MARKET RESEARCH

Infer a competitor's tech stack from engineering job descriptions

On demand, scrapes a target competitor's engineering postings, extracts the named technologies, and produces a Notion report inferring their stack, platform direction…

CategoryMarket Research
Enginesim
Difficultyadvanced
Triggermanual
Steps5
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerManual run with target URL
  • ActionScrape engineering role descriptionsFirecrawl
  • ActionExtract and cluster named technologiesOpenAI
  • LogicWeight by recurrence to find core stack
  • OutputPublish inferred stack report to NotionNotionNotion

What it does

Reverse-engineers a competitor's technology direction from what they ask candidates to know. It pulls their engineering job descriptions, extracts every named language, framework, database, and cloud tool, then synthesizes the evidence into a structured stack profile with confidence notes.

When to use it

When you are sizing up a single competitor before a deal, a pitch, or a strategy review and need to understand their architecture bets: which cloud, which data platform, whether they are migrating, and where they build versus buy. Run it ad hoc against one named target.

How it works

  1. 1A manual trigger supplies the target competitor's careers URL.
  2. 2Firecrawl scrapes all engineering and infrastructure role descriptions.
  3. 3OpenAI extracts named technologies and clusters them into stack layers (frontend, backend, data, infra, ML).
  4. 4A logic step weights each technology by how often it recurs to separate core stack from incidental mentions.
  5. 5Notion receives a formatted report: inferred stack by layer, likely platform direction, and build-versus-buy signals with the supporting postings cited.

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.

Run this workflow in your colony.

14-day trial. No DevOps. No Sales call. Provisioned in under a minute.