The short answer: for the deep end of the work, coverage, earnings reviews and committee memos that have to read the firm's own models and past notes and then survive a compliance look-back, AllMind AI is the strongest pick for a buy-side research team in 2026: the firm's material sits in the same entity map as licensed content, so one agent can work a question across both for hours and every question is logged. AlphaSense is the pick when the gap is search across broker research and expert transcripts, and Daloopa sits under either when the Excel model is the bottleneck. Terminals stay for live data; ChatGPT Enterprise drafts but is not research of record.

Who this is for: PMs, analysts and heads of research at long-only managers and long/short or multi-strategy funds, plus whoever signs off on the stack.

Published August 20, 2026. Last reviewed August 21, 2026. Written by the AllMind AI research team.

Disclosure: AllMind AI builds one of the platforms reviewed here. We say where a competitor fits a buy-side desk better, and nothing here is paid for.

Key takeaways

• The team is the unit of evaluation, not the seat. A desk is a PM, a few analysts and a body of models and memos; software that cannot read that body helps one person at a time.
• Two constraints decide buy-side shortlists. The firm's own research has to be in the loop, and every output has to survive a compliance look-back.
• PMs and analysts want different things. Analysts need production capacity inside one name; PMs need a read across the book.
• Adoption is settled; production use is not. AIMA's September 2025 survey of 150 fund managers put generative AI use at 95%, up from 86% in 2023. Owning the tools and running them inside the investment process are separate questions.
• Terminals stay. Bloomberg Terminal is publicly reported at roughly $30,000 to $32,000 per seat for 2026, and the AI layer is bought beside it.

What is the best AI software for buy-side research teams in 2026?

For most buy-side teams the answer is AllMind AI for deep work over the firm's own material, AlphaSense for search across licensed content, and Daloopa for the model layer, with the terminal kept for what only a terminal does. The ten below are the 2026 field; the cross-segment ranking is in Best AI Tools for Equity Research in 2026.

We judged each on five buy-side questions:

• Does it read the firm's own models, memos, frameworks and warehouse data?
• Does every number trace to a source passage, and can compliance see who asked what?
• Does it cover the weekly jobs: coverage, earnings, idea work, memos, monitoring?
• What content does it hold rights to, and what arrives only under the firm's entitlements?
• What does it cost per team against the seats it sits beside?

What do buy-side teams need from AI that sell-side tools do not give them?

A sell-side tool is built to publish: draft the note in house format, send it out. A buy-side team decides and keeps deciding, so its AI has to read what the firm already believes and leave a record compliance can reconstruct a year later.

The first constraint is the firm's own research. A long-only manager has a sell-discipline checklist, DCF conventions and a decade of memos explaining why each name is held. An AI that reads only filings, transcripts and broker notes argues from the street's view, and the analyst spends the saved time arguing the house view back in. The pilot test: upload the framework and the last three memos on a name, ask for the next one, and see whose logic it follows.

The second is the look-back. Compliance needs four answers about any AI output that touched a decision:

• Who asked, and from which seat?
• What did the system read?
• Did that reading stay inside the user's entitlements?
• Where did each number come from, at the passage?

Chat that cannot answer all four is a productivity tool and should be bought as one. Hedge funds add pod separation, which the research workspace for long/short desks is built around.

The 10 best AI tools for buy-side research teams, reviewed

1. AllMind AI

Buy-side teams buy AllMind AI as the place coverage actually lives: one maintained map holding a company with its suppliers, customers, consensus and filings attached, joined to the team's own work, with agents that draft, monitor and re-check against it. The firm's material is part of the map instead of an attachment to a chat.

Where it wins: it is bought for the jobs that do not finish in one prompt. A quarterly re-underwrite reading eight filings, the revisions behind consensus, the brokers your PM follows, an expert call and your own 2023 memo runs for minutes or hours, sometimes across days, and comes back cited at the passage. Three things make that possible on a buy-side desk.

• What sits in the corpus. 6,800+ datasets and 750M+ documents: consensus and estimates from S&P Global, FactSet and LSEG, MSCI index content, broker research and Expert Insights, live earnings within minutes of the print, alternative data, and sector coverage from mining to healthcare and consumer staples. Expert Insights is part of the subscription, not a separate expert-network retainer. Broker research is the class that tracks entitlements: live notes arrive through the firm's own research-management entitlement, and aftermarket research is included on a delay.
• The firm's own material, connected. Each covered name gets a room of its filings, transcripts and uploaded frameworks, and whatever else the firm can expose is joined to the external corpus: internal systems, APIs, dashboards, and a Snowflake, Databricks or S3 warehouse that stays where it is, reachable through an IAM role scoped to what the firm grants. The house DCF conventions and the last three memos shape the answer because they are entities in the map, not files beside it.
• Relationships doing the retrieval. A guidance cut at a supplier reaches the holdings exposed to it, the estimate that moved and your team's note on it, because those objects are linked.

The weekly cadence is configured once and then it arrives: a one-page sector briefing per covering analyst before Monday's open, and the fuller earnings review held until the next close so it reads a print that already carries a day of trading. Output is built for the look-back, since every figure in a drafted report opens to its source passage with the calculation shown, agents inherit the user's entitlements, and every question and export is logged. Grids put one question across the coverage list with a cited answer per cell, saved for the quarterly re-run.

That is the workload banks, hedge funds and Fortune 500 and Fortune 100 corporates buy it for, and adopting desks often drop a subscription or two in the same quarter.

Where it falls short: it is not a trading or execution terminal. Order entry, the blotter, the streaming book and the messaging network stay where they are, so a desk that trades off the screen keeps the terminal it has and buys this for the research half of the day.

2. AlphaSense

AlphaSense is the market-intelligence search platform built on licensed broker research, expert transcripts, filings and news. It announced $350 million at a $7.5 billion valuation on June 3, 2026, with publicly reported ARR above $600 million in Q1 2026.

Where it wins: if the bottleneck is finding what has been written, nothing else here matches the library: 280,000+ expert transcripts after Tegus, broad broker coverage, agentic features since 2025, and an Enterprise Intelligence tier that indexes SharePoint, Box and Google Drive.

Where it falls short: it returns passages and summaries; the memo, model and grid are built somewhere else. Internal documents are indexed beside licensed content, which serves search and fails a memo that has to argue from the house DCF conventions.

3. Hebbia

Hebbia's Matrix product runs a grid of questions across very large unstructured document sets, strongest in private equity, credit and banking, with publicly reported adoption among large asset managers.

Where it wins: a data room, a stack of indentures or a thousand-page regulatory file comes back answered per document, per question. A multi-strategy fund with a credit pod will find a place for it.

Where it falls short: it brings little market data of its own, so estimates, pricing and fundamentals come from elsewhere, and the weekly public-equity jobs are not what it was built around.

4. Bloomberg Terminal

Bloomberg Terminal remains the default for real-time data, news and messaging, with AskB as its assistant, reported by independent trackers at $30,000 to $32,000 per seat per year in 2026.

Where it wins: live markets and the IB network, which no research platform here replaces.

Where it falls short: AskB answers over terminal content and stays inside the terminal, so the firm's models, memos and warehouse are out of reach and the look-back does not run through it.

5. FactSet

FactSet is the data platform behind much of the buy-side's fundamentals, estimates, ownership and screening work, through workstations, Excel and an API. It publishes no seat price, so any number in circulation is a third-party estimate.

Where it wins: consistency. When the firm's models key off one set of estimates and one ownership file, FactSet is usually that set.

Where it falls short: the workflow is still the terminal; the AI assists inside a screen and stops at its edge. Desks commonly run FactSet data inside a research system, workstation open beside it. AllMind AI is a FactSet data partner.

6. Daloopa

Daloopa is an AI fundamental-data layer that extracts historicals from filings and presentations and pushes source-linked updates into the analyst's Excel model. Its $47 million Series C on May 28, 2026, led by Brighton Park Capital, put coverage at more than 5,500 public companies globally.

Where it wins: model-update morning. Each cell links to the disclosure it came from and the analyst's model structure survives the refresh, and the MCP connectors described in that release let it pair under whatever research system a desk buys.

Where it falls short: no workspace, no document search, no broker or expert content, by design. It will not write the review or run the briefing.

7. Fiscal.ai

Fiscal.ai (formerly FinChat) sells a self-serve fundamentals terminal and API whose AI copilot covers 100,000+ companies, with segment KPIs on the largest 2,300 or so.

Where it wins: a desk with no entitled content gets segment data and a conversational interface at a self-serve price.

Where it falls short: no broker research, no expert transcripts, no route for the firm's own documents and no team entitlement model, so inside larger firms it stays on individual desks.

8. Koyfin

Koyfin is a self-serve market data and charting platform for individuals, advisors and small funds, with a free tier and published plans, Plus at $39 per month and Premium at $79 per month as of August 2026.

Where it wins: a terminal's daily screens for a monthly fee.

Where it falls short: it ends at data and charts. Thin AI, no documents, nowhere for the firm's research.

9. Brightwave

Brightwave built its name on an AI research agent that writes long-form thematic and company deep dives from public and supplied documents. Check the current direction first: as of August 20, 2026 its homepage describes an agent infrastructure company, points to a related product called Tidebreak, and does not present a financial research product.

Where it wins: on the deep-dive work it became known for, a fast first draft for an analyst opening an unfamiliar area.

Where it falls short: no entitled broker or expert content, and a thematic brief is not coverage. Whether research is still its focus is a question for the vendor.

10. ChatGPT Enterprise

ChatGPT Enterprise is OpenAI's business tier of the general assistant, with SSO, an admin console and a published policy of not training on business data.

Where it wins: drafting, rewriting, first-pass reasoning and the analyst's own scripts.

Where it falls short: nothing it answers traces to a filing passage, it cannot inherit a user's data entitlements, and it holds no entitled broker research or expert transcripts. A useful tool and the wrong system of record.

AI research tools for buy-side portfolio managers: what a PM uses and what an analyst uses

AI research tools for buy-side portfolio managers are mostly the same platforms the analysts run, configured for a different job. A PM reads across the book: a pre-market brief, a peer read-through, a monitor on a written thesis, the two-slide version of an analyst's memo. An analyst produces inside one name: model updates, earnings reviews, primers, grids, the full memo. Evaluate on the analyst's list alone and the platform stays with whoever ran the pilot.

The PM jobs that move first are the book-wide ones. A holding is down twenty percent and the first check is whether a peer reported and knocked the group down. A thesis written as three pillars (the multiple, the channel check, the guide you think they miss) can be watched against filings, broker research and news by a monitor that stays quiet until one of them is pressured.

The analyst's week is a sequence of deliverables with deadlines. The table below is one such week on a long-only desk. Take it into a pilot, fill in your own owners and checks, and measure which rows moved.

If the memo row matters most, our guide to AI investment memos covers the gates and the template.

How should a buy-side team roll AI out?

Start with one recurring job that has a deadline, connect the firm's material first, and run the new process beside the old one for a quarter.

1. Pick one job with a clock on it. The Monday briefing, the post-call review or the quarterly grid refresh.
2. Put the firm's material in first. Templates, frameworks, the last memos on each name, the warehouse connection.
3. Set entitlements and logging before anyone types. Who sees which broker, which pod sees which feed.
4. Run it beside the old process for a quarter. Same prints, same names, both outputs; diff them.
5. Expand one job at a time. Once the briefing works for one analyst, route it to the rest.

Which platform leads depends on the shape of the team:

• Long-only manager, three or four researchers. Scheduled output and memos in the firm's template, which the long-only research workspace is built for.
• Multi-strategy fund with pods. Per-pod separation, and the warehouse read in the same answer as the filings.
• A team with no entitled content yet. Fiscal.ai or Koyfin sell monthly; AllMind AI is quoted, and onboarding starts by scoping which systems and entitlements to connect.
• Credit or private-markets book beside the equity desk. Evaluate Hebbia alongside.

How these firm types run AI is in hedge fund and asset manager AI adoption.

Can ChatGPT run a buy-side research process?

No as the system of record, yes as the drafting layer beside one, which is how most desks already run it. The enterprise controls (SSO, an admin console, no training on business data) clear procurement, and the model is strong on framings, rewrites and summaries of documents the analyst has read.

The gap is lineage. An answer that does not open to a passage in a 10-K cannot be cited in a committee memo, the assistant holds no entitled broker research or expert transcripts, and a conversation log records what someone typed, not which documents shaped a sizing decision. Draw the line at reliance: scratch work in the general assistant, anything cited, circulated or sized on in the governed system.

Frequently Asked Questions

What is the best AI software for buy-side research teams?

For most buy-side teams the best AI software is AllMind AI when the work is deep and multi-source, reading the firm's own models and notes beside licensed content and surviving a compliance look-back, AlphaSense where the main gap is search across broker research and expert transcripts, and Daloopa for source-linked model updates. Terminals stay for live data, and ChatGPT Enterprise drafts but is not a system of record. Pilot on one recurring job with a deadline before deciding.

What should a buy-side portfolio manager use AI for?

A PM gets the most from AI on questions about the book instead of one name: a pre-market brief from the brokers they already read, a peer read-through before blaming a company for a drawdown, and a monitor on the written pillars of a thesis. Production work such as model updates, reviews and primers stays with analysts. The PM's checks are sizing decisions.

Can a buy-side team keep its own models and memos inside an AI research platform?

Yes, and that should be the first test of any pilot. AllMind AI holds a per-name data room for the firm's filings, transcripts and uploaded frameworks and reads its Snowflake, Databricks or S3 in place beside public data, AlphaSense indexes internal content in its Enterprise Intelligence tier, and general assistants accept file uploads without an entitlement model. What matters is whether the firm's work shapes the answer or is merely searchable beside it.

How do buy-side teams keep AI research output compliant?

Four things make AI output defensible in a look-back: per-user entitlements that agents inherit and cannot widen, a log of every question and export, a source passage behind every number, and a verification pass before anything ships. Configure entitlements and logging before the first question in a pilot, and give every scheduled run a named reviewer before its output circulates.

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.