MARKET RESEARCH
Tech-stack shift detector from job-description keywords
Mines target-account job descriptions for named technologies, tracks which tools appear and disappear over time.
How it runs
The automated pipeline, trigger to output.
- TriggerWeekly schedule fires
- ActionScrape full job descriptions per accountApify
- ActionExtract named technologies from JD textOpenAI
- LogicDiff tech footprint vs prior reading
- OutputLog footprint + shifts to AirtableAirtable
What it does
Reads the full text of an account's open job descriptions, extracts the named technologies and platforms, and compares the current tech footprint to the prior reading. When a new tool starts appearing in postings - or a former one drops off - the workflow flags the shift as a potential migration, displacement, or net-new spend opportunity.
When to use it
When you sell infrastructure, dev tools, or anything where a competitor's tool showing up in JDs is a buying signal. Useful for both new-logo prospecting and at-risk competitive watch on existing customers.
How it works
- 1A weekly schedule kicks off the run.
- 2Apify scrapes each account's postings, including the full job-description body.
- 3OpenAI extracts a normalized list of technologies mentioned per account.
- 4A logic step diffs the tech list against the stored prior reading to surface adopted and dropped tools.
- 5The current footprint and the detected shifts are written to Airtable as the account's tech-signal log.
Set it up
What you configure once, before turning it on.
- 1Connect AirtableBases, tables, views, automations.
- 2Connect ApifyActors, scrapers, datasets.
- 3Connect OpenAIModels, embeddings, files.
- 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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