MARKET RESEARCH
Earnings Theme Warehouse Loader
Scrapes new competitor earnings transcripts, extracts structured themes and sentiment scores, and loads them into Snowflake as time-series rows for longitudinal analysis.
How it runs
The automated pipeline, trigger to output.
- TriggerScheduled run checks for newly reported competitors
- ActionLocate transcript URLs for new reportsExa
- ActionExtract transcript textFirecrawl
- ActionConvert transcript to normalized theme JSONOpenAI
- LogicValidate schema and dedupe already-loaded periods
- OutputInsert rows into Snowflake time-series tableSnowflake
What it does
It turns unstructured earnings-call transcripts into clean, queryable rows in Snowflake. Each run extracts the themes, their sentiment, and supporting metrics from new transcripts, then appends them to a warehouse table keyed by ticker and fiscal period so analysts can chart how a narrative trends over many quarters.
When to use it
Use it when you want earnings themes as data, not prose — feeding a BI dashboard, building a sentiment time series, or joining call themes against price and guidance history.
How it works
A schedule kicks off the run. Exa locates transcript URLs for the tracked competitors that have reported since the last run, and Firecrawl extracts the text. OpenAI converts each transcript into a normalized JSON record: theme labels, sentiment (-1 to 1), confidence, and representative quotes. A logic step validates the schema and skips anything already loaded, then the records are inserted into a Snowflake table with ticker, quarter, and run timestamp for clean longitudinal queries.
Set it up
What you configure once, before turning it on.
- 1Connect ExaNeural search across the web.
- 2Connect FirecrawlCrawl, scrape, structured extract.
- 3Connect OpenAIModels, embeddings, files.
- 4Connect SnowflakeWarehouses, queries, shares.
- 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.