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Scopio – Multi-Agent AI Sales Automation Platform
Scopio is a production-ready multi-agent AI platform designed to automate B2B sales and lead generation.
The platform coordinates multiple AI agents to perform end-to-end sales workflows:
Discover potential businesses from multiple sources
Research company websites and public information
Analyze business context using LLMs
Generate personalized outreach emails
Manage follow-up workflows automatically
Maintain conversation history and workflow state
I designed the agent architecture using LangGraph, enabling planning, tool calling, memory, and multi-step execution while integrating external APIs and backend services for reliable automation.
Key Features
Multi-Agent Architecture
Autonomous Business Research
AI-Powered Personalized Outreach
Tool Calling & API Integration
Workflow Orchestration
Long-Term Memory
PostgreSQL & Redis Integration
Dockerized Deployment
FastAPI Backend
Tech Stack
Python • LangGraph • LangChain • FastAPI • PostgreSQL • Redis • Docker • OpenAI • MCP • REST APIs
The network for creativity
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Deepen | Contra & Bubble.io Challenge
Say goodbye to boring small talk! 👋
Here is Deepen — a mobile card game for deeper connections, built completely on @Bubble.io . I created dynamic question decks and smooth workflows to make hangouts with friends effortless and fun.
https://www.youtube.com/watch?v=dAyuz8q9V6c
#BubbleFFA #NoCode #Bubble #Contra
Amazing, wish you win this challenge.
A production-ready LangGraph agent that discovers, audits, scores, enriches, and qualifies B2B prospects automatically.
I recently built this workflow in n8n to automate the first stage of handling customer support requests.
The idea was to take an incoming ticket and let the system handle the repetitive work:
→ Receive the customer request through a webhook
→ Process and structure the incoming data
→ Use an AI Agent to understand the ticket
→ Classify it as Genuine, Spam, or Unclear
→ Route it based on the classification
→ Trigger the appropriate email action
→ Log the interaction in Google Sheets
I also added an Unclear path so that tickets the AI isn’t confident about don’t get blindly processed.
The goal isn’t to remove humans from customer support.
It’s to reduce the repetitive sorting and routing work, so the team can spend more time on the requests that actually need their attention.
Would you automate customer support differently? I’d love to hear how you’d approach it.
The Unclear lane is exactly the right guardrail. I’d add two production checks: redact PII before sending ticket text to the model, and measure false-positive/false-negative rates by category rather than a single accuracy score. Logging resolutions and human overrides back into...