The Challenge

Sales and outreach teams spend significant time browsing LinkedIn manually — reading posts, judging whether a contact's activity signals buying intent, then switching to a CRM to log notes and draft a message. Done at scale, this workflow makes consistent prospecting nearly impossible. Most opportunities get missed simply because there aren't enough hours to monitor the feed.

Specific manual steps that needed automating:

• Scrolling through LinkedIn feeds to find posts that signal outreach opportunity
• Manually evaluating each contact's activity against prospect criteria
• Copying contact details and notes into the CRM one entry at a time
• Writing personalized connection requests or comments from scratch for each prospect

Our Solution

We built a Chrome extension that turns passive LinkedIn browsing into an automated prospecting workflow — running every 10 minutes to reload the feed page to load fresh posts.

• AI-powered opportunity detection: The extension reads LinkedIn posts and profiles as they load, using AI to evaluate whether each contact's recent activity matches the client's prospect criteria — and flags matches immediately.
• Automatic CRM logging: When a matching prospect is identified, the extension adds the contact automatically to the CRM via API, along with context notes pulled from their LinkedIn activity. No manual copy-paste required.
• Personalized outreach drafting: For each flagged prospect, the extension generates a personalized connection request or comment based on the specific post or activity that triggered the match.

Technologies Used

• JavaScript (Chrome Extension API)
• NodeJS
• CRM API Integration
• LinkedIn DOM Parsing

Results

• Automated end-to-end prospecting — qualifying, logging, and drafting outreach now happens passively while the user browses, with no separate workflow
• 4–5 weeks to build: Chrome extension (~3 weeks) and CRM backend integration (~2 weeks)
• Consistent pipeline activity regardless of how much time the team can dedicate to manual LinkedIn monitoring

‍