MARKET RESEARCH
Competitor Guidance-Change Brief from Quarterly Earnings Calls
On a schedule, scrapes the latest earnings-call transcripts for a watchlist of competitors, extracts every forward-guidance statement, compares it against the prior quarter.
How it runs
The automated pipeline, trigger to output.
- TriggerWeekly earnings-season schedule fires
- ActionFirecrawl scrapes each competitor IR transcript pageFirecrawl
- LogicSkip companies with no new transcript since last run
- ActionOpenAI extracts and classifies guidance vs. prior quarterOpenAI
- ActionOpenAI synthesizes ranked comparative delta briefOpenAI
- OutputPublish brief and guidance table to NotionNotion
What it does
Tracks a list of competitor tickers and, each time a new quarterly earnings transcript is posted, mines it for forward-looking guidance (revenue, margin, capex, unit, segment commentary). It diffs each statement against what the same company said last quarter and writes a single comparative brief that highlights who raised, held, or cut guidance.
When to use it
For competitive-intelligence or strategy-finance teams who manually skim earnings calls every quarter and want a standing, side-by-side view of how rivals' outlooks are shifting — without reading ten transcripts by hand.
How it works
- 1A weekly schedule kicks off the run during earnings season.
- 2Firecrawl crawls each competitor's investor-relations transcript page and returns clean text.
- 3A filter step skips any company whose latest transcript date matches the one already on file (no new call yet).
- 4OpenAI extracts structured guidance items and classifies each as raised, maintained, or lowered versus the stored prior-quarter figures.
- 5OpenAI synthesizes a comparative brief ranking the most material outlook changes.
- 6The brief and per-company guidance table are published to a Notion database page.
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.
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