MARKET RESEARCH

Weekly multi-competitor earnings digest compiled into a Coda dashboard

An agent works through every competitor that reported in the past week, scrapes each transcript, extracts and cross-compares strategy signals.

CategoryMarket Research
Enginepaperclip
Difficultyadvanced
Triggerschedule
Steps5
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerWeekly schedule starts the digest agent
  • ActionScrape each reporting competitor's transcriptFirecrawl
  • ActionExtract per-competitor signalsOpenAI
  • LogicCluster cross-competitor themes and rank
  • OutputPublish weekly digest to Coda dashboardCodaCoda

What it does

Instead of analyzing one call at a time, this agent sweeps every competitor that reported in the last week, reads each transcript, and synthesizes across them — surfacing shared industry themes (e.g. everyone is pivoting to AI pricing) alongside per-competitor signals. The output is one ranked weekly digest, not scattered rows.

When to use it

Use it during earnings season when several rivals report in the same week and you need the connective tissue: what's an industry-wide shift versus a single company's bet. Built for strategy leads who want one Monday-morning read.

How it works

  1. 1A weekly schedule kicks off the agent.
  2. 2The agent identifies which tracked competitors reported in the past week and scrapes each transcript with Firecrawl.
  3. 3For each transcript, OpenAI extracts per-competitor strategy signals.
  4. 4The agent reasons across all signals to cluster cross-competitor themes and rank them by significance.
  5. 5It compiles a structured digest with an executive summary, theme clusters, and per-competitor sections.
  6. 6The digest is published to a Coda dashboard page for the week.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect FirecrawlCrawl, scrape, structured extract.
  2. 2
    Connect OpenAIModels, embeddings, files.
  3. 3
    Connect CodaDocs, packs, automations.
  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.

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