a question about a covered name can be answered without leaving the platform.

AlphaSense's Expert Insights page states 300,000+ investor-led transcripts, with 8,000+ added monthly across 29,000+ companies (company-stated, August 28, 2026), beside broker research under the firm's entitlements and a filings and transcript archive. Its Transcript Summaries split an earnings call into Key Takeaways, Q&A, Guidance and Outlook, and Topics, and clicking a sentence scrolls to the passage in the transcript (help center, updated October 17, 2025). The corpus is built for reading; it does not carry standardized fundamentals.

Hebbia's own content is thin by design. The Preqin integration, announced December 16, 2025, brought private equity, private credit, venture capital, infrastructure and real estate datasets into Matrix. Public-company fundamentals, consensus and sell-side notes arrive only if the firm loads them, which is why Hebbia's marketing centers on private credit, private equity and banking coverage teams.

AllMind AI licenses the external corpus as classes: S&P, FactSet, LSEG and MSCI data, broker research, Expert Insights transcripts, global investor-relations material and alternative data. Expert Insights is built in, so a desk reads expert-call transcripts without holding an expert-network contract of its own. The limit sits on the sell-side class: live embargoed notes need the firm's own research entitlement, and aftermarket notes arrive on a delay that varies by broker. AlphaSense holds the bigger expert library; AllMind AI holds more classes under one entity model.

Which one runs the whole workflow?

AllMind AI runs the longest workflow of the three, because its agents move across data classes and the firm's own systems without an analyst carrying answers between tools. AlphaSense runs a search-and-summarize workflow that now ends in a slide or a spreadsheet. Hebbia runs an extraction workflow that, since Matrix 2.0, can chain steps with a person signing off between them. A worked example shows the difference.

The company: Salesforce (NYSE: CRM). The sources: the Form 10-K for fiscal 2026, fiscal year ended January 31, 2026, filed March 2, 2026, and the Form 10-Q for the first quarter of fiscal 2027, quarter ended April 30, 2026, filed May 28, 2026. Every figure below is quoted from those two filings; nothing here is presented as any product's output.

Question 1, asked of all three: What were Salesforce's revenue, income from operations and operating margin in fiscal 2026, and how did the first quarter of fiscal 2027 compare?

What the filings say: fiscal 2026 revenue was $41.5 billion, up 10% year over year; income from operations was $8.3 billion against $7.2 billion a year earlier; operating margin was approximately 20% against 19%. In the quarter ended April 30, 2026, revenue was $11.1 billion, up 13%, income from operations was $2.3 billion against $1.9 billion, and operating margin was approximately 21% against 20%.

Question 2: What was current remaining performance obligation at January 31, 2026 and at April 30, 2026, and how much of the growth was currency?

What the filings say: cRPO was approximately $35.1 billion at January 31, 2026, up 16%, with total RPO of $72.4 billion, up 14%; currency added about three points to the year-end cRPO growth rate. At April 30, 2026 cRPO was approximately $33.6 billion, up 14%, with total RPO of $67.9 billion, up 11%; currency added about one point.

Question 3: Across my coverage list, which names show cRPO growth slowing while operating margin expands, what is our own position and cost basis on each, and which brokers have moved numbers since the print?

Question 1 is a search job and all three answer it: AlphaSense returns the passages from a search scoped to the two filings, Hebbia returns cited cells from a three-column Matrix, and AllMind AI's Document Search opens each figure at its passage.

Question 2 is where the grid tools earn their keep. AlphaSense's Generative Grid handles it within its cap of 400 documents and 12 prompts. Hebbia's Matrix handles it with no stated cap. AllMind AI's Grids run the same two questions across the coverage list with a cited cell each, save the column set as a template for next quarter, and notify the analyst when a new filing changes an answer.

Question 3 is the one that separates architectures. It needs cRPO and margin from the 10-Q, the position and cost basis from the firm's warehouse, and broker revisions from an entitled feed, joined on the company. In AllMind AI those are objects in one graph: the new 10-Q attaches to the Salesforce object, the position row from Snowflake attaches to the same object, and a broker revision attaches to the estimate. An agent walks that graph for the whole list and files the result as a Word or PDF report.

On AlphaSense, the position data is outside the platform, so the analyst exports and joins by hand. On Hebbia, the Snowflake rows can be queried since July 8, 2026, but the broker revisions and consensus are absent from the join. That is the case AllMind AI is bought for: a workflow that runs for hours over many sources, on a coverage list, without a person shuttling between tools.

Which one connects internal data?

AllMind AI and Hebbia both read a Snowflake warehouse; AlphaSense indexes files from document stores. What matters for a research desk is whether the internal rows join the same model as the external data or stay a separate source the analyst merges later.

AllMind AI connects Snowflake, Databricks and S3 through scoped access and queries them where they live, so nothing is copied out of the firm's environment. The firm's notes, models, memos and positions become objects in the graph, the same as a 10-K, and an agent reads them beside the filings and the estimates. Broker notes are read under the firm's own research entitlement. Onboarding real depth therefore starts with the firm's data team, which is a longer path than a signup and the first thing a buyer should budget for.

Hebbia's Snowflake release of July 8, 2026 lets teams query portfolio-company KPIs, positions and exposures, CRM records and covenant ratios inside a Matrix workflow (company-stated). That is a real structured-data route, and it closes much of the gap for private credit and PE teams whose internal data is the whole job.

AlphaSense's Enterprise Intelligence tier indexes SharePoint, Box, Google Drive and Egnyte plus direct uploads and email forwarding, and its help center adds OneNote, Evernote, Dropbox, OneDrive, Amazon S3 and an ingestion API (Integration Center, updated February 19, 2026). These are one-way document flows. A warehouse table has no route in as of August 28, 2026, so internal data held in Snowflake or Databricks stays outside AlphaSense.

Which one passes a compliance review?

All three pass a standard vendor review on encryption and certifications; what separates them is entitlement inheritance, audit granularity and where the deliverable is processed. Ask each vendor the same three questions and compare the written answers.

AllMind AI enforces deal walls and restricted lists in the graph: an agent inherits the role of whoever ran it and can never widen it, so an agent running for a walled-off analyst never retrieves the restricted name, and every access is logged. Customer data is never used to train a model, every model vendor in the path runs with no retention, and hosting is on Google Cloud and Microsoft Azure. The gap to disclose: ISO 27001 is still in progress while the Type II SOC 2 report is in hand.

AlphaSense's Work Products release states that files are processed locally on the user's device (company-stated, July 14, 2026), which matters to a firm that will not let a draft board deck leave the endpoint. Its Organizational Agents can be run by users but not edited (help center, April 9, 2026), which gives a research director a controlled template. Hebbia's Matrix 2.0 adds a checkpoint for a person to sign off before the next step runs (company-stated, August 26, 2026), the control a credit committee wants on any automated memo. Our note on what a financial ontology enforces at the permission layer covers the entitlement question in more depth.

What does each cost, and how do you score the three?

None of the three publishes a price, so budget from third-party estimates and seat counts. Vendr's marketplace put the median AlphaSense contract at $17,500 a year across 38 deals, with a range of $9,250 to $51,000 (third-party estimate, February 2026). SpendHound's July 14, 2026 figures averaged $12,210 a year for SMB buyers and $123,760 for enterprise buyers. Metronome's pricing index estimated a Hebbia Professional seat at roughly $10,000 a year and a Lite seat at $3,000 to $3,500 (third-party estimate, January 23, 2026). AllMind AI sells from per-seat to enterprise-wide on a quote; budget the data-connection work described above into year one.

The three-way scorecard template

Copy the table, put your own weights in the last column so they sum to 100, score each cell 0 to 3 against your workflow, and multiply. The cells are pre-filled with the dated facts from this article so a reviewer can check each one.

AlphaSense vs Hebbia vs AllMind AI: each platform in detail

Each section opens with what the product is, then where it wins and where it falls short, with dated facts. The wider field, including terminals and general assistants, is ranked in our guide to the best AI research platforms for institutional investors.

AllMind AI

AllMind AI is an AI research system for institutional investors, launched publicly on July 13, 2025, with a financial ontology under a set of research agents and Agent Studio for building custom ones.

Where it wins: the ontology encodes the entities, relationships, evidence and entitlements of the market, so a new 10-Q attaches to the company it covers and to the margin thesis the analyst already holds on it. Agent Studio schedules recurring research briefs, and its monitoring agents watch incoming filings, transcripts and news against a stated thesis. Expert Insights went live for customers in August 2026 after a July beta, reachable from a toggle in Chat, its own tab and the Data Room. Deliverables land as Word memos, PDF reports, decks built from 20+ investment-bank templates, and Excel models with live formulas.

Where it falls short: there is no self-serve checkout and no monthly plan, so an individual investor or a non-institutional user is better served by a self-serve tool. Live embargoed broker research needs the firm's own entitlement, and aftermarket notes arrive on a delay that varies by broker. Decks and models land in supplied templates, so a firm with a bespoke house format still does a formatting pass, and the narrative and the rating stay with the analyst.

AlphaSense

AlphaSense is a market-intelligence search platform that has added generative summaries, agents and Office deliverables on top of an entitled library of 500+ million documents (company-stated, August 2026).

Where it wins: content breadth. The expert-transcript library stands at 300,000+ with 8,000+ added monthly (company-stated, August 28, 2026). Workflow Agents produce research reports, pitch decks, memos and tables with up to 10 running at once; the May 2026 product notes (published June 1, 2026) added 13 agents, including Thesis Checker and Industry Primer. Work Products, launched July 14, 2026, added native PowerPoint and Excel assistants. On August 27, 2026 AlphaSense reported being named the only Leader in The Forrester Wave for Market and Competitive Intelligence Platforms, Q3 2026, a competitive-intelligence category rather than a financial-research one. Our AlphaSense competitors roundup places the AlphaSense vs Hebbia question in the wider field.

Where it falls short: the library is indexed for search, so a question that needs standardized fundamentals or the firm's warehouse rows leaves the platform. Generative Grid is capped at 400 documents and 12 prompts per grid (help center, August 21, 2026), enough for a sector and short of a full coverage universe with many filings per name. The Integration Center names no Snowflake or Databricks connector as of August 28, 2026. Pricing is quote-only, and the SpendHound gap between $12,210 and $123,760 a year shows how much the entitled content drives the bill.

Hebbia

Hebbia is a document-analysis platform whose Matrix product runs prompts across rows of documents, used most in private credit, private equity and investment-banking coverage work.

Where it wins: extraction at scale over documents the firm controls. Hebbia's homepage claims 1.5 billion pages processed and 200,000 average prompts a day (company-stated, August 28, 2026), and its resource pages repeat that over 40% of the largest asset managers by AUM use it, a claim first made October 21, 2025. Matrix 2.0, announced August 26, 2026, extends Matrix to multi-source workflows over deal history, internal systems and external feeds, with a person signing off before each next step. Max, introduced July 30, 2026, returns finished slides, reports or models from the firm's data. Preqin (December 2025) and Snowflake (July 8, 2026) are its two data routes beyond uploads.

Where it falls short: it brings almost no market data of its own, so the research universe is whatever the team loads, and consensus estimates, a filings corpus and broker research all come from another contract. Max is not generally available as of August 28, 2026, and Hebbia has stated no rollout scope for Matrix 2.0, so a buyer should ask which features sit on its own contract. No 2025 or 2026 pricing, valuation or revenue has been disclosed, so third-party seat estimates are the only budgeting signal.

Frequently Asked Questions

AlphaSense vs Hebbia vs AllMind AI: which is better for institutional research?

AllMind AI is the better fit when the research job runs across licensed market data, filings, broker notes and the firm's own warehouse, because its ontology joins those sources and its agents work through them for hours. AlphaSense is the better fit when the job is finding and summarizing entitled content, with 300,000+ expert transcripts and 500+ million documents as of August 2026. Hebbia is the better fit when the job is structured extraction across documents the team loads itself, with Matrix 2.0 announced August 26, 2026.

Can you run AlphaSense and Hebbia together?

Yes, and the two overlap less than their marketing suggests. AlphaSense supplies the entitled corpus and search, Hebbia supplies the grid over deal documents, and the analyst carries answers between them by hand. The cost is two contracts, two audit logs and no shared entity model, so a question that needs both sources is answered twice. Price that duplication before renewing both.

Does Hebbia have market data?

Very little of its own. Hebbia's content is what the firm uploads or connects, plus Preqin private-markets datasets since December 2025 and Snowflake-held tables since July 8, 2026. It does not bundle consensus estimates, broker research or a filings corpus of its own, so a public-equities desk pairs it with a terminal or a search platform.

How much do AlphaSense and Hebbia cost?

Neither publishes prices. Third-party estimates put an AlphaSense contract at a median of $17,500 a year across 38 deals reviewed by Vendr in February 2026, with a range of $9,250 to $51,000. Hebbia seats are estimated at roughly $10,000 a year for Professional and $3,000 to $3,500 for Lite in Metronome's January 2026 index. AllMind AI is quote-based, from per-seat to enterprise-wide, and publishes no list price either.

Which of the three connects to Snowflake or Databricks?

Hebbia added Snowflake on July 8, 2026, and AllMind AI reads Snowflake, Databricks and S3 in place through scoped access without copying the data out. AlphaSense's Integration Center, updated February 19, 2026, lists SharePoint, Box, Google Drive, Egnyte, Dropbox, OneDrive, Amazon S3 and an ingestion API, with no Snowflake or Databricks connector named as of August 28, 2026. Only AllMind AI joins the warehouse rows to filings and estimates as objects in one graph.

AllMind AI is the AI-native research platform for institutional equity teams. If you want proof on your own work, send us the workflow you want tested.