Full transcripts as clean JSON: every speaker turn tagged with name and role, searchable across 248,000+ calls from 70 countries. One GET request per transcript - no scraping, no PDFs.
# 1. Scan a global watchlist for "agentic AI" mentions in Q1 2026
# → per-ticker mention counts across markets, in one request
curl -sG https://earningscalls.dev/api/v1/search/by_ticker \
-H "X-API-Key: ec_..." \
--data-urlencode 'q="agentic AI"' \
--data-urlencode 'tickers=NVDA,SAP,SHEL' \
--data-urlencode 'date_from=2026-01-01' \
--data-urlencode 'date_to=2026-04-30'
// example response · ~312 ms
{
"query": "\"agentic AI\"",
"tickers_scoped": 3,
"total_companies": 3,
"total_calls": 7,
"results_count": 3,
"results": [
{ "ticker": "NVDA", "company_name": "NVIDIA Corporation", "calls_matched": 4, "last_match_date": "2026-03-17T16:00:00.000Z" },
{ "ticker": "SAP", "company_name": "SAP SE", "calls_matched": 2, "last_match_date": "2026-04-23T21:00:00.000Z" },
{ "ticker": "SHEL", "company_name": "Shell plc", "calls_matched": 1, "last_match_date": "2026-02-05T14:30:00.000Z" }
]
}Four primitives that turn raw earnings calls into a clean dataset your strategy can consume.
Every segment pre-tagged. Five categories with verified counts: executive (826k), analyst (470k), operator (208k), attendee (33k), shareholder (4k). 93% carry the speaker's name.
GET /api/v1/speakers/:idSweep the live archive for phrases like "margin pressure" in one query. Boolean operators, exact phrases, ticker filters across global markets.
GET /api/v1/search?q=…Scan up to 500 tickers in a single request. Per-ticker counts, sector breakdown, last-match date — screen a global universe in one call.
GET /api/v1/search/by_tickerIncremental sync via since / after_id. Re-runnable, idempotent. New calls land within hours of the wire and flow straight into your pipeline.
GET /api/v1/transcripts/recentChange the ticker, click Run, see the actual response.
GET /api/v1/search?q=agentic AI&type=speakers&speaker_type=Executive&ticker=NVDA&limit=5
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Coverage, query semantics, and what the data supports.
Full text plus a segment array: speaker name, role tag, order and the verbatim text of each turn, along with call metadata (ticker with venue suffix, exchange, sector, date). See the API reference for the full schema.
248,000+ calls from 12,000+ companies across 70 countries and 170+ exchanges, from 2020 to today, growing daily during earnings season. New transcripts land within minutes of the call.
Same API, same key - this page focuses on the transcript endpoints. The earnings call API overview covers everything else: calendar, summaries, companies and search.
Every transcript is free to read on the website - the API plans are only for programmatic JSON access, from $24.99/month.
Yes - that is the most common quant use: cursor pagination over /transcripts/recent backfills the archive into a local store, giving you an earnings call transcript dataset of 248,000+ calls with speaker segments for ML, RAG or factor research. New calls stream in within minutes, so the dataset never goes stale. The same endpoints are what most teams mean by an earnings call transcripts API.
Reading transcripts on the website is free without an account. For programmatic access there is a free tier via RapidAPI (50 requests/month, previews and lookups) - details on the free earnings call API page - and full JSON access starts at $24.99/month. The earnings call API overview lists every endpoint group beyond transcripts.
Judge on three axes: coverage (248,000+ calls from 70 countries here), structure (speaker segments with roles, not raw text blobs), and price ($24.99/month vs $39-$825 at comparable providers, no annual lock-in). The earnings call API overview shows everything beyond transcripts; if you are still building intuition for the underlying data, start with what an earnings call is and how to read a transcript like a pro.
What research professionals say about the data and the API.
“Excellent service. Comprehensive coverage of the sectors I wanted and the API is very easy to work with. Highly recommend.”
“Transcripts are accurate, and the API is very user friendly.”
“Connecting Claude through MCP to analyze earnings transcripts has never been easier.”
From the team that brought you the Earnings Calls API - same clean data, same fair pricing.
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