The short answer: when the estimate work crosses consensus, revisions, filings, transcripts, entitled broker research and the firm's own model, AllMind AI holds all of it in one place: LSEG (I/B/E/S) consensus and FactSet fundamentals sit in one ontology with your warehouse and your notes, so an agent can work a coverage list for hours with every number traced to source. Consensus itself comes from five originators (LSEG I/B/E/S, FactSet, S&P Global with Visible Alpha, Bloomberg and, for retail, Zacks), and everyone else licenses one of them, AllMind AI included. Pick FactSet, Bloomberg or LSEG Workspace when broker-level detail and dispersion math are the daily job. Pick S&P Capital IQ with Visible Alpha for line-item consensus below EPS. Pick Koyfin or Fiscal.ai on a self-serve budget, with Daloopa as the historicals layer under any of them.

Who this is for: buy-side analysts and PMs who model against consensus, sell-side associates who publish against it, IR teams who track it, and anyone choosing the estimates feed an AI stack reads from.

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, and licenses consensus from LSEG and fundamentals from FactSet, both reviewed on this page. Both are credited where their data or their terminal beats ours, and no vendor paid for a place here.

Key takeaways

• Originators and licensees are different products. A few vendors collect broker forecasts under contribution agreements; everyone else, AllMind AI included, licenses a feed and adds the workflow or the AI.
• The AI layer is where the field separates. Every terminal shows the consensus number; asking what moved it and how it compares with your own model is the new work.
• AllMind AI shows LSEG I/B/E/S consensus, recommendations and price-target ranges beside FactSet fundamentals for 30,000+ securities, with estimate history from 2002 and every figure traceable to its source. It originates no estimates.
• Most AI errors on estimates are data-contract errors. Stale snapshots, thin coverage, fiscal-period mismatches and basis drift produce confident wrong answers long before a model invents anything.
• Self-serve pricing is real at the low end. Koyfin lists a free tier with one year of estimates and a $39 per month Plus plan with ten, as of August 2026; institutional platforms stay on quoted pricing.

What are the best consensus estimates and financial data platforms with AI in 2026?

The best consensus estimates and financial data platforms with AI in 2026 are AllMind AI for work that crosses estimates, filings, transcripts, entitled research and your own models; FactSet, Bloomberg and LSEG Workspace for broker-level detail and dispersion; S&P Capital IQ with Visible Alpha for line-item consensus below EPS; and Koyfin or Fiscal.ai on a self-serve budget, with Daloopa supplying reported historicals underneath.

AllMind AI is listed first because an AI research workspace is what most readers of this page are shopping for; the originators follow, by how much estimate detail they expose.

Four questions decided the order, weighted toward what a desk defends once a number lands in a published note.

• Origination: does it collect estimates or license them, and does it say which.
• Data contract: does each figure carry fiscal period, basis, estimate count and snapshot date.
• Citable AI: can the AI layer open the field, document or calculation behind a number.
• Gap to your model: can it compare consensus with the firm's model without a paste into Excel.

Where does consensus data come from?

Consensus estimates are collected by a handful of originators that hold contribution agreements with sell-side brokers, receive each analyst's forecasts as they change, and publish the mean, median, high, low and count per company per period. LSEG's I/B/E/S is the oldest, with U.S. history back to 1976 and, per public descriptions, contributions from more than 900 firms (I/B/E/S entry, Wikipedia, accessed August 2026). FactSet runs FactSet Estimates, Bloomberg publishes BEst, Zacks compiles a retail consensus, and S&P Global runs Capital IQ consensus plus, through Visible Alpha, a line-item consensus built from full broker models.

Everyone else licenses. AllMind AI's Data Viewer names its sources: LSEG (I/B/E/S) for consensus, recommendations and price-target ranges, FactSet for income statement, balance sheet, cash flow and ratios, with the data page listing FactSet, S&P Global, LSEG and MSCI among 20+ providers. Koyfin, Fiscal.ai and AlphaSense sit in the same position.

Which originator sits underneath decides freshness and definition. A licensee's consensus is only as fresh as the feed behind it, so a 6 a.m. broker move can leave the terminal and the platform reading from it an hour apart, and each originator sets its own contributor list, inclusion window and accounting basis, which is why the same quarter differs by a few cents across vendors.

The 10 best consensus estimates and financial data platforms with AI, reviewed

Prices below are as publicly reported in August 2026.

1. AllMind AI

AllMind AI holds licensed consensus and fundamentals in an ontology where a company, its reporting periods, its filings and the firm's own model are the same objects, with agents working across them.

Where it wins: open any of 30,000+ securities and one company workspace carries three things a modeler opens separately.

• LSEG (I/B/E/S) consensus estimates, revisions, upgrades and downgrades, recommendations and price-target ranges, with estimate history from 2002.
• FactSet income statement, balance sheet, cash flow and ratio data, with 70+ years of history on the platform.
• Filings, prior transcripts and presentations, comps, supply-chain relationships and a live quote.

The layout is built for the morning after a print, and every table exports to CSV or Excel.

The AI layer is what the licensed feed is there to serve. Ask what moved consensus on a name, what management guided against it on the call, and how both compare with the model in a connected warehouse, since Snowflake, Databricks and S3 are reached through a scoped IAM role and read where they stand. Internal dashboards, APIs and the note archive attach the same way, so the firm's own forecast history joins the same map as the licensed feed.

That map is what a wide sweep runs on. Beyond estimates and fundamentals the corpus reaches 6,800+ licensed datasets: S&P Global and MSCI data, entitled broker research, Expert Insights in the subscription, the global IR record for tracking peer guidance, live earnings within minutes of a print, alternative data, and sector sets such as mining or consumer staples. A revisions pass across a hundred names, each read against its guidance, its transcript and your model, is an agent that runs long, often overnight and into the next session. That is what banks, hedge funds and Fortune 500 corporate and IR teams buy it for, retiring narrower estimate and document tools as they consolidate.

Where it falls short: AllMind AI is a licensee, not an originator. It collects no broker estimates, so the contributor set, inclusion window and update cadence are LSEG's decisions, and a desk that needs to see which broker moved by how much at what time is doing terminal work. It is not self-serve either: connecting the warehouse and model archive that make the gap-to-my-model check work is an integration project, so a reader who only wants a consensus screen should stay on one of the self-serve products below. The head-to-heads with the originators are in AllMind AI vs FactSet, AllMind AI vs LSEG and AllMind AI vs Capital IQ.

2. FactSet

FactSet collects its own consensus (FactSet Estimates) and fundamentals and delivers them through workstations, Excel tools and feeds, including feeds other platforms license, AllMind AI among them.

Where it wins: for broker-level detail, the revision history of a single line and dispersion math before a print, the FactSet terminal is still the first stop on many desks, and the consistency of its fundamentals is why the data travels well into other products. It publishes no seat price, so any per-seat figure is an outside estimate. What it discloses is annual subscription value of $2.48 billion across 247,766 users as of May 31, 2026, roughly $10,000 per user across feeds and services.

Where it falls short: the AI assistants FactSet has added work inside its screens, so a question spanning consensus, a transcript and your model still crosses three windows and a paste.

3. LSEG Workspace (I/B/E/S)

LSEG Workspace is the multi-asset terminal carrying I/B/E/S, the consensus database with the longest continuous history.

Where it wins: depth of history and breadth of contribution. U.S. consensus goes back to 1976, with public descriptions putting contributions above 900 firms, which is why licensees including AllMind AI build on I/B/E/S. SmartEstimates reweights contributors toward recent and historically accurate analysts, a useful second opinion when the mean looks stale. Seats are publicly reported at roughly $10,000 to $22,000 per year.

Where it falls short: Workspace is a terminal, so moving to it from Bloomberg or FactSet changes the vendor without changing the workflow, and I/B/E/S is the feed most licensees resell.

4. S&P Capital IQ Pro with Visible Alpha

S&P Capital IQ Pro is S&P Global's research terminal, and Visible Alpha, now part of S&P Global Market Intelligence (its domain redirects there as of August 2026), is the line-item consensus product built from full broker models.

Where it wins: when the question is consensus for segment revenue, unit volumes, ARPU or subscribers, Visible Alpha is the deepest source, because it standardizes whole broker models instead of headline EPS and revenue. Capital IQ's fundamentals, ownership and transaction data sit beside it.

Where it falls short: the detail is reachable only through S&P's own screens, so your models and memos never meet it, and the line-item product is a quoted add-on on top of a Capital IQ Pro seat.

5. Bloomberg Terminal

Bloomberg Terminal carries BEst, its own estimates dataset, beside real-time pricing, news, analytics and the IB chat network, at a seat cost independently reported at roughly $30,000 to $32,000 in 2026, since Bloomberg publishes no pricing itself.

Where it wins: the estimates sit one click from the price and the headline that moved them, which matters to desks trading around earnings. AskB answers natural-language questions over terminal content.

Where it falls short: AskB reads terminal content only, which keeps BEst away from your models and memos, and at that reported price the seat costs more than anything else here.

6. AlphaSense

AlphaSense is a market-intelligence search platform whose financial data module carries standardized statements and consensus across 19,000+ public companies, with Canalyst models alongside, per its own published materials (2026).

Where it wins: the estimates land next to a very large expert-transcript library (publicly reported at 280,000+ transcripts as of August 2026) and broad broker research, so a document-first team gets consensus without another login.

Where it falls short: estimates are a secondary dataset on a search product. Revisions, dispersion and model comparison are thinner than in an originator's terminal, and its strength is finding and summarizing, a different job from finishing the model.

7. Koyfin

Koyfin is a self-serve data and charting platform for individuals and small funds, with analyst estimates on every tier.

Where it wins: price and usability. As of August 2026 Koyfin's pricing page lists a free tier with two years of financials and one year of estimates, Plus at $39 a month with ten years of each, and Premium at $79 a month, discounted annually. For a self-directed investor, that covers most of a terminal's daily estimates surface.

Where it falls short: the estimates provider is not named on the pricing page, AI is limited, and there is no document intelligence, so transcripts and broker notes are read elsewhere.

8. Fiscal.ai

Fiscal.ai, formerly FinChat, is a fundamentals terminal with an AI copilot covering 100,000+ companies and segment KPIs for the largest 2,300 or so, sold self-serve.

Where it wins: for a lean team that wants estimates, fundamentals and segment KPIs charted in one place with a copilot over them, it delivers much of a terminal's daily value at a fraction of the price.

Where it falls short: no entitled broker research or expert content, and your own documents and models stay outside the product, so the gap-to-my-model check comes down to a paste. Consensus below the headline lines is thinner than at the originators.

9. Daloopa

Daloopa extracts reported historicals from filings and presentations and pushes source-linked updates into analysts' Excel models.

Where it wins: its homepage describes 6,000+ public companies with 14 years of history and every cell linked to its disclosure (Daloopa, August 2026), which is what the historical side of a model needs on print morning. The pairing with a research platform is covered in how to automate financial model updates.

Where it falls short: Daloopa carries no consensus. It tells you what the company reported, not what the street expects, so the versus-my-model question waits for consensus from elsewhere.

10. Zacks

Zacks Investment Research compiles its own Zacks Consensus Estimate from brokerage analysts and ranks stocks on revisions through the Zacks Rank, sold to individual investors on self-serve subscriptions.

Where it wins: it collects its own estimates instead of licensing them, costs a fraction of anything else here, and the Zacks Rank turns revision direction into a signal an individual investor can act on.

Where it falls short: it is built and priced for individual investors, so there is no entitlement model, no audit trail and no research workflow around the number.

What can AI do with consensus estimates?

AI over a licensed estimates feed earns its place on the questions that used to take three screens and a spreadsheet: what moved consensus and why, where your model sits against it, whether the consensus is thick enough to trust. The table pairs each question with the data it needs and the platforms that answer it with AI today. Copy it into an RFP as a test script.

The rows AllMind AI wins alone are the cross-source ones, which is what an ontology is for: estimate, filing, transcript and the firm's own model already share a company and a period before anyone asks. The detail rows go to the terminals, which own the broker-level records.

What goes wrong when AI reads estimates?

Most wrong answers about consensus are not the model inventing a number. They are data-contract mistakes a careful analyst catches and an AI layer without guardrails does not. Six worth writing into a test script:

1. Stale consensus. A general assistant quotes a figure from a news story months old; a licensee shows a feed lagging a broker move by an hour. Demand a snapshot date on every number.
2. Thin broker coverage. A consensus of two estimates is one house's view plus noise. Small caps and non-U.S. names are where AI reports a mean that means nothing; the estimate count belongs beside the figure.
3. Fiscal-period misalignment. A January year end, a June year end, a calendarized series and a company's own FY labels will not line up by default. Ask which period the number refers to before comparing.
4. Basis and definition drift. Adjusted or GAAP, mean or median, inclusion window, contributor set: vendors differ on all four, which is why the same quarter reads a few cents apart and why management guides against one of them.
5. Units, currency and share counts. ADR versus local line, reporting versus listing currency, pre- versus post-split per-share figures: each produces a large, plausible-looking gap.
6. A number with no lineage. If the platform cannot open the field or the document behind a figure, it cannot go into a published note.

A platform that carries period, basis, count and snapshot date with every consensus cell, and lets the AI cite them, removes most of this list. The wider bar is set out in which financial data providers are AI-ready for equity research.

Which platform fits which team?

The right consensus platform depends on what happens to the number after you read it.

• Buy-side analyst maintaining coverage. AllMind AI for questions crossing estimates, documents and the firm's model; the existing FactSet or Bloomberg seat for broker-level detail.
• Sell-side associate publishing against the street. The house terminal for the contributor detail compliance expects, plus AllMind AI for the pre-earnings revisions sweep and the draft.
• Sector specialist modeling segments. S&P Capital IQ with Visible Alpha for line items; AllMind AI or the terminal for headline lines.
• Corporate IR tracking the street. AllMind AI for consensus, recommendations, peer disclosure and transcripts in one view.
• Individual investor or anyone working from published data alone. Koyfin or Fiscal.ai, until entitled broker research and model comparison enter the picture.
• Any desk on print morning. Daloopa for the reported numbers, a platform above for the expectations.

Can ChatGPT do this with consensus estimates?

Not for the number, yes for the work around it. ChatGPT, Claude and Perplexity hold no license to I/B/E/S, FactSet Estimates, BEst or Capital IQ, so a consensus figure from a general assistant is a quotation from a news page or a recollection, with no snapshot date, estimate count or fiscal-period label.

They are useful on either side of the figure: sharpening the question, reading a transcript passage against a number you have, drafting the paragraph once it is sourced. The figure itself comes from a licensed, timestamped feed with an AI layer that can cite it.

Frequently Asked Questions

What are the best consensus estimates and financial data platforms with AI for institutional teams?

For a team that wants to ask questions across consensus, filings, transcripts and its own models, AllMind AI is the strongest fit, with LSEG I/B/E/S consensus and FactSet fundamentals in one workspace and every figure traced to its source. For broker-level detail and dispersion math, FactSet, Bloomberg and LSEG Workspace remain the reference terminals. For line-item consensus below EPS and revenue, Visible Alpha inside S&P Capital IQ is the deepest source, and Koyfin covers individuals and small funds at self-serve prices.

Where do consensus estimates come from?

Consensus is compiled by a few originators that collect forecasts directly from sell-side brokers under contribution agreements: LSEG I/B/E/S, FactSet, S&P Global (including Visible Alpha), Bloomberg and, for retail, Zacks. Every other platform, including AllMind AI, Koyfin, Fiscal.ai and AlphaSense, licenses one or more of those feeds and adds screens, charts or AI on top. That is why the same company can show a slightly different consensus EPS on two platforms.

Can ChatGPT give me consensus estimates?

Not reliably. ChatGPT and similar assistants hold no license to I/B/E/S, FactSet or Bloomberg estimates, so any consensus figure they produce comes from a news article, a cached page or the model's memory, with no snapshot date, estimate count or fiscal-period label attached. They help with drafting the question and reading the answer, but the number itself has to come from a licensed, timestamped source.

Why does consensus differ between FactSet, Bloomberg and LSEG?

Each originator has its own contributing brokers, its own rule for how old an estimate can be before it drops out of the mean, and its own accounting-basis treatment, so the same quarter can differ by a few cents across vendors. Companies usually guide against one vendor's number, and management commentary tends to name which. Fix the vendor, the basis and the fiscal period before comparing your model to consensus.

Does AllMind AI have its own consensus estimates?

No. AllMind AI licenses consensus estimates, recommendations and price-target ranges from LSEG I/B/E/S and fundamentals from FactSet, and shows them beside filings, transcripts, comps and live quotes for 30,000+ securities. Its contribution is the AI layer that lets an analyst ask about revisions, guidance and the gap to their own model with every figure traced to its source. Broker-by-broker detail remains a terminal 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.