Run fast competitor research from one place. Teams get profiles, product info, reviews, and news for each rival and store it in a Notion database for easy reading and sharing.

The flow starts with a manual run. You set the source company. A search step finds similar companies and cleans the list with remove duplicates and domain extraction. The loop handles one company at a time to prevent failures from stopping the whole run. Three research tracks run in parallel. One gathers company details and news by scraping trusted profiles and search results. Another finds product pages on the company site and extracts key features. The third finds review sites, scrapes pages, and summarizes pros and cons. OpenAI models turn raw text into clear fields using a structured parser. Results are merged and added to Notion as a single row per company.

You will need API keys for Notion, OpenAI, SerpAPI, Firecrawl, and Exa. Set your Notion database and map fields. Pick how many competitors to process and add a short wait if you hit rate limits. Expect hours of manual work to drop to minutes with consistent output. Great for market scans, pitch prep, and product gap checks.

• On demand run with a manual trigger to control when research starts
• Similar company discovery and deduping with domain extraction to keep a clean list
• Batch loop processes one competitor at a time with a limit and a short wait to avoid rate limits
• Company overview path scrapes trusted profiles and fetches recent news
• Product path finds key site pages and extracts features and messaging
• Reviews path locates review sites, scrapes content, and summarizes pros and cons
• OpenAI models with low temperature for consistent summaries
• Structured output parser converts model text to clear fields ready for a database
• Notion insert creates one row per competitor with mapped properties

• Reduce manual research from 6 hours to 20 minutes per competitor
• Automate about 80 percent of data collection across sources
• Improve data completeness by combining five research inputs
• Handle up to 5 times more competitors each week with the same team
• Lower errors with structured outputs mapped directly to Notion fields

1. Import the template into n8n: Create a new workflow in n8n > Click the three dots menu > Select 'Import from File' > Choose the downloaded JSON file.
2. You'll need accounts with Notion, OpenAI, SerpAPI, Firecrawl and Exa. See the Tools Required section above for links to create accounts with these services.
3. Open the Insert Into Notion node, select your Notion credential, or click Create new credential and follow the on screen steps. In Notion, share your database with the integration and paste the database ID if asked.
4. Open the OpenAI Chat Model nodes, choose your OpenAI credential, or create a new one by pasting your API key from the OpenAI dashboard.
5. Open each SerpAPI node and select or create a SerpAPI credential using your API key from your SerpAPI account page.
6. Open each Firecrawl scraper node and add or select your Firecrawl API key under the credential dropdown.
7. Open the competitor search node and set or create the Exa credential with your Exa API key. Confirm the query field uses your source company name.
8. Double click the Set Source Company node and type the company you want to research. Save the node.
9. Open the Insert Into Notion node and map each output field to your Notion properties. Make sure property names and types match, then save.
10. Adjust the Limit node if you only want to test with a few competitors. Keep the Wait step enabled if you see rate limit errors.
11. Click Execute Workflow to run a test. Watch the loop and confirm that each competitor creates a row in your Notion database.
12. If scrapers return empty results, check that your API keys are active and that the target pages are public. If you see 403 or 429 errors, increase the wait time or lower the limit.
13. If Notion rows are missing fields, revisit the Structured Output Parser and Notion field mapping to ensure property names align.