# earnings.chat

> An AI model trained to interpret earnings calls, with 252,000+ earnings call transcripts from 12,000+ companies in its knowledge base. Users ask in plain language; every answer carries verbatim quotes, queried figures and named sources. New earnings calls are added within minutes of publication; the knowledge base reaches back to 2020.

earnings.chat is a chat interface, not an API. It answers questions about earnings calls, produces summaries, quarterly comparisons, guidance tracking, sentiment reads and sector analysis, and draws charts and tables from queried figures. Figures never come from the model: every number is the result of a database query or a verbatim quote from a transcript, and gaps are reported as gaps instead of being filled with guesses.

## Coverage

- 252,000+ earnings call transcripts, speaker-tagged
- 12,000+ companies across roughly 70 countries
- All 11 GICS sectors and 74 industries
- Transcripts back to 2020; new calls ready to discuss within minutes
- Languages: English, German, Spanish (quotes stay verbatim in their original language)

## Plans

Two monthly plans (Basic 24.99, Pro 39.99), cancel anytime. Both use the same model and data; only the included token volume differs. Payments are processed by Paddle.com as merchant of record. Accounts are created by subscribing to a plan at https://earnings.chat/#pricing.

## Pages

- [Home](https://earnings.chat/): product, pricing, FAQ
- [Blog](https://earnings.chat/blog): guides and earnings call knowledge

- [10 questions to ask an earnings call](https://earnings.chat/blog/questions-to-ask-earnings-calls): The ten most useful questions to ask about any earnings call, from a quick summary to sector-wide comparisons, and how earnings.chat answers each one.
- [Charts and tables from earnings calls, in one question](https://earnings.chat/blog/earnings-call-charts-tables): How to get bar charts, line charts and comparison tables out of earnings call transcripts with a single prompt, and why every number stays sourced.
- [Follow-up techniques: drilling into earnings calls](https://earnings.chat/blog/follow-up-techniques): How to run a multi-question conversation over earnings call transcripts: drill down, zoom out, switch models, and keep the thread across follow-ups.
- [From transcript to thesis: an earnings research workflow](https://earnings.chat/blog/earnings-research-workflow): A repeatable research workflow over earnings call transcripts: watchlist rounds, sector reads, comparison tables, and exports that keep their sources.
- [Why grounded AI beats generic AI for earnings analysis](https://earnings.chat/blog/grounded-ai-earnings-analysis): Generic chatbots invent plausible earnings figures. Grounded AI earnings analysis queries transcripts, quotes verbatim, and reports gaps as gaps.
- [What is an earnings call? A practical guide](https://earnings.chat/blog/what-is-an-earnings-call): What an earnings call is, what happens during one, who can listen, and why the Q&A section moves markets more than the prepared remarks.
- [How to read an earnings call transcript, fast](https://earnings.chat/blog/how-to-read-earnings-call-transcripts): A method for reading earnings call transcripts in minutes instead of an hour: where to start, what to skip, and how to interpret what management says.
- [Earnings call summaries: what a good one contains](https://earnings.chat/blog/earnings-call-summary-guide): The five elements every earnings call summary needs, the failure modes of AI-generated summaries, and how to judge whether a summary can be trusted.
- [Management guidance explained: raised, held, cut](https://earnings.chat/blog/management-guidance-explained): What guidance means in an earnings report, why raised or cut guidance moves stocks more than results, and how to track guidance language across quarters.
- [Earnings season: how to prepare a watchlist](https://earnings.chat/blog/earnings-season-watchlist): When earnings season happens, how to build a watchlist that survives peak weeks, and what to check for each name before and after the call.
- [Can AI analyze earnings calls? What works in 2026](https://earnings.chat/blog/can-ai-analyze-earnings-calls): What AI can genuinely do with earnings calls, where general chatbots fail, and how grounded systems turn transcripts into sourced answers.
- [Did the company beat earnings? How to answer it properly](https://earnings.chat/blog/did-a-company-beat-earnings): What "beating earnings" actually means, why a beat can still sink the stock, and how to read the call behind the headline in five minutes.
- [What did companies say about AI? Tracking a theme across calls](https://earnings.chat/blog/track-a-theme-across-earnings-calls): How to follow one theme, AI demand, tariffs, capacity, China, through hundreds of earnings calls, and turn scattered sentences into a sector signal.
- [Will the stock go up after earnings? What AI can honestly tell you](https://earnings.chat/blog/can-ai-predict-stock-after-earnings): Why no AI can predict the post-earnings move, what the transcripts genuinely reveal, and the questions that turn a call into evidence instead of a forecast.
- [Who raised guidance this week? A screening habit that works](https://earnings.chat/blog/who-raised-guidance-this-week): Guidance changes are the strongest recurring signal in earnings season. How to screen raises and cuts week by week with one question, and what to do with the list.
- [What risks did management mention? Reading danger in earnings calls](https://earnings.chat/blog/risks-in-earnings-calls): Where risk actually hides in an earnings call: prepared caveats, Q&A deflections, and the language changes that precede bad quarters.
- [Compare two companies by their earnings calls, properly](https://earnings.chat/blog/compare-companies-earnings-calls): How to compare competitors, suppliers and customers through their earnings calls: which pairs reveal the most, and the questions that make comparisons honest.
- [Earnings call sentiment analysis: what tone really tells you](https://earnings.chat/blog/earnings-call-sentiment-analysis): What sentiment analysis on earnings calls measures, where it genuinely predicts something, and how to use tone as evidence instead of a vibe.
- [Can you trust AI with earnings numbers? A verification guide](https://earnings.chat/blog/can-you-trust-ai-with-earnings-numbers): When AI-quoted financial figures can be trusted, when they cannot, and the thirty-second checks that separate sourced numbers from fluent guesses.
- [Before you buy: an earnings-call due diligence checklist](https://earnings.chat/blog/earnings-call-due-diligence-checklist): Eight checks against a company's earnings calls before taking a position: promises kept, guidance credibility, margin trajectory, and the questions that run them.

## Legal

- [Terms of Service](https://earnings.chat/terms)
- [Privacy Notice](https://earnings.chat/privacy)
- [Refund Policy](https://earnings.chat/refund-policy)

earnings.chat is a brand of DREAVERR Digital Solutions LLP. Contact: support@earnings.chat