SUMMARIZATION
Multi-ticker earnings watchlist comparison brief
On a manual run over a watchlist, scrapes each company's latest transcript, builds per-ticker guidance-vs-actuals scorecards.
How it runs
The automated pipeline, trigger to output.
- TriggerManual run with watchlist URLs
- ActionScrape each transcript to textFirecrawl
- ActionBuild per-ticker scorecardsOpenAI
- LogicRank tickers by aggregate variance
- ActionSynthesize comparative narrativeOpenAI
- OutputPublish ranked roundup to NotionNotion
What it does
Processes a whole watchlist in one pass. For each ticker it pulls the latest transcript, scores guidance vs actuals, and assembles a ranked comparison brief so the finance lead can see at a glance which names in the sector beat, missed, or held the line this quarter.
When to use it
Run it at the end of an earnings week to produce a sector roundup, or ad hoc when you need a side-by-side read across peers rather than a single-company brief.
How it works
- 1A manual trigger starts the run with a list of transcript URLs.
- 2For each URL, Firecrawl scrapes the transcript to clean text.
- 3OpenAI builds a per-ticker scorecard of guidance, actuals, and net beat/miss tally.
- 4A logic step ranks tickers by aggregate variance against guidance.
- 5OpenAI synthesizes a comparative narrative highlighting outperformers and laggards.
- 6The ranked roundup is published to a Notion page for the finance lead.
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 this workflow in your colony.
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