Snowflake (SNOW)

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As of Oct 23, 2026, Kalshi and Polymarket have settled 69 word markets on Snowflake’s earnings calls since 2025, on 33 different words. Next: Keynote, Nov 3, 2026 (16 words listed).

Next: Keynote ·

16 words listed · Occasion page

Who counts: Kalshi: named speaker only

Kalshi

Snowflake: the words listed for Tue, Nov 3, how often each was said on past occasions, and the probability for this one
WordQ3’25Nov '24Q4’25Feb '25Q1’26May '25Q2’26Aug '25Q3’26Dec '25Q4’26Feb '26Q1’27May '26Q2’27last callTrendLast 8ScriptedAll-timeProbabilityMarket
AWS / Amazon Web Service3 times: AWS / Amazon Web Service, Q3’25 Nov '24, counts as said1 time: AWS / Amazon Web Service, Q4’25 Feb '25, counts as said1 time: AWS / Amazon Web Service, Q1’26 May '25, counts as said1 time: AWS / Amazon Web Service, Q2’26 Aug '25, counts as said2 times: AWS / Amazon Web Service, Q3’26 Dec '25, counts as said0 times: AWS / Amazon Web Service, Q4’26 Feb '267 times: AWS / Amazon Web Service, Q1’27 May '26, counts as said1 time: AWS / Amazon Web Service, Q2’27 Sep '26, counts as saidrising7/888%3/838%20/3656%57%n=4 from settlement history28¢ Kalshi
OpenAI / ChatGPT0 times: OpenAI / ChatGPT, Q3’25 Nov '244 times: OpenAI / ChatGPT, Q4’25 Feb '25, counts as said3 times: OpenAI / ChatGPT, Q1’26 May '25, counts as said3 times: OpenAI / ChatGPT, Q2’26 Aug '25, counts as said0 times: OpenAI / ChatGPT, Q3’26 Dec '252 times: OpenAI / ChatGPT, Q4’26 Feb '26, counts as said2 times: OpenAI / ChatGPT, Q1’27 May '26, counts as said1 time: OpenAI / ChatGPT, Q2’27 Sep '26, counts as saidsteady6/875%5/862%6/3617%–n=1 too little history for a probability23¢ Kalshi
Azure2 times: Azure, Q3’25 Nov '24, counts as said0 times: Azure, Q4’25 Feb '254 times: Azure, Q1’26 May '25, counts as said5 times: Azure, Q2’26 Aug '25, counts as said0 times: Azure, Q3’26 Dec '250 times: Azure, Q4’26 Feb '261 time: Azure, Q1’27 May '26, counts as said1 time: Azure, Q2’27 Sep '26, counts as saidfalling5/862%1/812%15/3642%53%n=4 from settlement history32¢ Kalshi
Open Source1 time: Open Source, Q3’25 Nov '24, counts as said1 time: Open Source, Q4’25 Feb '25, counts as said1 time: Open Source, Q1’26 May '25, counts as said1 time: Open Source, Q2’26 Aug '25, counts as said0 times: Open Source, Q3’26 Dec '250 times: Open Source, Q4’26 Feb '261 time: Open Source, Q1’27 May '26, counts as said3 times: Open Source, Q2’27 Sep '26, counts as saidrising6/875%1/812%8/3622%53%n=4 from settlement history38¢ Kalshi
Automated / Automation1 time: Automated / Automation, Q3’25 Nov '24, counts as said1 time: Automated / Automation, Q4’25 Feb '25, counts as said0 times: Automated / Automation, Q1’26 May '251 time: Automated / Automation, Q2’26 Aug '25, counts as said0 times: Automated / Automation, Q3’26 Dec '250 times: Automated / Automation, Q4’26 Feb '264 times: Automated / Automation, Q1’27 May '26, counts as said1 time: Automated / Automation, Q2’27 Sep '26, counts as saidrising5/862%3/838%6/3617%–new new word, no probability yet72¢ Kalshi
CoCo0 times: CoCo, Q3’25 Nov '240 times: CoCo, Q4’25 Feb '250 times: CoCo, Q1’26 May '250 times: CoCo, Q2’26 Aug '250 times: CoCo, Q3’26 Dec '252 times: CoCo, Q4’26 Feb '26, counts as said42 times: CoCo, Q1’27 May '26, counts as said40 times: CoCo, Q2’27 Sep '26, counts as saidrising3/838%2/825%3/368%–new new word, no probability yet70¢ Kalshi
Cortex Sense0 times: Cortex Sense, Q3’25 Nov '240 times: Cortex Sense, Q4’25 Feb '250 times: Cortex Sense, Q1’26 May '250 times: Cortex Sense, Q2’26 Aug '250 times: Cortex Sense, Q3’26 Dec '250 times: Cortex Sense, Q4’26 Feb '260 times: Cortex Sense, Q1’27 May '261 time: Cortex Sense, Q2’27 Sep '26, counts as saidrising1/812%1/812%1/363%–new new word, no probability yet28¢ Kalshi
Disney1 time: Disney, Q3’25 Nov '24, counts as said0 times: Disney, Q4’25 Feb '251 time: Disney, Q1’26 May '25, counts as said0 times: Disney, Q2’26 Aug '250 times: Disney, Q3’26 Dec '250 times: Disney, Q4’26 Feb '260 times: Disney, Q1’27 May '260 times: Disney, Q2’27 Sep '26falling2/825%0/80%6/3617%–new new word, no probability yet80¢ Kalshi
Horizon Catalog0 times: Horizon Catalog, Q3’25 Nov '240 times: Horizon Catalog, Q4’25 Feb '250 times: Horizon Catalog, Q1’26 May '250 times: Horizon Catalog, Q2’26 Aug '251 time: Horizon Catalog, Q3’26 Dec '25, counts as said0 times: Horizon Catalog, Q4’26 Feb '260 times: Horizon Catalog, Q1’27 May '260 times: Horizon Catalog, Q2’27 Sep '26steady1/812%1/812%1/363%–new new word, no probability yet22¢ Kalshi
MCP / Model Context Protocol0 times: MCP / Model Context Protocol, Q3’25 Nov '240 times: MCP / Model Context Protocol, Q4’25 Feb '250 times: MCP / Model Context Protocol, Q1’26 May '250 times: MCP / Model Context Protocol, Q2’26 Aug '250 times: MCP / Model Context Protocol, Q3’26 Dec '251 time: MCP / Model Context Protocol, Q4’26 Feb '26, counts as said2 times: MCP / Model Context Protocol, Q1’27 May '26, counts as said0 times: MCP / Model Context Protocol, Q2’27 Sep '26rising2/825%0/80%2/366%–new new word, no probability yet31¢ Kalshi
Postgres0 times: Postgres, Q3’25 Nov '240 times: Postgres, Q4’25 Feb '250 times: Postgres, Q1’26 May '258 times: Postgres, Q2’26 Aug '25, counts as said3 times: Postgres, Q3’26 Dec '25, counts as said2 times: Postgres, Q4’26 Feb '26, counts as said0 times: Postgres, Q1’27 May '261 time: Postgres, Q2’27 Sep '26, counts as saidsteady4/850%2/825%4/3611%–new new word, no probability yet40¢ Kalshi
CoWork / Snowflake Intelligence2 times: CoWork / Snowflake Intelligence, Q3’25 Nov '24, counts as said3 times: CoWork / Snowflake Intelligence, Q4’25 Feb '25, counts as said0 times: CoWork / Snowflake Intelligence, Q1’26 May '2512 times: CoWork / Snowflake Intelligence, Q2’26 Aug '25, counts as said22 times: CoWork / Snowflake Intelligence, Q3’26 Dec '25, counts as said28 times: CoWork / Snowflake Intelligence, Q4’26 Feb '26, counts as said23 times: CoWork / Snowflake Intelligence, Q1’27 May '26, counts as said22 times: CoWork / Snowflake Intelligence, Q2’27 Sep '26, counts as saidrising7/888%6/875%7/3619%–n=1 too little history for a probability75¢ Kalshi
Databricks0 times: Databricks, Q3’25 Nov '240 times: Databricks, Q4’25 Feb '250 times: Databricks, Q1’26 May '250 times: Databricks, Q2’26 Aug '250 times: Databricks, Q3’26 Dec '250 times: Databricks, Q4’26 Feb '261 time: Databricks, Q1’27 May '26, counts as said0 times: Databricks, Q2’27 Sep '26steady1/812%0/80%2/366%–n=1 too little history for a probability6¢ Kalshi
Iceberg20 times: Iceberg, Q3’25 Nov '24, counts as said9 times: Iceberg, Q4’25 Feb '25, counts as said4 times: Iceberg, Q1’26 May '25, counts as said2 times: Iceberg, Q2’26 Aug '25, counts as said0 times: Iceberg, Q3’26 Dec '253 times: Iceberg, Q4’26 Feb '26, counts as said0 times: Iceberg, Q1’27 May '260 times: Iceberg, Q2’27 Sep '26falling5/862%4/850%12/3633%37%n=4 from settlement history33¢ Kalshi
Claude / Anthropic2 times: Claude / Anthropic, Q3’25 Nov '24, counts as said8 times: Claude / Anthropic, Q4’25 Feb '25, counts as said2 times: Claude / Anthropic, Q1’26 May '25, counts as said1 time: Claude / Anthropic, Q2’26 Aug '25, counts as said3 times: Claude / Anthropic, Q3’26 Dec '25, counts as said2 times: Claude / Anthropic, Q4’26 Feb '26, counts as said1 time: Claude / Anthropic, Q1’27 May '26, counts as said1 time: Claude / Anthropic, Q2’27 Sep '26, counts as saidfalling8/8100%4/850%8/3622%34%n=2 from settlement history57¢ Kalshi
Competition1 time: Competition, Q3’25 Nov '24, counts as said0 times: Competition, Q4’25 Feb '251 time: Competition, Q1’26 May '25, counts as said0 times: Competition, Q2’26 Aug '250 times: Competition, Q3’26 Dec '250 times: Competition, Q4’26 Feb '260 times: Competition, Q1’27 May '260 times: Competition, Q2’27 Sep '26falling2/825%1/812%5/3614%31%n=4 from settlement history18¢ Kalshi

The cells count each word on Snowflake’s last 8 earnings calls (company speakers + operator). This keynote market counts named speaker only; each word’s page has its settled record.

  • times said on that call
  • counted as said (ring)
  • no call
  • probability next time, with n
  • Select a word or a cell for its quotes
What the columns mean
Last N
of the appearances shown, how many counted as said
Trend
how the count moved over these calls
Scripted
calls of the last 8 where it was said in the prepared remarks
All-time
every call on file
Probability
the calibrated probability for the next listing; n is the settled observations behind it when it was computed, '+tx' adds the transcripts, 'new' means too little history

Market prices are mid quotes from Kalshi and Polymarket, shown for context, may be delayed by several minutes and are not a signal. Quotes as of Oct 20, 2026 23:45 UTC.

Every word

1–24 of 24, most settled first
Snowflake: every word with settled markets, most settled first
WordQ3’25Nov '24Q4’25Feb '25Q1’26May '25Q2’26Aug '25Q3’26Dec '25Q4’26Feb '26Q1’27May '26Q2’27last callTrendLast 8ScriptedAll-timeProbability
AWS3 times: AWS, Q3’25 Nov '24, counts as said1 time: AWS, Q4’25 Feb '25, counts as said1 time: AWS, Q1’26 May '25, counts as said1 time: AWS, Q2’26 Aug '25, counts as said2 times: AWS, Q3’26 Dec '25, counts as said0 times: AWS, Q4’26 Feb '267 times: AWS, Q1’27 May '26, counts as said1 time: AWS, Q2’27 Sep '26, counts as saidrising7/888%3/838%20/3656%81%n=4+tx from settlement history blended with transcripts
Hiring2 times: Hiring, Q3’25 Nov '24, counts as said2 times: Hiring, Q4’25 Feb '25, counts as said1 time: Hiring, Q1’26 May '25, counts as said1 time: Hiring, Q2’26 Aug '25, counts as said0 times: Hiring, Q3’26 Dec '251 time: Hiring, Q4’26 Feb '26, counts as said3 times: Hiring, Q1’27 May '26, counts as said0 times: Hiring, Q2’27 Sep '26falling6/875%4/850%21/3658%54%n=4+tx from settlement history blended with transcripts
Europe2 times: Europe, Q3’25 Nov '24, counts as said0 times: Europe, Q4’25 Feb '250 times: Europe, Q1’26 May '251 time: Europe, Q2’26 Aug '25, counts as said0 times: Europe, Q3’26 Dec '251 time: Europe, Q4’26 Feb '26, counts as said0 times: Europe, Q1’27 May '260 times: Europe, Q2’27 Sep '26falling3/838%1/812%15/3642%25%n=4+tx from settlement history blended with transcripts
Repurchase4 times: Repurchase, Q3’25 Nov '24, counts as said2 times: Repurchase, Q4’25 Feb '25, counts as said1 time: Repurchase, Q1’26 May '25, counts as said1 time: Repurchase, Q2’26 Aug '25, counts as said1 time: Repurchase, Q3’26 Dec '25, counts as said2 times: Repurchase, Q4’26 Feb '26, counts as said2 times: Repurchase, Q1’27 May '26, counts as said0 times: Repurchase, Q2’27 Sep '26falling7/888%7/888%15/3642%70%n=3+tx from settlement history blended with transcripts
Retention2 times: Retention, Q3’25 Nov '24, counts as said2 times: Retention, Q4’25 Feb '25, counts as said1 time: Retention, Q1’26 May '25, counts as said7 times: Retention, Q2’26 Aug '25, counts as said3 times: Retention, Q3’26 Dec '25, counts as said2 times: Retention, Q4’26 Feb '26, counts as said1 time: Retention, Q1’27 May '26, counts as said2 times: Retention, Q2’27 Sep '26, counts as saidsteady8/8100%8/8100%22/3661%85%n=3+tx from settlement history blended with transcripts
Partnership2 times: Partnership, Q3’25 Nov '24, counts as said10 times: Partnership, Q4’25 Feb '25, counts as said3 times: Partnership, Q1’26 May '25, counts as said5 times: Partnership, Q2’26 Aug '25, counts as said12 times: Partnership, Q3’26 Dec '25, counts as said4 times: Partnership, Q4’26 Feb '26, counts as said7 times: Partnership, Q1’27 May '26, counts as said1 time: Partnership, Q2’27 Sep '26, counts as saidsteady8/8100%7/888%20/3656%92%n=2+tx from settlement history blended with transcripts
Security2 times: Security, Q3’25 Nov '24, counts as said2 times: Security, Q4’25 Feb '25, counts as said2 times: Security, Q1’26 May '25, counts as said0 times: Security, Q2’26 Aug '252 times: Security, Q3’26 Dec '25, counts as said4 times: Security, Q4’26 Feb '26, counts as said6 times: Security, Q1’27 May '26, counts as said4 times: Security, Q2’27 Sep '26, counts as saidrising7/888%6/875%30/3683%88%n=2+tx from settlement history blended with transcripts
Accenture0 times: Accenture, Q3’25 Nov '240 times: Accenture, Q4’25 Feb '250 times: Accenture, Q1’26 May '250 times: Accenture, Q2’26 Aug '252 times: Accenture, Q3’26 Dec '25, counts as said0 times: Accenture, Q4’26 Feb '260 times: Accenture, Q1’27 May '260 times: Accenture, Q2’27 Sep '26steady1/812%1/812%4/3611%22%n=2+tx from settlement history blended with transcripts
Claude0 times: Claude, Q3’25 Nov '243 times: Claude, Q4’25 Feb '25, counts as said0 times: Claude, Q1’26 May '250 times: Claude, Q2’26 Aug '250 times: Claude, Q3’26 Dec '250 times: Claude, Q4’26 Feb '260 times: Claude, Q1’27 May '260 times: Claude, Q2’27 Sep '26falling1/812%1/812%1/363%14%n=2+tx from settlement history blended with transcripts
AI20+ times34 times: AI 20+ times, Q3’25 Nov '24, counts as said35 times: AI 20+ times, Q4’25 Feb '25, counts as said65 times: AI 20+ times, Q1’26 May '25, counts as said66 times: AI 20+ times, Q2’26 Aug '25, counts as said81 times: AI 20+ times, Q3’26 Dec '25, counts as said60 times: AI 20+ times, Q4’26 Feb '26, counts as said77 times: AI 20+ times, Q1’27 May '26, counts as said77 times: AI 20+ times, Q2’27 Sep '26, counts as saidrising8/8100%6/875%14/3639%88%n=1+tx from settlement history blended with transcripts
Backlog1 time: Backlog, Q3’25 Nov '24, counts as said0 times: Backlog, Q4’25 Feb '250 times: Backlog, Q1’26 May '250 times: Backlog, Q2’26 Aug '252 times: Backlog, Q3’26 Dec '25, counts as said0 times: Backlog, Q4’26 Feb '263 times: Backlog, Q1’27 May '26, counts as said1 time: Backlog, Q2’27 Sep '26, counts as saidrising4/850%0/80%7/3619%69%n=1+tx from settlement history blended with transcripts
Code0 times: Code, Q3’25 Nov '241 time: Code, Q4’25 Feb '25, counts as said3 times: Code, Q1’26 May '25, counts as said2 times: Code, Q2’26 Aug '25, counts as said0 times: Code, Q3’26 Dec '2524 times: Code, Q4’26 Feb '26, counts as said21 times: Code, Q1’27 May '26, counts as said2 times: Code, Q2’27 Sep '26, counts as saidrising6/875%3/838%14/3639%93%n=1+tx from settlement history blended with transcripts
Compute1 time: Compute, Q3’25 Nov '24, counts as said0 times: Compute, Q4’25 Feb '251 time: Compute, Q1’26 May '25, counts as said0 times: Compute, Q2’26 Aug '250 times: Compute, Q3’26 Dec '252 times: Compute, Q4’26 Feb '26, counts as said1 time: Compute, Q1’27 May '26, counts as said0 times: Compute, Q2’27 Sep '26steady4/850%1/812%15/3642%50%n=1+tx from settlement history blended with transcripts
Consumption18 times: Consumption, Q3’25 Nov '24, counts as said7 times: Consumption, Q4’25 Feb '25, counts as said10 times: Consumption, Q1’26 May '25, counts as said12 times: Consumption, Q2’26 Aug '25, counts as said18 times: Consumption, Q3’26 Dec '25, counts as said11 times: Consumption, Q4’26 Feb '26, counts as said9 times: Consumption, Q1’27 May '26, counts as said12 times: Consumption, Q2’27 Sep '26, counts as saidsteady8/8100%7/888%24/3667%96%n=1+tx from settlement history blended with transcripts
Cortex19 times: Cortex, Q3’25 Nov '24, counts as said12 times: Cortex, Q4’25 Feb '25, counts as said19 times: Cortex, Q1’26 May '25, counts as said10 times: Cortex, Q2’26 Aug '25, counts as said10 times: Cortex, Q3’26 Dec '25, counts as said24 times: Cortex, Q4’26 Feb '26, counts as said23 times: Cortex, Q1’27 May '26, counts as said5 times: Cortex, Q2’27 Sep '26, counts as saidsteady8/8100%8/8100%12/3633%96%n=1+tx from settlement history blended with transcripts
Enterprise8 times: Enterprise, Q3’25 Nov '24, counts as said5 times: Enterprise, Q4’25 Feb '25, counts as said8 times: Enterprise, Q1’26 May '25, counts as said20 times: Enterprise, Q2’26 Aug '25, counts as said15 times: Enterprise, Q3’26 Dec '25, counts as said21 times: Enterprise, Q4’26 Feb '26, counts as said23 times: Enterprise, Q1’27 May '26, counts as said23 times: Enterprise, Q2’27 Sep '26, counts as saidrising8/8100%8/8100%24/3667%97%n=1+tx from settlement history blended with transcripts
Fiscal5+ times6 times: Fiscal 5+ times, Q3’25 Nov '24, counts as said7 times: Fiscal 5+ times, Q4’25 Feb '25, counts as said7 times: Fiscal 5+ times, Q1’26 May '25, counts as said4 times: Fiscal 5+ times, Q2’26 Aug '25, below the threshold6 times: Fiscal 5+ times, Q3’26 Dec '25, counts as said13 times: Fiscal 5+ times, Q4’26 Feb '26, counts as said3 times: Fiscal 5+ times, Q1’27 May '26, below the threshold7 times: Fiscal 5+ times, Q2’27 Sep '26, counts as saidsteady6/875%3/838%33/3692%70%n=1+tx from settlement history blended with transcripts
OpenAI0 times: OpenAI, Q3’25 Nov '244 times: OpenAI, Q4’25 Feb '25, counts as said3 times: OpenAI, Q1’26 May '25, counts as said2 times: OpenAI, Q2’26 Aug '25, counts as said0 times: OpenAI, Q3’26 Dec '252 times: OpenAI, Q4’26 Feb '26, counts as said2 times: OpenAI, Q1’27 May '26, counts as said1 time: OpenAI, Q2’27 Sep '26, counts as saidsteady6/875%5/862%6/3617%71%n=1+tx from settlement history blended with transcripts
Quarter15+ times38 times: Quarter 15+ times, Q3’25 Nov '24, counts as said20 times: Quarter 15+ times, Q4’25 Feb '25, counts as said26 times: Quarter 15+ times, Q1’26 May '25, counts as said35 times: Quarter 15+ times, Q2’26 Aug '25, counts as said45 times: Quarter 15+ times, Q3’26 Dec '25, counts as said23 times: Quarter 15+ times, Q4’26 Feb '26, counts as said39 times: Quarter 15+ times, Q1’27 May '26, counts as said33 times: Quarter 15+ times, Q2’27 Sep '26, counts as saidsteady8/8100%4/850%32/3689%84%n=1+tx from settlement history blended with transcripts
Cloud10+ times5 times: Cloud 10+ times, Q3’25 Nov '24, below the threshold5 times: Cloud 10+ times, Q4’25 Feb '25, below the threshold5 times: Cloud 10+ times, Q1’26 May '25, below the threshold14 times: Cloud 10+ times, Q2’26 Aug '25, counts as said11 times: Cloud 10+ times, Q3’26 Dec '25, counts as said6 times: Cloud 10+ times, Q4’26 Feb '26, below the threshold13 times: Cloud 10+ times, Q1’27 May '26, counts as said7 times: Cloud 10+ times, Q2’27 Sep '26, below the thresholdrising3/838%1/812%15/3642%29%n=1+tx from settlement history blended with transcripts
Crunchy Data0 times: Crunchy Data, Q3’25 Nov '240 times: Crunchy Data, Q4’25 Feb '250 times: Crunchy Data, Q1’26 May '250 times: Crunchy Data, Q2’26 Aug '251 time: Crunchy Data, Q3’26 Dec '25, counts as said0 times: Crunchy Data, Q4’26 Feb '260 times: Crunchy Data, Q1’27 May '260 times: Crunchy Data, Q2’27 Sep '26steady1/812%0/80%1/363%20%n=1+tx from settlement history blended with transcripts
Healthcare1 time: Healthcare, Q3’25 Nov '24, counts as said1 time: Healthcare, Q4’25 Feb '25, counts as said1 time: Healthcare, Q1’26 May '25, counts as said1 time: Healthcare, Q2’26 Aug '25, counts as said0 times: Healthcare, Q3’26 Dec '250 times: Healthcare, Q4’26 Feb '260 times: Healthcare, Q1’27 May '260 times: Healthcare, Q2’27 Sep '26falling4/850%3/838%16/3644%21%n=1+tx from settlement history blended with transcripts
Retail0 times: Retail, Q3’25 Nov '240 times: Retail, Q4’25 Feb '251 time: Retail, Q1’26 May '25, counts as said0 times: Retail, Q2’26 Aug '250 times: Retail, Q3’26 Dec '250 times: Retail, Q4’26 Feb '260 times: Retail, Q1’27 May '260 times: Retail, Q2’27 Sep '26steady1/812%1/812%21/3658%15%n=1+tx from settlement history blended with transcripts
Snowflake Intelligence2 times: Snowflake Intelligence, Q3’25 Nov '24, counts as said3 times: Snowflake Intelligence, Q4’25 Feb '25, counts as said0 times: Snowflake Intelligence, Q1’26 May '2512 times: Snowflake Intelligence, Q2’26 Aug '25, counts as said25 times: Snowflake Intelligence, Q3’26 Dec '25, counts as said29 times: Snowflake Intelligence, Q4’26 Feb '26, counts as said24 times: Snowflake Intelligence, Q1’27 May '26, counts as said0 times: Snowflake Intelligence, Q2’27 Sep '26rising6/875%5/862%6/3617%88%n=1+tx from settlement history blended with transcripts
What the columns mean
Last N
of the appearances shown, how many counted as said
Trend
how the count moved over these calls
Scripted
calls of the last 8 where it was said in the prepared remarks
All-time
every call on file
Probability
the calibrated probability for the next listing; n is the settled observations behind it when it was computed, '+tx' adds the transcripts, 'new' means too little history

Appearances and settled record

latest 12

Our transcript counts matched how 67 of 69 settled words resolved (97.1%). How we check.

Recent appearances and how their listed words settled
DateAppearanceTextWordsSettledOur count agreed
Sep 2, 2026Earnings call FY27 Q2Speaker-labelled transcript2613 yes · 13 no26/26 (100%)
May 27, 2026Earnings call FY27 Q1Speaker-labelled transcript138 yes · 5 no13/13 (100%)
Feb 25, 2026Earnings call FY26 Q4Speaker-labelled transcript106 yes · 4 no10/10 (100%)
Dec 3, 2025Earnings call FY26 Q3Speaker-labelled transcript2010 yes · 10 no18/20 (90%)
Aug 27, 2025Earnings call FY26 Q2Speaker-labelled transcript0––
May 21, 2025Earnings call FY26 Q1Speaker-labelled transcript0––
Feb 26, 2025Earnings call FY25 Q4Speaker-labelled transcript0––
Nov 20, 2024Earnings call FY25 Q3Speaker-labelled transcript0––
Aug 21, 2024Earnings call FY25 Q2Speaker-labelled transcript0––
May 22, 2024Earnings call FY25 Q1Speaker-labelled transcript0––
Feb 28, 2024Earnings call FY24 Q4Speaker-labelled transcript0––
Nov 29, 2023Earnings call FY24 Q3Speaker-labelled transcript0––

Citeparrot.markets, “Snowflake (SNOW): earnings call word history”, as of Oct 23, 2026, https://parrot.markets/earnings/SNOW (CC BY 4.0)

Agents: this page as TSV at /api/v1/entity/snowflake · words: /api/v1/history?entity=snowflake · columnar JSON in #parrot-data · llms.txt · MCP