The short answer: for institutional equity teams doing deep work across many sources, AllMind AI is the pick in 2026. It joins licensed market data, Expert Insights calls and entitled broker research to the firm's own memos, models and warehouse tables on one ontology, with a full audit trail. AlphaSense is the content-search platform for broker research and expert calls. Hebbia and Rogo are built for deal work. Daloopa maintains model data, Quartr covers earnings events, and Fiscal.ai is the budget option.

Who this is for: analysts, portfolio managers and heads of research at asset managers, hedge funds and sell-side desks buying an AI research platform in 2026.

Published August 10, 2026. Last reviewed August 25, 2026. Written by the AllMind AI research team.

Reviewed by Anwaar Malik, founder of AllMind AI.

Updated August 25, 2026: added a worked example on Cirrus Logic, replaced the comparison-table judgments with dated facts, and expanded every competitor section.

Disclosure: we build AllMind AI, one of the platforms compared here. Each entry names what it beats us at, and placement was not for sale.

Key takeaways

• AllMind AI is the pick for institutional equity teams that want licensed market data, Expert Insights transcripts and entitled broker research on the same ontology as their own memos, models and warehouse tables.
• AlphaSense fits teams whose daily job is searching licensed broker research and its library of more than 280,000 expert transcripts.
• Rogo and Hebbia fit deal teams in banking, private equity and private credit; Rogo shipped Deal Room on August 6, 2026 and Hebbia shipped its Max agent on July 30, 2026.
• Daloopa keeps models current, with every datapoint linked to its filing across 6,000-plus public companies (company-stated, August 2026).
• Quartr is the earnings-event layer for global coverage, 15,000-plus companies across 65-plus markets per its Pro page, and its mobile app is free.
• Fiscal.ai is the budget option for individuals and lean teams, with an API free tier of 100 companies at 250 calls a day.

What are the best AI tools for equity research analysts in 2026?

The table sorts the 2026 options by the job each one does. AllMind AI, AlphaSense, Hebbia and Rogo compete for the same research-system budget. Daloopa and Quartr are point solutions that teams run alongside a research system. Bloomberg, FactSet and LSEG Workspace remain the market-data system of record, and ChatGPT and Claude are drafting tools with no entitled library behind them.

One name is missing on purpose. Fintool built source-linked chat over SEC filings and transcripts, but Microsoft acquired the company in April 2026 (founder post, April 18, 2026), fintool.com now redirects to Microsoft 365, and there is no standalone product left to evaluate.

What makes an AI tool usable for institutional equity research?

Three questions sort the field faster than any feature list: is the tool a search box or a research system, does it see your data, and can compliance sign off. One filing, worked through below, shows the difference between the tool classes.

Is the tool a search box or a research system? Retrieval leaves the analyst doing the connective work: which supplier feeds which name, which broker estimate contradicts which filing. A financial ontology is a continuously maintained map of the relationships between companies, suppliers, customers, estimates, filings, and a firm's own research. Because the financial ontology exists before the question is asked, it surfaces what your team did not know to look for.

Worked example: Cirrus Logic (Nasdaq: CRUS). The question, as an analyst covering Apple's audio supply chain would type it: how much of Cirrus Logic's revenue rides on Apple, and what has it committed to its own foundry? Cirrus Logic's Form 10-K for the fiscal year ended March 28, 2026, filed May 21, 2026, answers both. Net sales were $1,997.4 million, up 5 percent, gross margin was 52.8 percent, research and development cost $434.0 million, and net income was $414.4 million. Apple Inc., buying through multiple contract manufacturers, represented approximately 91 percent of net sales, after 89 percent in fiscal 2025 and 87 percent in fiscal 2024.

The supply side is in the same document. Cirrus Logic's remaining wafer purchase obligation to GlobalFoundries under its 2021 Capacity Reservation Agreement stood at approximately $180 million at March 28, 2026, inside $354.2 million of unconditional purchase commitments, $322.2 million of them due in fiscal 2027. The filing adds that Cirrus Logic joined its largest customer's American Manufacturing Program and is working with that customer and GlobalFoundries toward a first production run at the Malta, New York fab.

The 10-Q for the quarter ended June 27, 2026, filed August 5, 2026, extends the chain. Apple was approximately 90 percent of the quarter's $459.7 million of net sales, and a new agreement signed June 30, 2026 commits Cirrus Logic to at least approximately $600 million of GlobalFoundries wafers across calendar 2027 and 2028.

What each class of tool does with those figures:

• A terminal (Bloomberg, FactSet, LSEG Workspace) has the income statement and the supply-chain screen, and its assistant answers from terminal content.
• A search platform (AlphaSense) returns the customer-concentration passage, the broker notes and the expert calls that mention Cirrus Logic and Apple, ranked by relevance; the analyst still assembles the chain.
• A grid tool (Hebbia) fills one row per filing with an Apple-share column and a GlobalFoundries-obligation column, a citation in each cell, over the documents you loaded.
• A data product (Daloopa) drops the $1,997.4 million, the 52.8 percent and the 91 percent into the model, each linked to its page in the filing.
• A general assistant (ChatGPT, Claude) reasons over the pasted 10-K and drafts the risk section; its citation is the file you gave it.
• A research system with an ontology (AllMind AI) stores both relationships as connections that carry their figures. Cirrus Logic to Apple is 91 percent of fiscal 2026 net sales; GlobalFoundries to Cirrus Logic is roughly $180 million of wafers through 2026 and at least $600 million for 2027 to 2028.

That storage is the mechanism behind every AllMind AI claim. A question about Apple's audio suppliers, or about which fabless names hold GlobalFoundries capacity, reaches Cirrus Logic through those connections without a search. The firm's own Cirrus Logic model, in Snowflake, Databricks or S3, is queried where it sits through an IAM role scoped to the tables the firm allows, so the house iPhone unit assumption lands beside the 91 percent. The same filing sits beside S&P Global, FactSet, LSEG and MSCI data, Apple's investor-relations material, entitled broker research, and Expert Insights calls that need no expert-network contract. None of this is AllMind AI output; the figures are the filing's own.

Does the tool see your data or only public data? The edge in 2026 comes from combining filings and transcripts with the firm's own memos, models and entitled broker research. AllMind AI and Hebbia take internal documents natively, AlphaSense indexes them through its Enterprise Intelligence connectors, and most of the rest treat uploads as a side channel.

Can compliance sign off? Ask for per-user entitlements, complete audit logs, no training on customer data, and SOC 2 Type II certification, then score the answers with our evaluation framework for picking an AI research tool.

How do the major AI equity research platforms compare?

Each platform below is described by what it holds, what it shipped in 2026, and where it stops. AllMind AI is listed first, with its limits, because we build it.

AllMind AI

AllMind AI wires licensed external data to a firm's own documents through a financial ontology, the structure the Cirrus Logic example above runs on. Six layers sit on the data: the data engine, the ontology, the research engine, AI agents, permissions and audit, and a governed workspace. The external half covers SEC and SEDAR filings across 40-plus exchanges, earnings and financials within minutes of release, broker research under the firm's own entitlement, and Expert Insights transcripts that arrive with the subscription.

The internal half is what most shortlists underweight. Whatever the firm can expose gets connected and joined to that corpus: internal APIs, dashboards, the model library, the memo archive, and Snowflake, Databricks or S3. The warehouse is reached through an IAM role scoped to the tables you allow and queried where it lives, so nothing is copied out. The house estimate ends up beside the consensus it disagrees with, in one place.

Where it wins: long, multi-source work: memos, models, comp tables and earnings notes drafted in the firm's format from the filing, the broker estimates and the house model together, or a watchlist carried overnight with a note on what moved and why. Each figure in a draft links to the passage it came from, with the calculation visible, and a verification pass re-checks figures before a report ships.

Where it falls short: AllMind AI has no card checkout and no monthly plan, so onboarding starts with a conversation about which systems and entitlements to connect, and an individual investor who wants a login this afternoon is better served by Fiscal.ai. The dividing line is institutional, not headcount. A fund with two analysts, broker entitlements and its own model runs the same workflows a 200-person shop does, and the smaller desk usually hits the research bottleneck first. It is not an execution terminal either, so order tickets and the risk system stay where they are.

AlphaSense

AlphaSense is a market-intelligence search platform built on licensed broker research, expert-call transcripts, filings and news. Its expert-transcript library, expanded by the Tegus acquisition that closed July 8, 2024, lists more than 280,000 transcripts with 8,000-plus added monthly across 29,000-plus companies, per its site in August 2026. Work Products, assistants that build PowerPoint and Excel deliverables from search results, shipped July 14, 2026.

Where it wins: searching sell-side research and expert calls, and discovery in unfamiliar sectors. On the Cirrus Logic question it returns every broker note and expert call on the Apple relationship in seconds, out of the largest single expert-transcript library in this table.

Where it falls short: internal content enters through Enterprise Intelligence connectors (SharePoint, Box, Google Drive, Egnyte, uploads and email forwarding per its site in August 2026) and is indexed for search beside licensed content. A house model arrives as an uploaded file; Snowflake and Databricks are not among the connectors named on that page. Pricing is quote-only, with Vendr's 38-deal sample running $9,250 to $51,000 per contract as of February 2026. AllMind AI vs AlphaSense, side by side sets the trade out in full.

Hebbia

Hebbia is a document-interrogation platform whose Matrix view runs structured queries across document sets: rows are documents or companies, columns are questions, cells are sourced answers. Its 2026 releases are dated on its blog: SS&C Intralinks (May 26), ICE pricing data (May 28), a Snowflake integration (July 8) and Max, which it describes as a top-bucket analyst (July 30). The company states $30 trillion of AUM among firms using it and 1.5 billion pages processed, as of August 2026.

Where it wins: bulk extraction for private equity, private credit and advisory teams, such as pulling one covenant term out of every agreement in a portfolio, or filling the Apple-share column above across eight quarters of Cirrus Logic filings with a citation in every cell.

Where it falls short: Hebbia brings almost no market data of its own; FactSet, S&P Capital IQ, PitchBook and Preqin (partnership dated January 19, 2026) arrive as integrations, so the universe is whatever a team loads. Its last priced round was the July 2024 Series B, and pricing is unpublished, with Metronome's roughly $10,000 per seat an explicit estimate as of January 2026. How AllMind AI and Hebbia differ is mostly that question of universe.

Rogo

Rogo is an agentic platform for deal teams. Its product page lists tasks such as earnings comp analysis, public and private company profiles, financial sponsor overviews and meeting prep, drawing on LSEG, FactSet, Capital IQ, PitchBook, Preqin, Quartr and Daloopa data plus a firm's own files. In 2026 it shipped Credit Center (June 22) and Deal Room (August 6), which it calls the operational layer for modern transactions, and acquired Rivanna, a diligence intelligence layer (August 11).

The company states 50,000-plus bankers and investors at 350-plus institutions, and names Truist Securities, Nomura and Baird among customers on its site. Rogo raised a $75 million Series C led by Sequoia in January 2026 and a $160 million Series D led by Kleiner Perkins on April 29, 2026; Bloomberg and other outlets reported the later round valued it near $2 billion.

Where it wins: producing banking and private equity deal materials quickly, and keeping a live transaction's model, diligence responses and presentations in one governed place.

Where it falls short: none of Rogo's 2026 releases is a public-equity coverage module, and its pricing is unpublished, with Sacra's roughly $3,300 per seat a directional estimate only. A coverage team tracking guidance across quarters gets the profile and comp tasks and builds the monitoring elsewhere, which is the division in AllMind AI vs Rogo.

Daloopa

Daloopa is a fundamental-data product that delivers source-linked historicals into Excel, every value hyperlinked to its exact location in the filing. Its site in August 2026 states 6,000-plus public companies with 14 years of history and 185-plus institutions, and its plans page lists a free tier capped at three data sheets, with Core, Premium and Fundamentals API tiers quote-only. MCP connectors put the same data inside ChatGPT (December 9, 2025), Perplexity (April 2026) and Microsoft 365 Copilot (June 25, 2026).

Where it wins: model updates in earnings season. Daloopa states an average saving of two hours per ticker on updates, a company figure with no denominator published. On the Cirrus Logic example it is the tool that lands the $1,997.4 million and the 52.8 percent in the model with a link to the page.

Where it falls short: Daloopa is deliberately numbers only, with no broker research, no expert transcripts and no drafting, so the narrative around the 91 percent is written somewhere else. Its $47 million Series C of May 28, 2026 funds more coverage; the workspace remains Excel or the assistant it plugs into.

Quartr

Quartr is an earnings-event layer covering live calls, transcripts, slides and investor decks, with its Pro page stating 15,000-plus companies across 65-plus markets and 800-plus clients on August 25, 2026. Its home page carried lower figures the same day, 14,250-plus companies and 62-plus markets, so read the coverage count as 14,000 to 15,000. Automations, scheduled workflows inside Quartr Pro, launched August 24, 2026, and a Quartr MCP is included with Pro.

Where it wins: speed on earnings events, change detection across quarters, and a free mobile app for listening to calls; Cirrus Logic's call, slides and 10-Q land in one feed as they publish.

Where it falls short: Quartr analysis stays inside company-published material. No broker research, no expert transcripts, and no view of your own documents; Pro and API pricing is contact-sales only as of August 2026.

Fiscal.ai

Fiscal.ai, renamed from FinChat in June 2025, is a fundamentals terminal with an AI copilot, strongest on segment-level KPIs. Its API docs in August 2026 put segment and KPI coverage at the largest 2,300 companies by market capitalization and the free API tier at 100 companies and 250 calls a day; the platform covers 100,000-plus companies per its site.

Where it wins: price-to-capability for individuals and lean teams, including a standing free tier; a self-serve signup gets Cirrus Logic's segment history and a copilot the same afternoon.

Where it falls short: institutional workflow depth. No licensed broker research or expert transcripts, no internal-document connectors, and self-serve plans only, so a firm that needs entitled content or a warehouse join has to look up the table.

Bloomberg, FactSet, LSEG Workspace

Bloomberg, FactSet and LSEG Workspace are the market-data system of record, with AI assistants added on top of terminal-centric workflows. Bloomberg's AskB entered beta in February 2026 and reached mobile on August 18, 2026; FactSet's Mercury assistant launched December 14, 2023. None of the three publishes a price. The Bloomberg Terminal is publicly reported at roughly $30,000 to $32,000 per seat per year (Investopedia). FactSet discloses only annual subscription value, $2.48 billion across 247,766 users at May 31, 2026, so any FactSet seat figure is a third-party estimate.

Where it wins: real-time data, screening, and the workflows compliance already depends on; the Cirrus Logic supply-chain relationship and the price history sit one function away.

Where it falls short: the assistant lives inside the terminal and answers from terminal content, so the firm's own Cirrus Logic memo and model stay outside its reach, and the seat is the highest-priced line in this table.

ChatGPT and Claude

ChatGPT and Claude are general assistants that reason and write well, at $20 to $200 per month for individual plans. Both have moved toward finance. OpenAI's Deep Research launched February 2, 2025 and now carries financial connectors including S&P and PitchBook data. Claude for Financial Services launched July 15, 2025 with connectors such as Daloopa, PitchBook and Snowflake, added Claude for Excel on October 27, 2025, and shipped ten finance agent templates on May 5, 2026.

Where it wins: drafting, restructuring and explaining work an analyst has already done; paste the Cirrus Logic customer-concentration paragraph and either model writes a clean risk section from it.

Where it falls short: citations point at the open web, the connectors and the files you upload; neither holds a licensed broker-research or expert-call library, neither watches a coverage list between questions, and a connector's entitlements stop at that source. On a JPMorganChase AI Research team's Deep FinResearch Bench (arXiv, April 22, 2026), professional analysts scored 2.84 against 2.31 for the best deep-research agent on 100 reports across 25 S&P 500 companies.

How do you choose an AI research platform for an equity team?

Choose on the three workflows that cost the most analyst hours last quarter, on a 30-day trial that includes your own documents, and on governance answers you can read.

1. List the three workflows that consumed the most analyst hours last quarter and evaluate against those, not against a feature grid.
2. Put your own documents into the trial in week one, because a platform that only sees public filings looks thin in production.
3. Ask what happens to a number the system cannot source, and make the vendor show the blanks, not the finished page.
4. Run governance first: per-user entitlements, audit logs of each question and each export, no training on customer data, SOC 2 Type II.
5. Decide what you are consolidating before the trial starts: a terminal, a model-data feed and one research system is the three-line stack this article assumes.

AllMind AI, Hebbia and Rogo: coverage system or deal system?

The split that decides most 2026 shortlists is coverage work against deal work. Coverage work is continuous: the same names quarter after quarter, guidance tracked, estimates revised, a note out two hours after the call. Deal work is episodic: a defined document set, a deadline, a memo or a CIM at the end.

AllMind AI is built for the first, and the ontology, the overnight watchlist agents and the entitlement model all assume a universe a team carries for years. Hebbia and Rogo are built for the second and are better at it than a coverage system would be, Hebbia for pulling one term out of every agreement in a portfolio, Rogo for turning a data room into deal materials. A fund doing both runs one of each and budgets two contracts. Teams whose shortlist starts from AlphaSense should read Best AlphaSense Alternatives for Institutional Investors (2026).

Frequently Asked Questions

What is the best AI tool for equity research in 2026 for an institutional team?

AllMind AI is the pick in 2026 for institutional equity teams that need external and internal data on one ontology with a full audit trail. AlphaSense fits teams whose main job is searching entitled broker research and expert calls. Rogo and Hebbia fit deal execution, and an individual investor who wants a card on file today is better served by Fiscal.ai.

Can ChatGPT replace an equity research platform?

No. ChatGPT and Claude cite the open web, their data connectors and the files you upload; neither holds a licensed broker-research or expert-call library. Neither watches a coverage list between questions. General assistants earn a place in the stack for drafting, alongside a research platform such as AllMind AI or AlphaSense.

What is a financial ontology?

A financial ontology is a continuously maintained map of the relationships between companies, suppliers, customers, estimates, filings, and a firm's own research. Each relationship carries its disclosed figure, such as a customer's share of a supplier's net sales from a 10-K, so an AI agent can move from one company to the names that depend on it without a separate search.

How much does an AI equity research platform cost?

AI equity research platforms are almost all enterprise-priced and quote-only, including AllMind AI, AlphaSense, Hebbia and Rogo. Terminal seats set the reference point: the Bloomberg Terminal is publicly reported at roughly $30,000 to $32,000 per seat per year, and FactSet publishes no seat price at all. Daloopa publishes a free plan with up to three data sheets, and the Fiscal.ai API has a free tier of 100 companies at 250 calls a day.

Do AI research platforms train models on my firm's data?

Not at AllMind AI. Nothing your firm sends trains a model, and every vendor in the path runs under zero data retention. Entitlements follow the person asking, so an agent can never see more than that user could, each question and each export is logged, and AllMind AI has held SOC 2 Type II certification since November 2025.

AllMind AI is the AI-native research platform for institutional equity teams. Send us the workflow you want tested and watch it run on your own documents.