The short answer: AI-ready financial data is data an agent can use without a person cleaning it first: entity-resolved, point-in-time, machine-addressable, traceable to its source, and licensed for model use. If the goal is research work that crosses several licensed catalogs and the firm's own systems in one pass, AllMind AI is the platform built for it: FactSet, S&P Global, LSEG and MSCI content arrives connected as entities beside broker research, Expert Insights, global investor-relations data and live earnings, with the firm's warehouse joined to it.

If what you need is the feed itself, buy it from the vendor: S&P Global and FactSet for the deepest point-in-time fundamentals and estimates, LSEG for I/B/E/S and open identifiers, Daloopa for source-linked model data with an agent endpoint, Fiscal.ai and Polygon.io at self-serve prices, and SEC EDGAR and SEDAR+ for free primary filings.

Who this is for: heads of research and heads of data at asset managers and hedge funds, quant and data-engineering leads wiring agents to feeds, and anyone renegotiating a data contract with AI use on the table.

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

Disclosure: AllMind AI builds one of the platforms discussed here and licenses data from several of the vendors it is compared with. Where a vendor fits better, we say so, and placement here is not for sale.

Key takeaways

• AI-ready describes the data model and the contract, not the vendor's AI features. Resolved entities, revision timestamps and a license naming model use make a feed AI-ready with no copilot attached.
• The large vendors are converging on an agent-facing door onto the same catalog. Kensho connects LLMs and agents to S&P Global data, and LSEG markets AI-ready data and cloud infrastructure with Microsoft (vendor sites, August 2026).
• Free filings pass four of the five tests and fail the fifth. EDGAR and SEDAR+ are dated, entity-keyed and the source itself, but nothing is normalized, so the cleaning cost moves to your team.
• Lineage is the criterion most catalogs miss. Daloopa links every figure to its disclosure and AllMind AI opens the source passage behind every number; most feeds reference a filing, not a line in it.
• The join is where the value sits. Clean tables from five vendors still leave an analyst reconciling identifiers and dates.

What makes financial data AI-ready?

Five properties separate AI-ready data from a clean-looking spreadsheet. A dataset can be expensive, accurate and widely used while failing three of them.

• Entity-resolved. Each record ties to a company, security or person through a stable identifier, not a ticker that changes on a relisting or means four companies across venues.
• Point-in-time. Every value carries the date it became known, so an as-of question about 2023 returns the 2023 number and not the restated one.
• Machine-addressable. An agent can fetch one field for one company for one date through an API, a warehouse share or a retrieval endpoint, with nobody clicking a screen.
• Lineage preserved. Each figure points back to the filing page, transcript passage or feed record it came from, so a reviewer can open the line and not only the vendor name.
• Licensed for model use. The contract permits retrieval at query time, combination into derived values and, if needed, training. Silence is not permission.

No vendor clears all five on every dataset, and the free sources clear more than most buyers expect.

Which platforms offer AI-ready financial data for research?

Eleven sources cover most institutional equity research needs in 2026, in three groups: the large licensed catalogs (FactSet, S&P Global Market Intelligence, LSEG, Bloomberg Data License, MSCI, Morningstar), the AI-native and developer-priced layers (Daloopa, Fiscal.ai, Polygon.io), and the free primary sources (SEC EDGAR and SEDAR+). AllMind AI is the one entry that is not a data vendor: it licenses from four of the others and joins them.

Narrower cuts of this question: platforms bundling market data, filings and expert calls, consensus-estimates platforms for AI and index and ETF holdings data.

Best data providers for AI-driven equity research: how they compare

The best provider depends on which criterion your workflow is short on: missing lineage points to Daloopa and AllMind AI, missing point-in-time depth to S&P Global and FactSet, a tight budget to EDGAR, Fiscal.ai and Polygon.io. AllMind AI is first below because it assembles several of the others.

AllMind AI

AllMind AI sits a layer above most of the providers below it. It licenses data from partners including FactSet, S&P Global, LSEG and MSCI, indexes SEC and SEDAR filings, and joins both to the firm's own research through an ontology the agents query.

Where it wins: the four data-side criteria are met by the architecture, not by a contract you negotiate.

• The catalog is a set of classes, not a headline count: licensed fundamentals and estimates from S&P Global, FactSet and LSEG, MSCI index and ratings content, broker research, Expert Insights, global investor-relations data, live earnings within minutes of release, alternative data, and sector datasets covering mining, healthcare and consumer staples. Expert Insights transcripts are part of the subscription rather than a separate expert-network contract; aftermarket broker research is included on a delay, and only live notes need the firm's own entitlement.
• Companies, suppliers, customers, estimates, filings and internal notes resolve to one set of entities in the financial ontology, so a question about one name reaches its supply chain.
• Every number opens its source document at the passage, and a derived figure shows the arithmetic behind it.
• Fundamentals run from 1965 and I/B/E/S estimates from 2002, daily, per the data partners page.
• The firm's own sources join the same graph: internal systems, APIs, dashboards, document stores, and Snowflake, Databricks or S3 queried where they sit under a scoped IAM role, nothing copied out. Agents inherit the entitlements of whoever runs them, and the audit log keeps every question and export.

The workload behind that design is the long one: an agent working a question across several catalogs and the firm's own tables for minutes or hours, sometimes across days. Institutional buyers use it for that: banks and hedge funds alongside Fortune 500 and Fortune 100 corporates, often folding narrower subscriptions into it.

Where it falls short: AllMind AI is not a data vendor. Partner data is used on the partners' terms inside the platform, so there is no raw feed or bulk file to take into a warehouse, and a desk that needs the feed still signs with FactSet, S&P Global or LSEG. Execution-grade ticks stay with a terminal.

FactSet

FactSet is a terminal and enterprise data business with one of the deepest normalized catalogs of fundamentals, estimates, ownership and filings, and an AllMind AI data partner.

Where it wins: content is consistent across decades and geographies, the symbology is stable, and enterprise delivery through APIs and feeds is mature.

Where it falls short: the AI assistants sit inside workstation screens, and feed rights for model use are contracted separately from the seat. FactSet publishes no per-seat price, so any seat figure is a third-party estimate; what it discloses is $2.48 billion of subscription value across 247,766 users as of May 31, 2026, spanning feeds and services as well as seats. Teams re-extracting workstation data for an agent end up at the FactSet alternatives guide.

S&P Global Market Intelligence

S&P Global Market Intelligence supplies Capital IQ fundamentals, estimates and transcripts along with Compustat, and its Kensho unit builds the retrieval layer between that catalog and models.

Where it wins: point-in-time history is the standout, and Compustat and Capital IQ are what quant teams reach for when restatement-aware backtests matter. Kensho's public positioning as of August 2026 is that it connects LLMs, agents and AI applications to S&P Global data, the clearest statement of intent among the large vendors.

Where it falls short: the catalog is wide enough that scoping the license is the work, the dataset an agent needs often sits in a different product from the one licensed, and the AI route is priced on top of that entitlement.

LSEG

LSEG supplies I/B/E/S estimates, pricing, reference data and the open PermID identifier, through Workspace and through feeds and cloud channels built with Microsoft.

Where it wins: I/B/E/S is the estimates benchmark most research processes were built on, PermID is free and solves a real entity-resolution problem for anyone stitching vendors together, and LSEG puts AI-ready data and cloud delivery at the center of its strategy (lseg.com, August 2026).

Where it falls short: desktop and feed products are contracted separately, Workspace is publicly reported at $10,000 to $22,000 per seat, and the AI-ready label covers more ground than any single product a desk can point an agent at.

Bloomberg Data License

Bloomberg Data License is the enterprise product beside the Terminal, delivering reference, pricing and fundamentals data under a contract scoped to the fields, securities and uses a firm names.

Where it wins: the security master is wide, FIGI is open, and a firm already on Bloomberg can extend into AI use without a new vendor.

Where it falls short: Terminal rights do not carry into a model. The Terminal is publicly reported at roughly $30,000 to $32,000 per seat for 2026, Data License is priced by quote and scope, so an agent project starts with a usage-rights conversation.

Daloopa

Daloopa extracts fundamentals and KPIs from filings and presentations with AI, links every figure to its disclosure, and delivers through Excel, an API and an MCP endpoint for agents.

Where it wins: lineage is the product. Daloopa states coverage of 6,000+ public companies with 14 years of history and average accuracy above 99 percent (daloopa.com, August 2026), and the MCP endpoint lets an agent fetch a source-linked figure directly. See AllMind AI vs Daloopa.

Where it falls short: the scope is deliberately narrow: no market data, no document corpus, no workspace, and 14 years is short for a factor backtest.

Fiscal.ai

Fiscal.ai (formerly FinChat) is a self-serve fundamentals terminal and API with an AI copilot, publicly reported to cover 100,000+ public companies, with segment KPIs reaching about 2,300 of them as of August 2026.

Where it wins: segment KPIs are hard to buy elsewhere at any price, and the self-serve API gets a lean fund much of a terminal's daily data without an enterprise contract.

Where it falls short: entitled broker and expert content is absent, there is no route for a firm's own documents, and KPI depth tracks disclosure.

Polygon.io

Polygon.io, now branded Massive, is a developer-priced market data provider for US equities, options, indices, futures, FX and crypto, by API, flat files and SQL.

Where it wins: the pricing is published: stock tiers run from a free plan with two years of history and five API calls a minute to $199 a month with 20+ years and unlimited calls (massive.com, August 2026), and flat files suit an agent that needs the whole tape.

Where it falls short: coverage is prices and ticks with no fundamentals, and self-serve tiers are marked individual and non-professional, so a desk-wide agent needs an institutional agreement.

SEC EDGAR and SEDAR+

SEC EDGAR and SEDAR+ are the free primary filing systems for the US and Canada, and every vendor on this page normalizes from them.

Where they win: free, dated at filing, keyed to a company identifier, and the source itself, which is the strongest lineage there is. EDGAR also exposes XBRL through public APIs, so an agent can pull reported values from the filer's tags.

Where they fall short: nothing is normalized. Tag choices differ between filers, restatements and non-GAAP reconciliations land on your team, EDGAR access is rate-capped under its fair-access policy, and SEDAR+ filings arrive as documents with no structured API. Our guide to AI for SEC filing analysis covers the cleanup.

Raw feeds vs connected data: what does an ontology change?

A feed gives you a table; an ontology gives you the joins. That is why a firm can license four excellent feeds and still have analysts reconciling identifiers and dates.

Take a question asked every quarter: which holdings have meaningful revenue exposure to a supplier that just cut guidance? From feeds that means the supplier's customers (one vendor), their segment revenue (another), consensus for each (a third), the filing passages (EDGAR) and the firm's own position notes (a shared drive). Every join is a place where a number can lose its date or its source.

An ontology does those joins once. In AllMind AI the supplier, customer, estimate, filing and internal note are objects in one graph, so an agent walks from the guidance cut to the exposed holdings and returns a cited answer, and the search, grid and agent surfaces read those same objects. That is also what lets one run stay coherent for hours instead of collapsing into a series of lookups. The feeds underneath stay the partners': a team that needs the raw I/B/E/S tape for a backtest buys it from LSEG, and a team that needs the connected view buys a platform.

How should a research team buy data for AI?

Buy against the five criteria (entity resolution, point-in-time, machine-addressable, lineage, license), in writing, before any pilot. Copy the scorecard below into your evaluation and fill the third column from the vendor's documentation and contract, not the sales deck.

Two habits sit on top of it: run the pilot on the data you will actually license, and decide first whether you are buying feeds, a platform or both, because the contract, the security review and the owning team differ for each.

What are the licensing traps with AI and financial data?

Four traps recur at nearly every vendor, and they surface after the pilot works, the expensive moment to find them.

• Display versus use. A seat licenses a person to see data on a screen. Loading it into a retrieval index, a model or an agent tool is a different right, and most seat contracts are silent on it or exclude it.
• Derived data. A ratio computed from two licensed fields is, in many contracts, still the vendor's data. Agents produce derived values constantly, so ask how those outputs can be stored, shared and shown.
• Training versus retrieval. Retrieval at query time, fine-tuning and pretraining are licensed differently. Most research use is retrieval, so say so in the contract and skip training rights you do not need.
• Individual-use plans at scale. Self-serve providers price for one person, so pointing a desk-wide agent at an individual plan breaches the terms.

Platforms built on partner data absorb some of this: in AllMind AI an agent inherits the entitlements of the person running it, and the partner contracts govern what it can retrieve and show. That does not replace your own feed contracts.

Frequently Asked Questions

What is AI-ready financial data?

AI-ready financial data is data a model or agent can use without a person cleaning it first: tied to a resolved entity instead of a ticker string, stamped with the date it became known, reachable by machine through an API or warehouse share, traceable to the filing or feed behind it, and licensed for use in models. Most datasets clear two or three of those five tests, so the decision is about which gaps your workflow can absorb.

What are the best data providers for AI-driven equity research?

For licensed fundamentals and estimates with deep point-in-time history, S&P Global Market Intelligence and FactSet lead, with LSEG strongest on I/B/E/S estimates and open identifiers. Daloopa is the pick for source-linked model data with an agent endpoint, Fiscal.ai and Polygon.io serve lean teams at self-serve prices, and SEC EDGAR and SEDAR+ are the free primary sources. AllMind AI is the answer when the requirement is the research work and not the feed: content from several of these vendors arrives connected as entities beside broker research, expert content, global IR data and live earnings, with the firm's own warehouse joined to it.

Is free SEC EDGAR data AI-ready?

Partly. EDGAR filings are free, dated, keyed to a company identifier and are the source every vendor normalizes from, so they pass four of the five tests. Nothing is normalized, so tagging inconsistencies, restatements and non-GAAP reconciliations land on your team, and SEDAR+ filings mostly arrive as documents with no structured data API.

Is AllMind AI a financial data provider?

No. AllMind AI is a research platform that licenses data from partners including FactSet, S&P Global, LSEG and MSCI, indexes SEC and SEDAR filings, and connects all of it with a firm's own research through a financial ontology that agents work from. It does not sell raw feeds or bulk files, so a desk that needs a feed for a warehouse or a backtest still contracts with the vendor.

Can I use Bloomberg or FactSet data in my own LLM?

Only under a license that says so. Terminal and workstation subscriptions cover display to a named user and usually do not extend to loading data into a model, an index or an agent tool, while enterprise feed and data-license agreements can, with the scope written into the contract. Before any pilot, ask for the clause covering derived data, retrieval at query time and model training, because those three are licensed differently.

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.