AI AGENTS

Competitor Pricing Snapshot to Warehouse

A webhook-triggered agent that scrapes a competitor pricing page, extracts a structured snapshot with AI.

CategoryAI Agents
Enginesim
Difficultyadvanced
Triggerwebhook
Steps5
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerInbound webhook with competitor + URLHTTP webhook
  • ActionScrape pricing page (Firecrawl)Firecrawl
  • ActionExtract schema-aligned snapshot (OpenAI)OpenAI
  • LogicValidate against schema, reject malformed
  • OutputAppend time-series row to BigQueryGoogle BigQueryBigQuery

What it does

This agent captures point-in-time competitor pricing snapshots into your data warehouse so analysts can chart pricing trends over months. Each invocation scrapes one competitor, structures the result, and appends an immutable dated row to BigQuery — building a clean historical dataset no manual tracking can match.

When to use it

Use it when you want pricing history as analyzable data, not just alerts: feeding dashboards, correlating competitor moves with your win rates, or training internal models. The webhook trigger lets you fire it from any external system or on a custom cadence.

How it works

An incoming webhook carries the competitor identifier and pricing URL to capture. Firecrawl scrapes the page, and an OpenAI step extracts a flat, schema-aligned record (competitor, plan, price, currency, billing period, captured_at). A logic step validates the record against the expected schema and rejects malformed extractions so bad rows never reach the warehouse. The agent appends the validated snapshot to a BigQuery table as a new time-series row, leaving prior snapshots intact for longitudinal analysis.

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 BigQueryDatasets, queries, schemas.
  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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