MARKET RESEARCH
Tech-Stack Shift Inference from Job Descriptions
Reads new job descriptions for target accounts, uses an LLM to extract named technologies and infer stack changes.
How it runs
The automated pipeline, trigger to output.
- TriggerDaily schedule fires
- ActionScrape job-description text with ApifyApify
- ActionExtract technologies with OpenAIOpenAI
- LogicDiff against stored stack and flag shifts
- OutputUpdate tech-stack profile in AirtableAirtable
What it does
Turns hiring text into competitive intelligence. It scrapes new job descriptions, has an LLM pull out the concrete tools, languages, and vendors mentioned, then compares the extracted stack to what was previously known about each account to surface additions and likely migrations.
When to use it
Use it when you sell into or against a technology stack and need to know what a prospect runs. A posting requiring Snowflake and dbt where they previously listed Redshift is a migration signal worth a tailored outreach. Ideal for sales engineering and competitive teams.
How it works
- 1A daily schedule fires the run.
- 2Apify scrapes full job-description text for newly posted roles.
- 3An OpenAI call extracts named technologies and categorizes them by layer.
- 4Diff the extracted tools against the account's stored stack profile.
- 5Branch: flag accounts with net-new vendors or competitor displacement.
- 6Write the updated stack profile and changes back to Airtable.
Set it up
What you configure once, before turning it on.
- 1Connect ApifyActors, scrapers, datasets.
- 2Connect OpenAIModels, embeddings, files.
- 3Connect AirtableBases, tables, views, automations.
- 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.
14-day trial. No DevOps. No Sales call. Provisioned in under a minute.