MARKET RESEARCH
Tech-Stack Signal Extractor from Job Descriptions
On a schedule it scrapes competitor engineering job descriptions, extracts the named tools, frameworks, and cloud platforms.
How it runs
The automated pipeline, trigger to output.
- TriggerScheduled run
- ActionScrape engineering job descriptionsApify
- ActionExtract named technologiesOpenAI
- LogicDiff against existing stack matrix
- ActionUpdate tech-stack matrixAirtable
- OutputPost new-tool summaryDiscord
What it does
It scrapes competitors' engineering and data job descriptions, extracts every named technology - languages, frameworks, databases, cloud, ML tooling - and maintains an Airtable matrix of which stack each competitor is hiring for, including new tools appearing over time.
When to use it
Use this when a competitor's technical direction is the signal you care about. New mentions of a vector database hint at an AI feature; a shift to a new cloud or a Rust rewrite reveals architecture bets. Job descriptions are the most reliable public source for this.
How it works
- 1A schedule fires the run on your chosen cadence.
- 2Apify scrapes engineering and data postings across the competitor watchlist.
- 3An OpenAI step extracts a normalized list of technologies from each description.
- 4A logic step diffs extracted tools against the existing matrix to isolate newly appearing technologies.
- 5The Airtable stack matrix is updated, marking new entries with first-seen dates.
- 6A summary of newly detected tools is posted to Discord for the team.
Set it up
What you configure once, before turning it on.
- 1Connect ApifyActors, scrapers, datasets.
- 2Connect OpenAIModels, embeddings, files.
- 3Connect AirtableBases, tables, views, automations.
- 4Connect DiscordCommunity channels + voice + bots.
- 5Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 6Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 7Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
More Market Research workflows
Enrich Inbound Accounts with BigQuery Firmographics and Score Fit
When a new account row lands in Airtable, joins it against BigQuery public business datasets to attach firmographic attributes.
Blend BigQuery TAM with Live Competitor Signals into a Notion Brief
On demand, sizes a chosen segment from BigQuery public data, gathers current competitor signals via Brave Search, and synthesizes a one-page market brief into Notion.
Allocate Sales Territory TAM from BigQuery Geo Data to HubSpot
When triggered by a webhook, queries BigQuery public ZIP-level business data to compute TAM per sales territory.
Hiring Surge Detector with Slack Alert
Detects when a target account's open-role count jumps above its recent baseline and posts a ranked Slack alert to the GTM channel so reps can act on a company that is clearly…
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.
Weekly Hiring-Intel Briefing for GTM
An agent reviews the week's accumulated hiring signals across all target accounts, writes a narrative briefing that infers each account's likely initiatives.
Run it inside a business
This workflow drops into a full company template. Import the org, and this is one of the playbooks its agents run.
Run this workflow in your colony.
14-day trial. No DevOps. No Sales call. Provisioned in under a minute.