AI AGENTS
Competitor Pricing-to-Notion Living Tracker
On a schedule, scrapes competitor pricing, structures it with AI, and keeps a Notion database in sync as the single source of truth.
How it runs
The automated pipeline, trigger to output.
- TriggerDaily schedule
- ActionScrape competitor pricing pages (Firecrawl)Firecrawl
- ActionNormalize into structured records (OpenAI)OpenAI
- LogicMatch rows: new / changed / unchanged
- ActionUpsert records into Notion databaseNotion
- OutputAppend dated changelog entries in NotionNotion
What it does
This agent maintains a living competitive-pricing tracker in Notion. On each run it re-scrapes every competitor, normalizes the data with AI, and updates the matching Notion rows in place so the board always reflects current market pricing, while appending a dated changelog entry whenever something moves.
When to use it
Use it when your team lives in Notion and wants a always-current pricing board they can browse, filter, and reference in strategy docs — rather than a one-shot Slack message that scrolls away. Great for PMMs maintaining a battlecard source of truth.
How it works
A daily schedule starts the run. Firecrawl scrapes each tracked competitor's pricing page, and an OpenAI step converts the markdown into a consistent record (plan, price, billing period, notable features). A logic step matches each record to an existing Notion page by competitor + plan and decides whether it is new, changed, or unchanged. The agent upserts changed and new records into the Notion pricing database, then writes a separate changelog entry capturing the before/after for each modified plan so the history is auditable.
Set it up
What you configure once, before turning it on.
- 1Connect FirecrawlCrawl, scrape, structured extract.
- 2Connect OpenAIModels, embeddings, files.
- 3Connect NotionPages, databases, comments.
- 4Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 5Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 6Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
More AI Agents workflows
Custom Metrics Cardinality Spike Pager
A webhook from a Datadog monitor fires when custom-metric cardinality jumps; an agent pinpoints the offending metric and tag, estimates the added cost.
Sentry-to-Confluence Runbook Updater
When a Sentry issue is resolved, the agent finds the matching Confluence runbook page and proposes an inline update with the verified fix.
Stale Doc-PR Chaser for Runbook Gaps
On a daily schedule the agent finds runbook doc PRs that were opened from resolved incidents but never reviewed, summarizes what each one fixes.
Resolved Incident to Public Troubleshooting Doc
For customer-facing errors resolved in Sentry, the agent drafts a sanitized troubleshooting entry and opens a PR to your ReadMe documentation.
On-Call Runbook Gap Closer: Resolved Sentry Issues to Doc PRs
An agent reads each newly resolved Sentry issue, compares the actual fix against your existing runbook, and opens a GitHub PR adding the missing remediation steps.
Weekly On-Call Doc-Gap Digest
Each week the agent reviews every Sentry issue resolved in the last 7 days, ranks the ones whose runbook coverage is missing or thin.
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.