MARKET RESEARCH

Flag New & Changed 10-Q Risk Factors with Email Alerts

On each new competitor 10-Q, extract the risk-factor section, detect language added or removed versus the prior filing, log it to Notion.

CategoryMarket Research
Enginesim
Difficultyadvanced
Triggerschedule
Steps6
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerScheduled check for new 10-Q filings
  • ActionFetch filing and isolate risk-factors sectionFirecrawl
  • ActionClassify risk items as added, removed, or reworded vs priorOpenAI
  • ActionLog all risk changes to Notion risk-watch databaseNotionNotion
  • LogicDetect newly added material risks
  • OutputEmail alert to the watch listGmailGmail

What it does

Focuses narrowly on the risk-factors section of competitor 10-Qs — the place where companies quietly disclose new threats. It compares each new filing's risk language to the last one, records every addition and removal in a Notion risk-watch database, and sends a targeted email when a genuinely new material risk shows up.

When to use it

Use it when emerging-risk disclosure is your early-warning signal — regulatory exposure, supply concentration, litigation, or new competitive threats a rival is forced to name. Suited to risk, legal, and strategy teams who want to be told only when something new appears.

How it works

  1. 1A scheduled check detects new 10-Q filings for tracked tickers.
  2. 2Firecrawl fetches the filing and the workflow isolates the risk-factors section.
  3. 3OpenAI compares it against the prior filing's risk language and classifies each item as added, removed, or reworded.
  4. 4Every change is logged as a row in the Notion risk-watch database.
  5. 5A logic gate checks for newly added material risks; when found, an email alert goes out to the watch list.

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
    Connect GmailRead, draft, send, label.
  5. 5
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  6. 6
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  7. 7
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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