MARKET RESEARCH

Strategic-Bet Brief from Competitor Hiring Clusters

An agent pulls a competitor's recent postings, clusters them by function and seniority, infers the implied strategic bet.

CategoryMarket Research
Enginepaperclip
Difficultyadvanced
Triggermanual
Steps6
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerOperator runs with competitor name
  • ActionScrape competitor job boardApify
  • ActionEnrich with recent web contextExa
  • LogicCluster and infer strategic bets with citations
  • LogicValidate every claim cites a posting
  • OutputWrite sourced brief to NotionNotionNotion

What it does

Given a competitor name, this workflow assembles their current hiring footprint and reasons about what it implies. Instead of a raw list of jobs, it produces a written brief: "Heavy hiring in field sales + solutions engineering across three new metros suggests an enterprise land-and-expand push." Every inference is tied back to the specific postings that support it.

When to use it

Use it when you need a defensible, narrative read on a single competitor ahead of a board update, a pricing decision, or a positioning review. It trades the speed of an alert for depth and reasoning.

How it works

  1. 1An operator triggers the run with a competitor name.
  2. 2Apify scrapes that competitor's job board for active postings.
  3. 3Exa enriches with recent web context (funding, launches, leadership moves) to ground the analysis.
  4. 4The agent clusters postings by function, seniority, and location, then drafts an inferred strategic bet for each cluster with the supporting postings cited.
  5. 5A logic step checks the brief cites at least one posting per claim and flags thin sections.
  6. 6The finished brief is written to Notion, tagged by competitor and date.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect ApifyActors, scrapers, datasets.
  2. 2
    Connect ExaNeural search across the web.
  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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