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Built an intelligent Messenger automation system powered by RAG (Retrieval-Augmented Generation) using Pinecone Vector Database and n8n. This workflow automatically handles incoming Messenger messages, analyzes images sent by users, retrieves relevant context from a knowledge base, and generates accurate AI-powered responses in real time.
Key features:
Webhook-based Messenger integration for instant message handling
Image analysis and understanding using AI vision
RAG architecture with Pinecone Vector Store for context-aware responses
Text summarization and semantic search using OpenAI embeddings
Multi-agent system (RAG agent + Question-answering agent) for accurate, context-driven replies
Automated message routing based on content type (text/image)
Real-time response delivery back to Messenger users
Tech stack: n8n, OpenAI (GPT + Embeddings), Pinecone Vector Database, Messenger API, Webhooks
This automation eliminates manual customer support work by giving users instant, intelligent, and context-aware answers — even when they send images — making it ideal for businesses that want to scale their customer engagement without adding support staff.
The network for creativity
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Creatives on Contra have earned over $150M and we are just getting started
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Hey! I just shared a write-up on something I learned while rebuilding a product with AI agents:)
The main idea is pretty simple:
Don’t put all your product context in one giant agent file.
Keep the main file small, and put the real product knowledge inside structured docs that the AI can navigate when it needs them.
This has helped me a lot when working on a SaaS product with a lot of business logic!
Sharing the full breakdown here:
I'm offering consulting services to set this up! :)
The table-of-contents-in-the-main-file pattern is the key move here - it keeps the agent's default context small while still giving it a map to go fetch the architecture or naming-convention doc only when the task actually touches that domain.
The client needed a better way to manage and qualify new leads without doing everything manually. I built an AI-powered system that captures new leads, qualifies them, scores them, updates the CRM, and sends the right follow-up based on each lead. The client liked how the workflow reduced manual work and made it easier to see which leads needed attention.
Tools: n8n, OpenAI, Airtable, Gmail, Webhooks
This is awesome! And segregating the leads to deal differently with each segment is so important when it comes to outreach and lead gen systems! Great work.
AI SaaS isn’t just about adding ChatGPT to a product.
The real challenge is turning AI into a useful, reliable product that solves a real business problem.
A strong AI SaaS needs:
→ A clear problem and target user
→ Simple, intuitive UX
→ Well-designed AI workflows
→ Reliable APIs and backend infrastructure
→ Proper data handling and security
→ Fast responses and scalable architecture
→ A clear path from MVP to a production-ready product
The goal isn’t to make something look “AI-powered.”
The goal is to build a product where AI genuinely makes the user’s job faster, easier, or better.
That’s what separates an AI demo from a real AI SaaS product.
Well done !