MARKET RESEARCH

Earnings-Call Theme Extraction into a Competitor Comparison Grid

Scrapes the latest earnings-call transcripts for your tracked competitor set, extracts recurring strategic themes with an LLM.

CategoryMarket Research
Enginesim
Difficultyintermediate
Triggerschedule
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerEarnings-season schedule fires
  • ActionRead tracked competitors + transcript URLsAirtableAirtable
  • ActionScrape each transcript to clean textFirecrawl
  • ActionExtract themes, quotes, sentiment per companyOpenAI
  • LogicNormalize to one row per company-theme
  • OutputUpsert rows into comparison gridAirtableAirtable

What it does

Keeps a living comparison grid of what your competitors are actually talking about on their earnings calls. For each company in a tracked list, it pulls the most recent transcript, distills the management commentary into a fixed set of strategic themes (pricing, AI, margins, headcount, guidance, churn, etc.), and lands one row per company-theme into an Airtable grid you can pivot and filter.

When to use it

Run it after each earnings season when you need a structured read on competitive positioning rather than reading ten transcripts by hand. Useful for product strategy, competitive intelligence, and board-prep decks.

How it works

  1. 1A schedule kicks off the run during earnings season.
  2. 2The flow reads the tracked competitor list and each company's transcript URL from an Airtable base.
  3. 3Firecrawl scrapes each transcript page into clean text.
  4. 4OpenAI extracts the named themes per company, returning a structured JSON array with a verbatim supporting quote and a sentiment tag for each theme.
  5. 5The flow normalizes results into one row per company-theme.
  6. 6Rows are upserted into the Airtable comparison grid, keyed on company plus quarter so re-runs update in place.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect AirtableBases, tables, views, automations.
  2. 2
    Connect FirecrawlCrawl, scrape, structured extract.
  3. 3
    Connect OpenAIModels, embeddings, files.
  4. 4
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  5. 5
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  6. 6
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

Run this workflow in your colony.

14-day trial. No DevOps. No Sales call. Provisioned in under a minute.