SUMMARIZATION

Earnings-call guidance-vs-actuals delta brief to Notion

Scrapes a freshly posted earnings-call transcript, extracts every forward guidance figure and actual result, and publishes a one-page delta brief to Notion flagging each beat.

CategorySummarization
Enginesim
Difficultyintermediate
Triggerschedule
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerScheduled run on earnings morning
  • ActionScrape transcript to clean textFirecrawl
  • ActionExtract guidance and actuals tableOpenAI
  • LogicClassify each metric beat / miss / inline
  • ActionDraft headline and variance narrativeOpenAI
  • OutputPublish dated delta brief to NotionNotionNotion

What it does

Turns a long earnings-call transcript into a tight finance-ready brief that pairs each prior-quarter guidance number against the actual reported result, labels it beat / miss / inline, and explains the variance in plain English. The output lands as a structured Notion page the finance lead can scan in two minutes.

When to use it

Run it the morning after a portfolio company, competitor, or your own company reports. Good for FP&A teams, IR analysts, and operators who track a watchlist and need the guidance-vs-actuals story without reading 12,000 words of transcript.

How it works

  1. 1A scheduled check fires on earnings morning.
  2. 2Firecrawl scrapes the published transcript URL and strips it to clean text.
  3. 3OpenAI extracts a structured table of metrics: prior guidance range, actual, and management commentary per line item.
  4. 4A logic step classifies each metric as beat, miss, or inline using the guidance midpoint.
  5. 5OpenAI drafts a headline takeaway plus a variance narrative for the flagged misses.
  6. 6The brief is written to Notion as a dated page under the earnings database.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect FirecrawlCrawl, scrape, structured extract.
  2. 2
    Connect OpenAIModels, embeddings, files.
  3. 3
    Connect NotionPages, databases, comments.
  4. 4
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  5. 5
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  6. 6
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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