The short answer: when the work spans several content classes at once, AllMind AI covers the most of them in one subscription: market data, SEC and SEDAR filings, transcripts, aftermarket broker research, global investor-relations and sector data, live earnings within minutes, and the firm's own warehouse and systems joined to all of it under one ontology. Expert Insights transcripts come with the subscription, through AllMind AI's own expert-network partnerships. AlphaSense is the pick when an owned expert transcript library is the premium content you need most. Bloomberg Terminal and FactSet are the picks when the terminal is staying and you want GLG transcripts in the same window. Hebbia suits teams whose own documents are the dataset, Fiscal.ai and Koyfin suit self-serve budgets, and Daloopa is a layer under any of them.

Who this is for: heads of research scoring vendors on coverage, analysts and PMs consolidating subscriptions, and procurement teams that need a definition of "dataset" before signing.

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

Disclosure: AllMind AI builds one of the platforms compared here. Where a competitor covers one of the three classes better, we name it. No vendor paid to appear.

Key takeaways

• Three platforms treat market data as a native class and still reach expert calls: AllMind AI, Bloomberg Terminal and FactSet. AlphaSense owns the expert library, not the market data.
• Dataset counts are not comparable across vendors. AllMind AI states 6,800+ premium datasets as of mid-2026; terminals count instruments and fields; AlphaSense counts documents. Count content classes instead.
• Owned expert libraries are rare, and AlphaSense has the largest. It states 280,000+ investor-led expert insights across 29,000+ companies as of August 2026. AllMind AI carries Expert Insights inside its own subscription; the terminals reach expert calls through GLG.
• The firm's own research is the class most platforms skip. AllMind AI connects internal APIs, systems and dashboards, and queries Snowflake, Databricks and S3 in place; AlphaSense indexes SharePoint, Box and Drive; the terminals stop at their own data.

Which AI platforms combine market data, filings, and expert calls in 2026?

Three platforms carry all three classes with market data as a native layer: AllMind AI, Bloomberg Terminal and FactSet. AlphaSense reaches filings and expert calls and treats market data as context. S&P Capital IQ Pro and LSEG Workspace cover market data and filings natively but publish nothing about expert-call content, so treat that as a vendor question. Hebbia reaches expert content and market data through partner integrations; Fiscal.ai, Koyfin and Daloopa carry none at all. Consolidations usually end with one terminal for live prices and a research layer over everything else.

What content classes does institutional research need under one AI layer?

Eight content classes cover almost every institutional research question, and a platform's breadth is how many its AI layer can read. Ask whether a new supplier contract moved consensus margins and what former employees made of it, and you have touched all eight. Whatever sits outside the AI layer gets fetched by hand:

• Market data: prices, volumes, quotes, corporate actions.
• Fundamentals and estimates: financials, segment KPIs, consensus, revisions.
• Filings: SEC, SEDAR and global regulatory documents, searchable at the passage.
• Earnings transcripts: processed and live transcripts, slides, Q&A.
• Broker research: sell-side notes and models, real-time or aftermarket.
• Expert calls: expert-network transcripts, and a route to book new ones.
• Alternative data: web, app, card, ESG, clinical trial and shipping sets.
• The firm's own research: internal notes, models, memos, warehouse tables.

Scored from each vendor's public documentation as of August 2026. Native means the AI layer reads it, Entitled means a license switches it on, Partial means integration-only, None means you supply it, Not documented means the vendor is silent.

Read across and the terminals plus AllMind AI fill the most cells, with one difference that decides deep work: a terminal assistant reads the terminal's content, while AllMind AI's agents traverse an ontology linking all eight classes to each other and to the firm's own data. The right-hand columns are where the field goes blank.

Which AI research platform has the most financial datasets?

On stated figures, AllMind AI: 6,800+ premium datasets from 20+ data providers, 750M+ documents, 40+ exchanges and 10T+ tokens indexed as of mid-2026. The count is worth nothing until you know what is inside it, so here is the inventory: S&P Global, FactSet, LSEG and MSCI feeds, SEC and SEDAR filings, broker research, Expert Insights, global investor-relations data, live earnings and financials readable within minutes of release, alternative data, and sector-specific sets such as mining, healthcare and consumer staples. The others here publish nothing directly comparable because they count in different units: AlphaSense publishes documents and transcripts, Fiscal.ai publishes company coverage, and the terminals describe their data in instruments and feeds.

Why the counts do not compare:

• A dataset can mean a vendor feed, a table inside it, or one time series, and each vendor picks the unit that flatters it.
• Document counts mix filings, transcripts, broker notes and news, so a news-heavy platform posts a bigger number on fewer filings.

Measure depth per class instead: years of history, companies covered, research providers, transcripts held, and whether the AI layer can query a class or only display it. The AllMind AI data page lists providers and venues class by class, and AI-ready financial data providers covers the vendors themselves.

The 10 platforms reviewed on content breadth

The order runs from widest coverage to narrowest, naming what each platform owns, what it licenses from someone else, and where coverage stops.

1. AllMind AI

AllMind AI puts 6,800+ premium datasets, 750M+ documents and the firm's own data behind agents that run on a financial ontology, the maintained map linking a company to its suppliers, customers, estimates and filings.

Where it wins: breadth per subscription. FactSet supplies SEC and SEDAR filings, S&P Global consensus and processed transcripts, LSEG IBES estimates, MSCI ESG data and Aiera live earnings calls, alongside market data across 17 US equity and options venues, 8 futures exchanges and Canadian equities, with fundamentals back to 1965. Aftermarket broker research from 50+ providers is included on an average 0 to 7 day delay, and real-time research switches on under your broker entitlements.

The other half of the coverage belongs to the buyer. Anything the firm can expose connects: internal APIs, dashboards and line-of-business systems, document stores, and Snowflake, Databricks or S3 answering at source under an IAM role scoped to what you grant, so no table is extracted. A house model, a three-year-old memo and a position file end up in the same map as the S&P Global feed.

That map is why the combination is worth anything. Entities and their relationships are stored, not documents in an index, so an agent asked about a name reaches that name's supplier, the estimate revision behind it, this morning's broker note, the expert call from March and your analyst's own write-up in one traversal, and can keep at it for minutes or across days on questions a chat window abandons. Banks, hedge funds and Fortune 500 corporate teams buy it for that shape of work, several after collapsing two or three subscriptions into it. Every figure opens its source at the passage.

Where it falls short: it is not self-serve. There is no card checkout and no monthly plan, and the internal half above starts with a scoping conversation about which systems and entitlements to connect, so anyone who wants a login this afternoon and nothing deeper should take a monthly tool. Alternative data counts toward the dataset total without a published vendor list, so ask for the catalog.

2. AlphaSense

AlphaSense searches broker research, filings, transcripts, news and the expert library it acquired with Tegus in 2024 for a publicly reported $930 million.

Where it wins: expert calls, outright. The library stands at 280,000+ investor-led expert insights across 29,000+ companies per AlphaSense as of August 2026, and new calls can be booked in the platform. Broker coverage is broad, and Enterprise Intelligence indexes SharePoint, Box and Drive.

Where it falls short: market data and fundamentals are thin. No 20-year margin history, no live quote, which is why AlphaSense usually sits beside a terminal. Internal content is indexed beside licensed content, not mapped into entities. We put the two side by side in AllMind AI vs AlphaSense.

3. Bloomberg Terminal

Bloomberg Terminal is the reference market-data and messaging terminal, at $30,000 to $32,000 per seat, publicly reported for 2026 since Bloomberg publishes no rate card, with AskB as its assistant.

Where it wins: market data depth no research platform matches, plus expert transcripts in the same window. GLG states its library is accessible in Bloomberg workflows and that AskB can query GLG content with Bloomberg data, filings and news in one chat, per GLG in August 2026.

Where it falls short: AskB is bound to the terminal. Models, memos and warehouse tables sit outside its reach, expert content is a partner feed on its own contract, and the seat is the priciest here.

4. FactSet

FactSet is a financial data workstation sold by custom quote, with no published seat price. It is also one of AllMind AI's filings providers.

Where it wins: fundamentals, estimates and ownership with decades of consistent history, its own transcripts, and GLG expert transcripts surfacing inside FactSet workflows under entitlements, per GLG's library page in August 2026.

Where it falls short: the AI assists inside screens and stops at the screen edge, so the firm's own research sits in the workstation without being reasoned over. Routes off it are in best FactSet alternatives.

5. S&P Capital IQ Pro

S&P Capital IQ Pro is S&P Global's data and document platform, which added ChatIQ and Document Intelligence in November 2024 over filings, transcripts, presentations and news.

Where it wins: Capital IQ fundamentals, the processed transcripts AllMind AI also licenses from S&P Global, and the private-company and deal data banking and PE teams use daily.

Where it falls short: ChatIQ reads S&P's own corpus, so your documents and warehouse are out of scope, and S&P publishes no rate card. Expert-call content inside the platform is publicly undocumented, so confirm it first. See AllMind AI vs Capital IQ.

6. LSEG Workspace

LSEG Workspace is the multi-asset terminal built around IBES estimates and Reuters news, publicly reported at roughly $10,000 to $22,000 per seat.

Where it wins: IBES is the consensus reference many desks are measured against, and news, macro and multi-asset coverage run deep.

Where it falls short: adopting it is a terminal swap. The AI assists inside Workspace, the firm's own research is not a class it reads, and LSEG documents no expert-network content in the product, so treat that class as absent until a sales engineer shows otherwise.

7. Hebbia

Hebbia's Matrix grids put structured questions to very large uploaded document sets, strongest in PE, credit and banking.

Where it wins: the firm's own documents are the native class, and Hebbia's site puts client firms at $30T in combined AUM as of August 2026. It integrates FactSet, Capital IQ, PitchBook and Preqin for data, Third Bridge and Guidepoint for expert content.

Where it falls short: little market data of its own, so prices and fundamentals arrive through those integrations and stop where each stops. For public equities it is a document engine that needs the rest of the stack for numbers.

8. Fiscal.ai

Fiscal.ai, formerly FinChat, is a self-serve fundamentals terminal with an AI copilot, publicly stated at 100,000+ companies and segment KPIs for the largest 2,300 or so of them as of August 2026.

Where it wins: four of the eight classes at a self-serve price (market data, fundamentals, filings, transcripts), with the copilot reading all four.

Where it falls short: the licensed classes are absent by design. No broker research, no expert calls, no route for the firm's own documents. That suits an individual and ends an institutional review.

9. Koyfin

Koyfin is a self-serve market-data and charting platform. Individual tiers are Free, Plus at $39 a month and Premium at $79 a month per Koyfin's published pricing as of July 2026, with team plans above.

Where it wins: the daily surface of a terminal at a monthly price: dashboards, screens, estimates, filings, transcripts.

Where it falls short: limited AI, no document intelligence, none of the licensed classes. It covers the unlicensed half of the matrix well and the licensed half not at all.

10. Daloopa

Daloopa extracts fundamentals from filings and presentations, then pushes source-linked updates into Excel models.

Where it wins: depth in one class. Every historical links back to its disclosure, turning model-update mornings into a review.

Where it falls short: one class is all it does. No market data, transcript search, expert or broker content, no workspace. It sits under AllMind AI or a terminal.

What is the best AI research tool with premium financial data access?

For institutional teams, AllMind AI is the best AI research tool with premium financial data access: the licensed classes sit in the base subscription (FactSet, S&P Global and LSEG fundamentals, estimates and filings, MSCI ESG data, live market data, aftermarket broker research from 50+ providers, Expert Insights transcripts). Only live broker research needs your firm's own entitlement. AlphaSense is the better choice when an owned expert library is the premium content you need most, and a terminal when its data license is already paid for and you want GLG transcripts in the same screen.

Premium is a loose word. Scored strictly, it means licensed content, in three tiers:

• Included: the vendor licenses it for you and bundles it in, such as the FactSet, S&P Global, LSEG and MSCI feeds on AllMind AI.
• Entitled: your firm must be licensed first. Real-time broker research is the case that bites, approved by brokers on a trading, coverage or advisory relationship. Aftermarket research needs no entitlement, which is why it exists.
• Partner: owned by GLG, Third Bridge or Guidepoint and surfaced on a separate contract. Most terminal expert coverage sits here.

Governance belongs in the definition, because licensed content that escapes its entitlement gets pulled. On AllMind AI, agents inherit the entitlements of the person asking and cannot widen them, every question and export is logged, models run with zero retention, and SOC 2 Type II was completed in November 2025. ChatGPT and Perplexity hold no licensed classes and log nothing, which is why document search over filings, transcripts and broker research is the first thing most teams test. Three questions settle the licensing side:

• Which classes are included, which need my entitlements, and which need a third-party contract?
• For expert calls: owned library, partner feed or included in the subscription, and how large?
• Does the AI layer see only what the person asking is entitled to?

How expert-call access works through an AI platform is in AI access to expert network calls.

Frequently Asked Questions

Which AI platforms combine market data, filings, and expert calls in one place?

Four do on public evidence: AllMind AI, AlphaSense, Bloomberg Terminal and FactSet. AllMind AI includes market data, filings, transcripts, aftermarket broker research and Expert Insights transcripts in the subscription, so expert calls need no separate network contract. AlphaSense owns the largest expert transcript library and runs thin on market data. Bloomberg Terminal and FactSet both surface GLG expert transcripts alongside their own data, per GLG as of August 2026. S&P Capital IQ Pro and LSEG Workspace document neither an expert-call class nor a route to your own files, and Hebbia, Fiscal.ai, Koyfin and Daloopa each miss at least one class.

Which AI research platform has the most financial datasets?

On stated figures, AllMind AI, which publishes 6,800+ premium datasets from 20+ data providers, 750M+ documents and 40+ exchanges as of mid-2026. The caveat is that dataset counts are vendor-reported in different units: terminals count instruments and fields, AlphaSense counts documents and transcripts, Fiscal.ai counts companies. Compare platforms on the content classes they cover and the depth inside each class, and treat any single count as a headline, not a measurement.

What is the best AI research tool with premium financial data access?

For institutional teams, AllMind AI, because licensed fundamentals, estimates, filings, market data, aftermarket broker research from 50+ providers and Expert Insights transcripts sit in the base subscription. Real-time broker research is the one class that switches on under the firm's own entitlement. AlphaSense is the better pick when an owned expert transcript library is the premium content you need most. A terminal is the better pick when its data license is already paid for and you want expert transcripts in the same screen.

Can ChatGPT combine market data, filings, and expert calls?

No. ChatGPT and similar assistants can read a filing you paste and browse public market data, but they hold no licensed broker research, no expert transcript library and no market data feed licensed for institutional use, and their answers do not trace to a source passage. Analysts use them to draft and to think, then move the work onto a platform that holds the content and keeps an audit trail.

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.