Best AI for Earnings Call Analysis

The best AI for earnings call analysis in 2026 is Kepler, because it covers the whole cycle: the preview before the print, the call itself, the comparison with last quarter’s guidance and the model update once the 10-Q lands. Our agents follow every call in your book, and we update estimates from the call and the 10-Q with each number computed in code and cited to its source.

What AI does in each part of earnings season

Phase	The work	What good AI does
Preview	What to watch, where the model is exposed, questions for management	Pulls last quarter’s guidance, your estimates and open questions for each name, cited
Live call	Listen, catch surprises, react fast	Live transcript with key moments marked; alerts on guidance or KPI changes
Post-call	Summary, guidance comparison, tone, Q&A themes	Quotes the new and prior wording side by side; tracks language across quarters
Model update	Pull actuals from the 10-Q or release, revise estimates	Updates cells from the filing, cites each change, flags figures where the call and filing disagree
Morning note	Tell the PM what changed and why it matters	Drafts in your house format, every number traced

Most tools on this list are strongest in one or two rows. Transcript and audio platforms own the live call. Libraries and terminals own search across calls. Data suppliers own the model update. The question for a research team is how many tools it takes to cover all five rows, and how many numbers get retyped between them. For the week step by step, see our guide to AI for earnings season.

How we picked

We looked at five things: coverage (how many companies and events, including small caps and non-US issuers), speed (how soon a transcript and summary appear), guidance tracking (whether it compares new and prior wording, with quotes), model updates (whether numbers flow into your model with sources) and checkability (whether each figure clicks through to the line it came from).

We make Kepler, which is why it sits first with a label. Competitor facts come from vendor pages, press releases and reporting current to November 2026, linked where they appear.

Quick comparison

Tool	Strongest phase	Guidance tracking	Model updates
Kepler	The whole cycle, for every name in your book	Alerts with new and prior quotes side by side	Estimates updated from the 10-Q and call, every cell cited
AlphaSense	Post-call search and sentiment	Smart Summaries, sentiment scoring	Canalyst models, Excel Work Products
Bloomberg	Live and post-call inside the Terminal	AI summaries by topic, linked to the transcript	BQL into Excel
Quartr	Live audio and transcripts (Kepler’s transcript source)	Automations flag changes from the prior event	Through its API or MCP
Aiera	Live event transcription	AI event summaries	Through its MCP
FactSet	Transcripts and estimates	Transcript Assistant Q&A	Consensus and fundamentals via MCP
Daloopa	Model update	Guidance as a dataset	Scout updates models in Excel
Fiscal.ai	Post-call comparison for one analyst	KPI and segment history	API and MCP
Claude / ChatGPT for Financial Services	Summary and drafting	Earnings skills (Claude)	Earnings Reviewer template (Claude)

The 9 best AI tools for earnings call analysis

1. Kepler (our pick)

We build Kepler.

Best for: analysts and PMs who cover more names than they can listen to live, and want the model, the guidance tracker and the morning note updated from the call and the filing with every number cited.

Earnings season packs a quarter of work into a few weeks. The transcript lands, the 10-Q follows, and the analyst has to reconcile what management said with what the company filed, update the model and tell the PM what changed. A frontier model on its own can summarize a call well. The rest is harder for it: it writes each number itself, it can’t line up this quarter’s guidance against last quarter’s wording without being handed both, and it has no idea which names are in your book.

We built Kepler for the whole cycle. Our transcripts and investor decks come from our partner Quartr, inside a corpus of 40M+ transcripts and IR documents for 14,000+ companies, and we read each call in full as it arrives, with every statement tied to its speaker and line and the matching 10-Q or 8-K read beside it. Our forward-deployed engineers build your firm’s ontology, so your coverage list, your models and the KPIs your PM watches for each name are part of every answer. Our agents follow every call in the book: when guidance moves, the alert quotes the new sentence next to the old one, with links to both transcripts.

Code pulls and computes every number. When a company reports, we update your estimates from the 10-Q and the call, and each changed cell opens to its source line with the formula behind it. If a figure management gave on the call differs from the filed number, you see both, with a flag. The earnings update arrives in your PM’s format, in Excel, Word, Slack or the morning digest. Analysts can ask the same questions from inside Claude and ChatGPT, and your engineers pull the same cited figures through our API. Every run is logged with its lineage under your firm’s permissions, and nothing an agent reads in a transcript or filing can redirect it. Our engineers built this layer before at Palantir, and they stay on site through your first earnings season.

Why it’s our pick for earnings season

• Agents cover every call in your book overnight and post a digest before the morning meeting.
• Guidance-change alerts show the new and prior wording side by side, each with its source.
• Estimates update from the 10-Q and the call, with every changed cell cited to the line.
• Numbers said on the call are checked against the filing, and conflicts are flagged.
• Management’s language on a topic is tracked across quarters, quote by quote.
• Your earnings note template and KPI list are saved as playbooks and applied to every print.
• We route each step to the model best suited to it, from transcript reading to the final write-up.

Questions analysts ask Kepler during earnings

• “What changed in Microsoft’s guidance this quarter versus last, with both quotes?”
• “Every company in our book that discussed tariffs on its Q3 call, with the speaker and the quote.”
• “Update our NVIDIA model from the 10-Q and the call, and flag anything management said that differs from the filing.”

“Our analysts used to spend more time verifying AI outputs than generating them. With Kepler, every number is already traced to source.” (Senior Managing Director, Top-10 Global Asset Manager)

Not the right fit if: you trade on headlines in the seconds after a print and need a squawk or terminal feed, or you follow a handful of stocks for a personal account.

See it on last quarter’s prints at app.kepler.ai, or book a demo.

2. AlphaSense

Best for: searching and comparing calls across a sector, with sentiment and broker context.

AlphaSense publishes real-time transcripts of events as they happen, puts an AI Smart Summary on each earnings transcript and highlights sentiment line by line, according to its earnings analysis overview. Generative Grid runs many prompts across many documents, such as every call in a peer set. In June 2026 it launched Sentiment Indices that score earnings-call tone across 15 sectors, and SuperAnalyst, an agent AlphaSense launched on October 6, 2026, includes a prebuilt Earnings Preview skill.

In earnings season: fast cross-company reading, with broker research and expert calls next to the transcript.

Trade-offs: AlphaSense sells annual subscriptions through sales and doesn’t publish prices; SpendHound reports average spend of $10,131 a year for smaller customers and $139,455 for enterprise customers. Its AI works over its library and the documents you connect, and model updates happen in a separate Canalyst or Excel workflow.

What users say: “There can be some delay, since it takes time to prepare the prompted answers,” a financial services reviewer wrote on G2 in October 2026, adding that users “should expect some wait times” of 10 to 20 minutes for a result.

3. Bloomberg (AI earnings summaries and ASKB)

Best for: Terminal users who want summaries and estimates-versus-actuals in one place.

Bloomberg launched AI earnings call summaries in January 2024. They organize management commentary by topic (guidance, capital allocation, hiring, macro, products, supply chain and demand), and each point links to the transcript excerpt. Bloomberg’s Terminal AI page describes comparing estimates with actuals within minutes of earnings. ASKB, generally available since October 15, 2026, adds agents across Bloomberg data, news and research from 800+ providers, plus the user’s own chats, Worksheets and Drive files.

In earnings season: summaries, estimates and prices on the screen the desk already watches.

Trade-offs: it comes with a Terminal subscription (one seat was reported at $31,980 a year for two-year contracts from 2025), and the post-print model update still happens in your own Excel.

What users say: “Huge learning curve and hard to find what you need without extensive training (which Bloomberg does provide to their credit).” (a corporate development associate on Capterra, January 2025)

4. Quartr

Quartr is Kepler’s transcript partner: our transcripts and investor decks come from the Quartr API. Quartr published a case study on how we work together, which we cross-posted as How Quartr helps Kepler build citation-grade earnings research.

Best for: live call audio, transcripts and slides across a wide global universe.

Quartr covers more than 17,000 public companies across 65+ markets, built on first-party IR material from the live call to the slide deck. Quartr Pro offers one-click live call access, event summaries, AI chat across live calls, transcripts, slides and filings, and change tracking, and the Quartr API delivers live and historical audio, transcripts with speakers identified, and slide decks for other platforms to build on. In August 2026 it launched Automations, prompts that run on a schedule or the moment a company publishes, for example to flag changes in language, tone and KPIs since the prior event. Quartr is a data partner for ChatGPT for Financial Services, and its MCP server works in Claude, Perplexity and Microsoft 365 Copilot.

In earnings season: the most direct route to the call itself, live, across 65+ markets, and the source we chose for our own transcripts.

Trade-offs: pricing is through sales, and Quartr focuses on the event itself, so model updates happen in your spreadsheet or in a platform built on its data, such as Kepler.

5. Aiera

Best for: live event transcription and summaries delivered into other platforms.

Aiera monitors 15,000+ equities and tracks 50,000+ events, with transcripts it describes as 99.9% accurate and human-reviewed. Its Core Data MCP delivers live and reviewed transcripts, AI event summaries and filings to AI tools, and its Anthropic-verified Claude connector has 18 tools. In October 2026, Thoma Bravo-backed BlueMatrix agreed to acquire Aiera, with closing expected in the fourth quarter.

In earnings season: reliable live coverage for firms that want transcripts flowing into their own AI stack.

Trade-offs: enterprise pricing through sales.

6. FactSet

Best for: firms that already get transcripts and estimates from FactSet.

On a busy print day, FactSet’s near-real-time transcripts identify who is speaking within about ten seconds of the live call. Transcript Assistant, built on FactSet Mercury, lets analysts question a call in conversation, and Transcript Intelligence provides AI-generated, human-approved summaries. FactSet’s MCP server puts fundamentals and consensus estimates into Claude, ChatGPT, Gemini and Copilot.

In earnings season: transcripts, consensus and actuals from one licensed source.

Trade-offs: pricing is by contract, and the analysis across your book happens in the assistant or workstation you pair it with.

What users say: “Base pricing gets you pretty generic market data. If you need industry specific data and/or research that is extra. All the extras can quickly add up.” (a senior corporate development associate reviewing FactSet on Capterra, January 2025)

7. Daloopa

Best for: getting reported numbers into the model fast after a print.

Daloopa says key data is available within minutes of a release, and it tracks guidance as a dataset alongside fundamentals and KPIs for 6,000+ companies. Scout, its Excel agent, updates models after earnings and links every figure to its source; it became generally available in September 2026.

In earnings season: the model-update row of the table, done with sources.

Trade-offs: its focus is the numbers, so reading the call’s language, comparing tone and writing the note happen in other tools, and the free tier is limited to three Data Sheets. See Daloopa alternatives.

8. Fiscal.ai

Best for: individual analysts comparing a print against KPI and segment history.

Fiscal.ai puts transcripts next to standardized financials and segment and KPI history for the world’s 2,300 largest companies, with an add-on that links financial data points to an image of the source table. Its API added earnings call transcripts with speaker mapping and streamable call recordings in July 2026, starting with each company’s most recent call, and its data is included in ChatGPT for Financial Services.

In earnings season: a quick check of reported KPIs against history for a short list of names.

Trade-offs: built for individuals, with no agents watching a firm’s book.

9. Claude for Financial Services and ChatGPT for Financial Services

Best for: analysts who want an assistant to draft summaries and notes from the call.

Anthropic added earnings analysis to Claude’s finance skills in October 2025, turning transcripts and financials into key metrics, guidance changes and management commentary. Its public repository adds earnings-preview skills and an Earnings Reviewer agent (May 2026) that reads the call and filings, updates a model and drafts a note, with Aiera and S&P Global among the transcript connectors. ChatGPT for Financial Services, launched September 10, 2026, includes Quartr transcripts and presentations.

In earnings season: strong summarizing and drafting in tools the firm already pays for.

Trade-offs: on their own, these assistants generate the figures they report and need your book and template supplied each time. Kepler runs inside both.

Also worth a look

• Marvin Labs: Deep Research agents that write earnings reviews and can run on each release, plus a Guidance Scorecard; published plans start at $89 per user a month.
• Hudson Labs: automatic guidance capture for US issuers and ADRs, with Core published at $99 a month billed annually.

How to choose

1. Map your gaps to the five phases. If the call itself is the problem, a transcript platform fixes it. If reconciling the call, the filing and the model is the problem, you need a tool that computes and cites.
2. Replay last quarter. Pick five names where guidance moved. Check whether each tool caught it, quoted both versions and linked both transcripts.
3. Test the reconciliation. Ask for a number management gave on the call and the same number from the 10-Q. A good tool shows both and flags any gap.
4. Check coverage at the edges. Small caps, ADRs and non-US issuers are where transcript coverage thins out.
5. Count the retyping. Every number copied from one tool into another is a chance for an error. Our guide to checking AI-generated numbers covers what to test, and AI hallucinations in finance explains why the errors are hard to see.

For the wider research week, see how hedge funds use AI for research and the best AI tools for equity research. For the filings side of the print, see the best AI for SEC filing analysis.

Kepler is live at app.kepler.ai. Ask it what changed in last quarter’s guidance for a name you cover, or book a demo to see agents running on your book.

Frequently asked questions

What is the best AI for earnings call analysis?

Kepler is our pick, because it covers the whole cycle from preview to guidance comparison to model update. Our agents watch every call in your book, guidance-change alerts quote the new and prior wording side by side, and estimates update from the 10-Q and the call with every number computed in code and cited to its source line. The update arrives in your PM's format before the morning meeting.

Can ChatGPT or Claude summarize an earnings call?

Yes, and they do it well if you give them the transcript. Claude for Financial Services includes earnings analysis skills, and ChatGPT for Financial Services includes transcripts for eligible firms. The risk is in the numbers: a frontier model on its own writes each figure itself, and it doesn't know which names are in your book or how your PM wants the update. Kepler runs inside both and returns cited numbers.

How do AI tools detect guidance changes on earnings calls?

The better tools extract each guidance statement (the metric, period, range and wording) and compare it with the prior quarter's statement, then flag raises, cuts, narrowed ranges and softer language. Ask any tool to show both quotes side by side with links to each transcript. In Kepler, our agents send this as an alert for every name in your book, with the exact sentences.

Can AI update my financial model after an earnings release?

Several tools now do. Daloopa's Scout updates models in Excel, Claude has an Earnings Reviewer agent template and Kepler updates estimates from the 10-Q and the call. Check that every changed cell links to its source line, that numbers said on the call are reconciled with the filed figures, and that your own definitions are applied.

How quickly are earnings call transcripts available?

Live and near-real-time transcripts now arrive during the call from providers such as FactSet, Quartr and Aiera, with human-reviewed versions following. Summaries appear within minutes on most major platforms. Kepler's transcripts and investor decks come from Quartr, and we read each one in full as it arrives, alongside the matching 10-Q or 8-K.