This setup handles incoming WhatsApp messages end-to-end: - Detects intent automatically - Retrieves answers from a structured knowledge base - Updates/searches records dynamically - Sends fast, context-aware responses without manual intervention Biggest learning: Separating intent detection from retrieval logic significantly improved response accuracy and reduced hallucinations. Sharing this to get feedback from the community. Happy to walk through the flow, explain design choices, or iterate based on suggestions Let me know what you’d optimize or do differently. 👇

🚀 We have opened up Premium & VIP Tiers here in the community if you guys would like to expedite your growth and accelerate your projects. Click the link in the community bio or here: https://www.skool.com/cc-strategic-ai/plans Check out the Premium and VIP tiers. I think VIP is a great option for those of you looking to get an idea off the ground the quickest with direct 1:1 access with Chris, the CC Strategic Team, and I! No pressure to upgrade, but this will support our community, and also allow us to pour into you more intimately. Also if you haven't already, join the discord, link's in the community bio.

Been at this all day, and it's almost done. Excited to launch and share with you guys. Should be a HUGE addition to the Claude Section of the Classroom!

I recently developed AntiCollab, an AI-powered system designed to streamline the management of sponsorship emails for creators and founders. As inbox volumes increase, sponsorship requests often become difficult to identify and manage. This solution automates the classification and organization of incoming emails, significantly reducing manual effort while improving visibility and response efficiency. The system enables automated detection of sponsorship-related emails, structured data storage with real-time synchronization, and a centralized dashboard featuring message previews, one-click archive and unarchive functionality, and light and dark mode support. It is built using Gmail integration, n8n for workflow orchestration, OpenAI for intent-based classification, and Supabase for data storage, alongside a custom frontend developed with Next.js, TypeScript, and Tailwind CSS. Claude AI was utilized to assist with end-to-end UI/UX development. Planned enhancements include the integration of Retrieval-Augmented Generation (RAG) to leverage historical collaboration data and brand insights, enabling AI-assisted evaluation of partnership fit and response strategy. This project is shared as part of building in public, and feedback or discussion is welcome. 🎥 Product Walkthrough:https://www.loom.com/share/70ea25f4b2cb4af385445fe68658fa12

Join here: https://us06web.zoom.us/j/7834933051?pwd=Tfl5U1b171HuIDOFG1YCjYc7CenYag.1 Long story short, I'm sitting here live building a new app for one of my clients. If you want to hop in and watch/work together as I'm building be my guest. Recorded Call: https://app.read.ai/analytics/meetings/01KEWEBXB004YCN94FA7H8QE5Y?utm_source=Share_CopyLink