The short answer: For a team covering public names, AllMind AI is the Hebbia alternative to test first. Its Grids run one question across a ticker list with a cited cell per company, and each row carries the estimates, fundamentals and broker notes connected to it in the ontology. AlphaSense fits when the documents that matter are licensed research, Rogo fits banks that need decks and memos, and Daloopa or Fiscal.ai fill the data gap. Hebbia keeps its place for PE and credit data-room reading at scale.

Who this is for: credit and PE analysts, public-equity research teams, and heads of research reviewing a Hebbia renewal or a first document-AI buy.

Published August 24, 2026. Last reviewed August 24, 2026. Written by the AllMind AI research team.

Reviewed by Anwaar Malik, founder of AllMind AI.

Disclosure: AllMind AI builds one of the platforms compared here. We name the cases where Hebbia or another competitor fits better, and no vendor paid to be listed.

Key takeaways

• Hebbia's limit is structural. Matrix reads what you load; market data arrives through connectors (FactSet, S&P Capital IQ, PitchBook, Preqin) that run on subscriptions you already hold (hebbia.com, August 2026).
• The alternatives split by job. AllMind AI for coverage grids with market data attached, AlphaSense for licensed research, Rogo for deliverables, V7 Go for pipelines, Daloopa and Fiscal.ai for data.
• Every vendor here quotes privately. The only Hebbia seat figures are a third-party estimate from January 2026: about $10,000 Professional, $3,000 to $3,500 Lite, labeled unverified by its publisher.
• Hebbia stays the incumbent for deal-room reading. Company-stated use at over 40% of the largest asset managers by AUM (October 2025), Snowflake (July 8, 2026) and Max (July 30, 2026) anchor it.
• Run one real task before you decide. The worked example pulls Whirlpool's revolver covenants from its FY2025 10-K; the 12-task rubric scores any grid tool on your own documents.

Why do teams look for Hebbia alternatives?

Teams look for Hebbia alternatives when the work moves from reading private documents to covering public companies, because a Matrix grid holds no market data of its own. Four structural complaints follow.

• The universe is what you load. Hebbia lists SEC filings, transcripts and provider connectors (FactSet, S&P Capital IQ, PitchBook, Preqin, ICE Market Data); the connectors draw on licenses the firm holds separately.
• No broker research library. AlphaSense's compare page (August 2026) argues that Hebbia lacks broker research and trade journals, and the point is accurate.
• Coverage questions leave the grid. A column that needs consensus estimates, fundamentals or prices has nowhere to draw from inside Matrix, so the analyst exports to Excel and joins by hand.
• Enterprise-only buying. hebbia.com offers a preview request and no price list; at about $10,000 per Professional seat (third-party estimate, January 2026), a two-analyst team pays enterprise money for reading that a self-serve tool covers.

The deal-phase view is in the guide to AI tools for private equity due diligence; the category map is the 2026 landscape of AI vendors for institutional investment teams.

Hebbia alternatives by the job you hired Hebbia for

The table holds the checkable facts; each tool is then judged on one input, the Whirlpool covenant row worked further down.

AllMind AI

AllMind AI is an AI research system for institutional investors, and its Grids product is the closest like-for-like answer to Matrix: tickers down the rows, questions across the columns, a cited answer in every cell.

Where it wins: The grid mechanics match the Matrix habit and add the coverage loop: paste a ticker list and the universe resolves, save the columns as a template, subscribe to be told when an answer changes, export to Excel. All four are on the Grids page (August 2026).

Under the grid, each ticker row is an entity in the financial ontology, connected to the company's filings, transcripts, estimates and broker notes, so a coverage column that needs a consensus figure has somewhere to draw from. Entitlements are inherited by any agent a user runs and can never widen, and each query and export is logged. The product launched publicly on July 13, 2025 and completed its SOC 2 Type II audit in November 2025.

Where it falls short: AllMind AI is quote-based with no self-serve tier. A grid over public filings needs only a ticker list once the contract is in place, but the columns that justify the seat, drawn from the position file and the warehouse, are an integration project scoped with the firm's own data engineers. A security review will also find ISO 27001 certification still in progress alongside the completed SOC 2 Type II report. AllMind AI vs Hebbia sets the two side by side.

AlphaSense

AlphaSense is a market-intelligence search platform over broker research, expert transcripts and news, the Hebbia alternative for teams whose documents are mostly licensed content.

Where it wins: Generative Grid applies several prompts across many documents and returns a table, the Matrix motion over AlphaSense's own library (its compare page, August 2026). That library holds 280,000+ expert transcripts since the Tegus acquisition closed on July 8, 2024, plus PowerPoint and Excel Work Products since July 14, 2026. On the Whirlpool task: the covenant columns from the 10-K, plus whatever the sell side wrote about the revolver.

Where it falls short: Internal files are indexed through the Enterprise Intelligence tier (SharePoint, Box, Google Drive) with no query into Snowflake or Databricks, so a positions column stays outside. Pricing is quote-only; Vendr's median contract of $17,500 a year (February 2026) is an estimate. The wider field is in AlphaSense competitors in 2026.

Rogo

Rogo is an AI analyst for investment banking and private equity deliverables: decks, comps, memos and models in the bank's own format, with data partners built in.

Where it wins: Rogo ships output: Deal Room launched August 6, 2026, the Rivanna acquisition of August 11, 2026 added data-room querying, and Rogo states 50,000+ professionals at 350+ institutions (August 2026). On the Whirlpool task: the covenant set lands inside a credit page or a deck section, shaped for the pitch.

Where it falls short: The shape is the deal, so a maintained grid over 200 public names that refreshes each quarter sits outside its design. Pricing is unpublished, and Sacra's figure of about $3,300 per seat is directional only. The Rogo comparison goes deeper, and the Hebbia vs Rogo section below settles the pair.

V7 Go

V7 Go is V7's workflow-automation platform for documents in finance, insurance and legal, launched April 10, 2024, the Hebbia alternative for Matrix-style extraction as a repeatable pipeline.

Where it wins: It reads messy PDFs, including handwritten text, into properties and generated documents; the finance page names CIM to IC memo, DDQ completion, LPA analysis and portfolio monitoring as built workflows (August 2026). It announced a PitchBook partnership on August 6, 2026, and pricing combines platform, users and document volume with no rate card. On the Whirlpool task: the eight columns become properties in a workflow over the uploaded PDF, repeated on each new filing.

Where it falls short: V7's pages list integrations and no public-filings corpus or market data, so the filing has to be supplied. It is a cross-industry product: a company is a set of documents, with no financial entity model joining filings to estimates.

Daloopa

Daloopa is an AI fundamental data layer that updates Excel models with datapoints linked to their filing location, and it complements a grid tool more often than it replaces one.

Where it wins: Coverage runs to 5,500+ companies (company-stated, May 28, 2026), every number opens to its source, and MCP connectors put the data inside ChatGPT (December 2025), Perplexity (April 2026) and Microsoft 365 Copilot (June 25, 2026). A Free plan with three data sheets exists. On the Whirlpool task: the net sales line ($15,524 million for FY2025) as structured data, with the source cell one click away.

Where it falls short: The covenant columns are text, and text is out of scope: no facility size, no coverage floor, no compliance statement. There is no grid over private documents and no data-room concept, so a PE team replacing Hebbia still needs a reader beside it.

Fiscal.ai

Fiscal.ai, formerly FinChat and rebranded in 2025, is a fundamentals terminal with an AI copilot, the self-serve Hebbia alternative for a small team reading public companies.

Where it wins: Coverage is 100,000+ public companies with segment-level KPIs for the largest 2,300 of them, and the API's free tier allows 100 companies and 250 calls a day (docs.fiscal.ai, August 2026). Plans are self-serve, so replacing a Lite seat takes no procurement cycle. On the Whirlpool task: the financials are a screen away; check in the trial whether Whirlpool is among the segment-KPI names.

Where it falls short: No private documents, no entitled broker research or expert calls and no data room, so the CIM and the credit agreement have nowhere to go. The covenant row exists only as far as the copilot reads the 10-K in a chat, with no grid to hold it across issuers.

BlueFlame AI

BlueFlame AI is an LLM-agnostic platform for alternatives managers, owned by Datasite since July 24, 2025, the Hebbia alternative for DDQs and deal memos at a PE or credit fund.

Where it wins: The workflows are the fund's own: DDQ completion, deal memos and investor-reporting drafts, with the Amp dealmaking agent added on June 23, 2026 and Datasite's data rooms next door. On the Whirlpool task: the covenant questions are answered from a document loaded into a deal workspace, in the format the deal team files.

Where it falls short: Public-equities depth is thin: no coverage universe, no estimates and no market data, so a hedge fund or long-only team gets a diligence tool without the daily research half. Pricing is quote-only; budget from a demo and test the DDQ workflow on a live questionnaire.

What does one real document task look like as a grid?

Here is a credit analyst's standing question run as a grid row. The output column is what Whirlpool Corporation (NYSE: WHR) states in its Form 10-K for the fiscal year ended December 31, 2025, filed February 11, 2026. Nothing here is a product screenshot; every cell is a figure from the filing.

Source: Whirlpool Corporation, Form 10-K filed February 11, 2026, Note 6 (Financing Arrangements) and the liquidity section of Item 7.

Filling the row by hand takes one careful reading of the financing note in the 2025 10-K. The grid question is what happens next: the same eight columns across every issuer, refreshed at the next 10-Q, each opening to the passage behind the $3.5 billion facility and the 3.0x floor.

On AllMind AI the filing is already in the corpus, so the row starts from the ticker: paste WHR and the issuer list, add the columns, and each cell cites the note. The WHR row is an entity in the ontology, connected to reported interest expense and estimates, so the analyst reads the 3.0x floor against the numbers that test it.

The position and exposure columns can come from the firm's own warehouse, queried in place through a scoped role. The content under the grid spans S&P, FactSet, LSEG and MSCI data, broker research, Expert Insights, investor-relations data, live earnings and sector sets such as mining. Expert Insights is part of what the firm already pays for, while broker research follows the entitlements it holds.

The document-grid evaluation rubric: 12 tasks

Run the twelve tasks on any grid tool with your own documents; budget two analysts and one week. Score 0, 1 or 2 per task, 24 maximum. Tasks 1, 4, 5, 10 and 11 test the Matrix job; tasks 2, 3, 7, 8 and 9 test what a document grid does not hold. A tool scoring 2 on the first group and 0 on the second is a Hebbia clone, fine if that is not your work.

Why doesn't Hebbia publish its pricing?

Hebbia quotes each firm privately; hebbia.com carries a preview request and no price list as of August 2026. AllMind AI, AlphaSense and Rogo also quote privately. The metronome pricing index (January 23, 2026) puts a Professional seat at about $10,000 a year and a Lite seat at $3,000 to $3,500, and labels the figures an unverified estimate.

As a planning number, five Professional seats come to about $50,000 a year before data subscriptions, and five Lite seats to $15,000 to $17,500. The per-seat picture across vendors is in the 2026 pricing guide for AI research tools.

Is Hebbia worth it?

Hebbia is worth it when the work is reading private document sets against a checklist: a PE deal team with several hundred documents per data room is the clearest case, then the credit fund working agreements and amendments. At the third-party estimate of about $10,000 per Professional seat (January 2026), the seat buys a grid that reads that material with citations, plus Max for slides, reports and models.

It is a weaker buy in two cases. For public-markets coverage, the filings are already indexed in AllMind AI, AlphaSense or Fiscal.ai, and the missing piece is market data, which Hebbia does not hold. For a desk whose document work is a handful of PDFs a quarter, a self-serve tool at hundreds of dollars a year covers most of the reading. Run the rubric before renewing: a 2 on tasks 1, 4, 5, 6 and 10 is the case for staying; a 0 on tasks 3, 7 and 9 is the case for adding or switching.

When to stay with Hebbia

These are the concrete cases for staying with Hebbia.

• PE and credit data-room work. Hundreds of documents per deal, a fixed checklist, plus Max (July 30, 2026) for the memo and the deck from the same room: the job Matrix was built for.
• Large asset managers already standardized on Matrix. Hebbia states use at over 40% of the largest asset managers by AUM (October 2025); where templates, permissions and connectors are built, the switching cost is the rebuild.
• Firms whose internal data sits in Snowflake. The July 8, 2026 integration queries covenant ratios, positions and CRM records beside the documents.
• Manager research on private markets. The Preqin partnership (January 2026) puts fund and manager data beside the DDQs and LPAs the research runs on.

Hebbia vs Rogo

Hebbia is a document platform: Matrix grids and, since July 30, 2026, the Max agent over whatever the firm loads. Rogo is an AI analyst for bank deliverables, with data partners built in and, since August 6, 2026, a Deal Room. Choose Hebbia when the input is the problem (a data room, a stack of credit agreements). Choose Rogo when the output is the problem (a pitch due Monday, a comps page).

• Funding. Hebbia's last priced round is the $130 million Series B led by a16z at about $700 million (July 2024, TechCrunch). Rogo raised a $75 million Series C at a reported $750 million (Axios, January 28, 2026) and a $160 million Series D on April 29, 2026. The roughly $2 billion valuation was reported by Bloomberg and others and never by Rogo.
• Diligence. Rogo's Rivanna acquisition (August 11, 2026) added data-room querying; the overlap grows from Rogo's side, and Hebbia answered with Max.
• Pricing. Both unpublished; circulating estimates are about $10,000 per Hebbia Professional seat (metronome, January 2026) and about $3,300 per Rogo seat (Sacra, directional only).

Neither is built for living coverage of public names with market data attached; that case is AllMind AI's.

Frequently Asked Questions

Does Hebbia have its own market data?

No. Hebbia reads the documents a firm loads or connects and reaches provider data through connectors to FactSet, S&P Capital IQ, PitchBook, Preqin and ICE Market Data, listed on its site (August 2026). Those connectors run on subscriptions the firm already holds, so a Matrix grid over public names carries no estimates, fundamentals or prices of its own. Teams that need market data beside the documents add Daloopa or move the coverage work to AllMind AI.

Can you use Hebbia and Rogo together?

Yes. The split that matches the products is Hebbia for reading large document sets in cited grids and Rogo for producing decks, comps and memos in the bank's format. Both are quote-only enterprise contracts, so running both means two procurement cycles and two permission models. Rogo's Rivanna acquisition on August 11, 2026 added data-room querying, which narrows the question to whether Rogo's diligence output is enough for the credit team.

What is Hebbia Matrix?

Matrix is Hebbia's spreadsheet-style workspace: documents down the rows, questions across the columns and a cited answer in each cell, built on its Iterative Source Decomposition retrieval. It runs across thousands of documents in one grid, which is why PE and credit teams use it for data rooms, credit agreements and fund documents. Since July 30, 2026 the Max agent sits on top and returns slides, reports or models from the same material.

How long does it take to evaluate a Hebbia alternative?

Plan 30 days with the 12-task rubric and your own documents. Week one loads a data room and a ticker list, weeks two and three run and score the tasks with two analysts, and week four is the security review of the audit log and permissions. Score the covenant, segment and re-run tasks first, because they expose the market-data gap fastest. A vendor that cannot run the pilot on your documents in that window has answered the question.

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.