Best AI Tools for Equity Research in 2026

The best AI tool for equity research in 2026 is Kepler, because it takes on the parts of the job that eat the week and proves every number it touches. We build statements from XBRL with every cell cited, as filed or as restated, read every call and 10-Q in your coverage before you ask, update estimates from the results, and draft the note in your house format.

This list is for buy-side and sell-side analysts who build models and publish research. Retail stock pickers and screeners are out of scope, and hedge fund PMs will find a wider list in our best AI tools for hedge funds.

How we picked

We scored each tool on the work an equity analyst does every quarter:

• Model building: does it produce historicals you would put in a model, with each cell traced to the filing?
• Restatements and reclassifications: does it distinguish a number as filed from the same number as restated?
• Earnings: can it update estimates and draft the post-print note from the 10-Q and the call?
• Your definitions: does it apply your adjusted EBITDA, your KPIs and your segment mapping every time?
• Where it works: Excel, Word, PowerPoint and the tools your team already uses.

Kepler is our product, so it comes first and carries a label. What we say about other tools comes from their own sites, press releases or public reporting as of November 2026, with links, and we give prices only where a vendor publishes them or a reputable outlet has reported them.

Quick comparison

Tool	Best for	Model-ready data	How it shows sources
Kepler	Models, estimates and notes with every number cited	Statements from XBRL, as filed or as restated; KPIs on your definitions	Exact page, line or cell; formula for each calculation
AlphaSense	Broker research and expert calls	4,000+ Canalyst models	Exact text snippet
Daloopa	Source-linked historicals	Fundamentals and KPIs for 6,000+ companies	Every data point links to its document
Hebbia	Questions across a whole coverage universe	Extracted per question	Sentence-level (per Hebbia)
Bloomberg ASKB	Terminal-based research teams	Bloomberg fundamentals via BQL	Linked sources
FactSet	Firms on FactSet for estimates and transcripts	Fundamentals and consensus estimates via MCP	Depends on the workflow
Hudson Labs	Guidance and red flags on US equities	Guidance and operating metrics	Links to filings and calls
Marvin Labs	Individual analysts and small teams	Earnings reviews, KPI trackers, guidance scorecard	Links each figure to the filing or call transcript
Fiscal.ai	Budget terminal for one analyst	Segments and KPIs for 2,300 companies	Source table image and PDF (add-on)
Claude / ChatGPT for Financial Services	Drafting in a general assistant	Licensed connectors or included datasets	Varies by connector

The 10 best AI tools for equity research

1. Kepler (our pick)

We build Kepler.

Best for: buy-side and sell-side analysts who build models and publish notes, and need every historical, estimate and quoted figure to trace back to the filing or call it came from.

The slow part of equity research is the history. Companies rename lines, merge segments and restate prior years, and a pipeline that follows XBRL tags or retrieved passages quietly mixes old and new definitions. In our paper Reclassified to Conform, 194 of 200 randomly drawn companies had reshaped a statement somewhere in their history in ways the tags can’t follow. A frontier model on its own doesn’t see those changes, and it can’t tell you which version of a number it used.

In Kepler, code assembles each statement from the filer’s own XBRL with no model in the path, and we rebuild each history so you can pull a line as originally filed, as restated, or as it stood on any date. Every cell clicks through to its page and line, and every derived figure (segment margin, share count bridge, organic growth) opens with its formula and cited inputs. Extraction models we train read every 10-K, 10-Q, 8-K, proxy and transcript in full at ingest, so the number buried in a footnote is indexed before anyone asks for it.

Your coverage universe lives in your firm’s ontology. Our forward-deployed engineers map each company, segment, subsidiary and KPI to one record, with your firm’s definitions of adjusted EBITDA, free cash flow and organic growth applied every time. Your initiation template, earnings note and comps sheet are saved as playbooks, and agents run them when a company reports. The work lands where you already write it: Excel, Word, PowerPoint, the Kepler app, Claude and ChatGPT. Our engineers built this layer before at Palantir, and they set yours up on site with your team.

Why it’s our pick for equity research

• Three statements from XBRL with every cell cited and restatements flagged, as filed or as restated.
• Segment and margin build-outs that survive reorganizations, because each line follows its own history.
• Estimate revisions with the source sentence or table attached to each change.
• Post-print notes and earnings previews in your house format, from your playbooks.
• Comps on each filer’s own adjusted EBITDA, with every multiple traced to a line item (see how to build trading comps with AI).
• 26M+ SEC filings and 40M+ transcripts and IR documents for 14,000+ companies, read in full before you ask.
• We route each step to the model best suited to it, every estimate change is logged with its lineage under your firm’s permissions, and we hold SOC 2 Type II.
• Excel, Word and PowerPoint, plus our API and MCP for analysts who script.

Questions equity analysts ask Kepler

• “Update FY2026 estimates from Alphabet’s latest 10-Q and earnings call, and show what changed.”
• “Microsoft’s operating margin by segment for 12 quarters, restated to the current segment structure.”
• “Draft the post-print note for NVIDIA in our template, with every number cited.”

“Kepler’s numbers match the filings. My team stopped re-checking every figure before it goes to a client.” (Vice President, Global Investment Bank)

Not the right fit if: you’re an individual investor screening stocks for a personal portfolio, or your main job is reading broker research and expert calls.

Start with a company you cover at app.kepler.ai, or book a demo.

2. AlphaSense

Best for: analysts who rely on broker research and expert calls alongside filings.

For equity research, AlphaSense’s draw is content: Wall Street Insights research from 1,700+ broker sources and more than 300,000 expert interviews, searchable next to filings and transcripts. For the model itself, AlphaSense offers Canalyst Models, acquired with Tegus: more than 4,000 pre-built drivable models, which Premium Models subscribers can work through a Modeling Tools add-in for Excel. Since July 2026, AlphaSense for Excel and for PowerPoint bring financials, transcripts and comps into a workbook or deck with source links, and SuperAnalyst, an agent launched on October 6, 2026, includes an Earnings Preview skill.

For an analyst: the fastest way to see what the street and industry experts think before you write.

Trade-offs: AlphaSense doesn’t publish prices; across the contracts SpendHound tracks, smaller customers average $10,131 a year and enterprise customers $139,455. The Canalyst models follow Canalyst’s structure, which analysts adapt to their own definitions and segment mapping by hand.

What users say: “I still find errors or misinterpretations in outputs.” (a G2 reviewer, October 2026)

3. Daloopa

Best for: analysts who want historicals delivered into Excel with every figure linked to its source.

Daloopa’s dataset spans 6,000+ listed companies and as much as 14 years of fundamentals, KPIs and guidance. For a covering analyst, the draw is Scout, an Excel agent that drafts a three-statement model, rolls it forward after each print and ties every figure to its source; Scout opened to general use in September 2026 and comes with the Core and Premium plans. By Daloopa’s account, key data is available within minutes of a release, and the same data reaches Claude, ChatGPT, Copilot, Gemini Enterprise and Perplexity through an MCP connector. Plans run from a free tier to Premium with API access, and none has a published price.

For an analyst: clean, auditable historicals with less manual spreading.

Trade-offs: it supplies data and model updates; notes, screens and your firm’s own documents sit elsewhere. See Daloopa alternatives.

4. Hebbia

Best for: asking the same questions across an entire coverage universe.

Hebbia’s Matrix runs a grid of questions over a set of documents, such as every 10-K in a sector, and returns answers with sentence-level citations (per Hebbia). Matrix 2.0 (August 2026) carries work through to models, memos and decks. Daloopa data arrived in Hebbia on September 1, 2026, and a Microsoft Word plug-in has since followed, so a note can draw on Hebbia where it’s written. Filing coverage spans the US, Canada, Europe, the UK and Australia and New Zealand, and Headless Hebbia exposes it all by API and MCP.

For an analyst: sector-wide comparisons of disclosures, such as how every peer describes pricing.

Trade-offs: a model extracts each answer from the documents when you run the Matrix, so figures aren’t computed from a fixed dataset, and pricing isn’t published.

5. Bloomberg Terminal with ASKB

Best for: research teams whose models and data already run on Bloomberg.

ASKB, generally available since October 15, 2026, sends coordinated agents through Bloomberg’s data, news and Bloomberg Intelligence and through outside research from 800+ providers, then hands back the BQL query behind an answer so analysts can run it in Excel or BQuant.

For an analyst: a quick path from a question to a refreshable Excel pull.

Trade-offs: the cost is a Terminal seat, reported at $31,980 a year under the two-year contracts that began in 2025, and your firm’s own models and notes reach ASKB through Bloomberg Drive.

What users say: “FA function lacks Non-GAAP adjustments for the non-GAAP numbers provided by companies.” (a TrustRadius reviewer at an investment firm, on the Terminal’s financial analysis function, November 2025)

6. FactSet

Best for: firms that already use FactSet for estimates, transcripts and fundamentals.

FactSet’s MCP server launched in December 2025 with nine datasets, including Fundamentals, Consensus Estimates, Ownership and Events, and its dataset list has since grown to take in fixed income, private company and macroeconomic data; a Claude connector with 23 tools followed in February 2026, and a Gemini Enterprise integration in August. Its near-real-time transcripts identify speakers within about ten seconds of the live call. On the workstation, Transcript Assistant, built on FactSet Mercury, answers questions about a call.

For an analyst: consensus estimates and transcripts inside whichever AI assistant the firm picks.

Trade-offs: FactSet supplies the data; the reasoning happens in the assistant you connect, and pricing is by contract.

What users say: “I do find the financial statement data such as cash flow, capex can sometimes be incorrect for international companies.” (a VP of product reviewing FactSet on Capterra, November 2025)

7. Hudson Labs

Best for: analysts who want a low-cost co-analyst for guidance and forensic flags on US stocks.

Hudson Labs covers all US issuers and ADRs, drawing on 250M+ filings, presentations and calls. For a covering analyst it logs each company’s guidance as it’s given, follows operating metrics and catalysts, and gives every name a forensic risk score. It offers a No Hallucination Guarantee of $50 per confirmed fabricated figure (calculations and omissions excluded), and Core is published at $99 a month billed annually.

For an analyst: a guidance log and red-flag screen for a US coverage list.

Trade-offs: US only, and it doesn’t build full models or work on your own research.

8. Marvin Labs

Best for: individual analysts who want AI summaries and Q&A over filings and calls.

Marvin Labs sells AI equity research for analysts: an AI analyst chat, Deep Research agents that write earnings reviews, company primers and KPI trackers, a Guidance Scorecard and sentiment analysis. Its published plans start with a free tier covering 15+ companies; Standard is $89 per user a month for 300+ companies, with agents that can run on each earnings release, and Pro is $399 per user a month with up to 50 custom companies and event transcripts.

For an analyst: a self-serve research assistant with transparent pricing.

Trade-offs: coverage on the Standard plan is a set list of 300+ companies, and there’s no deployment on a firm’s own models, notes or definitions.

9. Fiscal.ai

Best for: a single analyst who needs standardized financials and KPIs on a budget.

Fiscal.ai, the company formerly called FinChat, pairs 20+ years of standardized financials with segment and KPI data on the world’s 2,300 largest companies. With the paid Sourcing to Filing add-on, each financials or ratios figure opens an image and PDF of the table it came from, and its MCP server works in Claude, ChatGPT, Gemini Enterprise and Microsoft 365 Copilot. In October 2026, Terminal Pro was reported at $39 a month on yearly billing.

For an analyst: fast comps and KPI history with a source image a click away.

Trade-offs: built for individuals, without deployment on a firm’s own documents, templates or definitions. More options in Fiscal.ai alternatives.

10. Claude for Financial Services and ChatGPT for Financial Services

Best for: drafting and analysis in the general assistant your firm has standardized on.

Anthropic’s public financial-services repository includes equity research skills for earnings previews, earnings analysis, initiating coverage, model updates, morning notes and thesis tracking, and Claude for Excel, PowerPoint and Word reached general availability in May 2026. OpenAI’s ChatGPT for Financial Services, launched September 10, 2026, names equity research as an initial focus, includes filings, Quartr transcripts and Fiscal.ai data, and lets admins publish firm templates for Excel, Word and PowerPoint.

For an analyst: a capable writing and reading partner in the tools you already open.

Trade-offs: both are general frontier models, which write the figures in their answers themselves. Kepler runs inside both, so cited numbers can come into the same window.

How to choose

1. Start with a company you know cold. Pick one with a recent segment change or restatement, and ask each tool for five years of segment revenue and operating income.
2. Check the restated year. Ask for the year before the change as originally filed and as restated. Tools that can’t show both will mix definitions in your model. Our guide to tracking restatements and reclassifications shows where to look.
3. Click ten cells. Each should land on the exact line, with the right period and units. Our guide to checking AI-generated numbers has the full routine.
4. Run last quarter’s print. Ask for a post-print note in your format and an estimate update from the 10-Q and the call, then compare it with what you published.
5. Test your definitions. Ask for adjusted EBITDA your way on three companies.
6. Price the stack. Connector-based assistants need your FactSet, Capital IQ or other licenses on top.

For filings work specifically, see the best AI for SEC filing and 10-K analysis, and for earnings season, the best AI for earnings call analysis.

Kepler is live at app.kepler.ai. Open a company you cover and click any number, or book a demo to see your template and definitions built in.

Frequently asked questions

What is the best AI for equity research?

Kepler is our pick for the core work of building models, updating estimates and writing notes. Statements come from the filings' XBRL in code, as filed or as restated, every cell is cited to its source line, estimates update from the 10-Q and the call, and notes follow your house format. Our engineers build your coverage universe and your firm's definitions into its ontology.

Can AI build a financial model from SEC filings?

Yes, with a caveat. Several tools now build three-statement models from filings, including Kepler, Daloopa's Scout and Claude's model-building templates. The test is the history: companies reclassify lines and restate prior years, so a usable model has to show each figure as filed or as restated, cite the cell to its source line and keep the line definitions consistent across years. At Kepler, we assemble statements from XBRL with no model in the path.

Will AI replace equity research analysts?

AI is taking over the operational drag of the job: pulling historicals, reconciling restatements, reading every transcript and drafting the first version of a note. Judgment about a business, management access and the call on the stock stay with the analyst. Teams using AI well cover more names and spend more of the week on the thesis, with every number already traced to source.

How do I check whether an AI tool's numbers are correct?

Click ten citations and check each figure, period and unit against the filing. Ask the same question twice and compare. Request a year that was later restated, both as filed and as restated. Ask for a metric on your own definition, such as your adjusted EBITDA. A tool that passes all four is safe to put into a model.

Do buy-side and sell-side analysts need different AI tools?

The core work overlaps: models, earnings updates and notes. Sell-side analysts care more about publishing speed and house style for client notes; buy-side analysts care more about coverage of the whole book, thesis tracking and the fund's own definitions. We handle both in Kepler by building each firm's ontology and saving its templates as playbooks.