AI AGENTS

On-Demand Competitor Price Check Logged to Airtable

A webhook lets anyone request an instant price check for a competitor URL; the flow scrapes it, an agent normalizes the price and currency.

CategoryAI Agents
Enginesim
Difficultybeginner
Triggerwebhook
Steps5
Setup~5 min

How it runs

The automated pipeline, trigger to output.

  • TriggerWebhook receives competitor URLHTTP webhook
  • ActionFetch rendered page contentFirecrawl
  • ActionAgent extracts and normalizes priceOpenAI
  • LogicReject if no confident price found
  • OutputAppend row to Airtable trackerAirtableAirtable

What it does

Gives your team a one-call price lookup. Hit the webhook with a competitor product URL (from a button, a chat command, or a script) and the flow returns and records the current price. Firecrawl pulls the page, an agent extracts and normalizes the price, currency, and any promo flag, and the result lands as a new row in an Airtable history base you can chart over time.

When to use it

Use it for ad-hoc checks during deal negotiations or QBRs, and to build a clean, queryable price-history table without a heavy scraping stack. Pairs well with a Slack slash command or an internal tool that posts to the webhook.

How it works

  1. 1An inbound webhook delivers a competitor URL and an optional label.
  2. 2Firecrawl fetches and returns the rendered page content.
  3. 3An OpenAI agent extracts the price, currency, unit, and promo status into a clean structure.
  4. 4A logic check rejects the run if no confident price was found, returning an error.
  5. 5The normalized record is appended to the Airtable pricing-history table.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect HTTP webhookTrigger any URL on agent actions.
  2. 2
    Connect FirecrawlCrawl, scrape, structured extract.
  3. 3
    Connect OpenAIModels, embeddings, files.
  4. 4
    Connect AirtableBases, tables, views, automations.
  5. 5
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  6. 6
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  7. 7
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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