MARKET RESEARCH
Quarter-over-Quarter Guidance Tracker in Airtable
Each quarter, scrapes competitor earnings transcripts, extracts guidance figures into structured rows.
How it runs
The automated pipeline, trigger to output.
- TriggerQuarterly schedule starts extraction run
- ActionFirecrawl scrapes latest competitor transcriptsFirecrawl
- ActionOpenAI extracts guidance into strict numeric schemaOpenAI
- LogicDeduplicate against existing Airtable rows
- OutputAppend timestamped guidance rows to AirtableAirtable
What it does
Builds and maintains a structured database of competitor guidance over time. Every quarter it scrapes the new transcripts, extracts each numeric guidance item into typed fields, and appends a dated row per company per metric — turning unstructured calls into a queryable time series you can chart and filter.
When to use it
When you want durable history rather than a one-off brief: tracking how a rival's revenue or margin guidance has drifted across many quarters, feeding a dashboard, or backing analysis with a clean dataset instead of a pile of PDFs.
How it works
- 1A quarterly schedule starts the extraction run.
- 2Firecrawl scrapes each competitor's latest transcript.
- 3OpenAI extracts guidance items into a strict schema (metric, period, low, high, basis, direction).
- 4A logic step deduplicates against existing rows so the same call is never recorded twice.
- 5New guidance rows are appended to the Airtable base, timestamped and linked to the source transcript URL.
Set it up
What you configure once, before turning it on.
- 1Connect FirecrawlCrawl, scrape, structured extract.
- 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.
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.