MARKET RESEARCH
Quarterly Earnings-Call Theme Tracker Across a Competitor Watchlist
Each quarter, scrapes the latest earnings-call transcripts for every competitor on your watchlist, extracts the strategic themes each one is emphasizing, and writes a labeled.
How it runs
The automated pipeline, trigger to output.
- TriggerQuarterly schedule fires after earnings window
- ActionLoad competitor watchlist and IR transcript URLsNotion
- ActionScrape latest transcript per companyFirecrawl
- LogicSkip companies whose latest quarter is already on file
- ActionExtract themes and emphasis weights from each transcriptOpenAI
- OutputUpsert one Notion row per company-quarterNotion
What it does
Maintains a living view of what your competitors are talking about on their earnings calls. Once a quarter it pulls each company's newest transcript, classifies the strategic themes they emphasize (AI, margins, headcount, pricing, international expansion, etc.), scores how heavily each theme is weighted, and updates one Notion database row per company so you can see how each narrative shifts across quarters.
When to use it
Use it when you cover a defined set of competitors and need a repeatable, low-effort way to know what each is prioritizing — without reading dozens of transcripts by hand every reporting season. Ideal for competitive-intelligence, strategy, and investor-relations teams.
How it works
- 1A quarterly schedule fires after the typical earnings window closes.
- 2The flow reads the watchlist of competitor companies and their investor-relations transcript URLs.
- 3Firecrawl scrapes the latest published transcript for each company.
- 4A filter skips any company whose newest transcript matches the quarter already on file, avoiding duplicate processing.
- 5OpenAI extracts the dominant themes per transcript and assigns each a 0-100 emphasis weight.
- 6The results upsert into a Notion database, one row per company-quarter, ready for side-by-side review.
Set it up
What you configure once, before turning it on.
- 1Connect FirecrawlCrawl, scrape, structured extract.
- 2Connect OpenAIModels, embeddings, files.
- 3Connect NotionPages, databases, comments.
- 4Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 5Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 6Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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Run it inside a business
This workflow drops into a full company template. Import the org, and this is one of the playbooks its agents run.
Run this workflow in your colony.
14-day trial. No DevOps. No Sales call. Provisioned in under a minute.