AI Agent Designer Projects in IndiaAI Agent Designer Projects in India
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Cover image for Converc — Turn Website Visitors
Converc — Turn Website Visitors Into Live Conversations URL: https://converc.com One-liner Built a real-time browser-based calling platform from scratch — MVP live in 5 days. Project Overview Converc solves a problem that costs B2B companies revenue every day: high-intent visitors land on your site, can't get a human immediately, and leave. It embeds a call widget that connects visitors to sales reps in real time — no booking links, no forms, no friction. I architected and built the full product: a Next.js frontend, Supabase backend with Row Level Security, and WebRTC-powered peer-to-peer audio calling that works entirely in the browser with no native app required. The stack was chosen for performance, low latency, and the ability to ship fast without sacrificing production quality. What I Built WebRTC call engine with real-time signalling via Supabase Realtime Embeddable call widget (drop-in script for any website) Agent dashboard with live call status, queue management, and session history Webhook integration layer for CRM and Slack notifications on call events OAuth-based authentication with Google and dev/prod environment separation Supabase RLS policies enforcing strict data isolation between workspaces How I Shipped It The core MVP — widget, signalling, agent dashboard, and working calls — was shipped in 5 days using Cursor with Claude as the AI pair programmer. Post-MVP work covered Google Safe Browsing clearance, Slack App submission, analytics scoping, and hardening the auth and security model for production. Tech Stack Next.js · Supabase · PostgreSQL · WebRTC · Vercel · Tailwind CSS
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Cover image for We recently built an AI
We recently built an AI Customer Support Agent, and one thing surprised us... The chatbot wasn't the hardest part. Getting the AI to understand the business was. The quality of an AI assistant isn't determined by the model alone. It's determined by the information you give it. During development, we found that connecting the AI to the right documentation, FAQs, business rules, and workflows made a far bigger difference than switching between models. Once the AI had the right context, responses became more accurate, consistent, and genuinely helpful. It reinforced something we've been seeing across recent AI projects: Businesses don't need another chatbot. They need an AI assistant that actually understands their business. That's where the real value is. Curious to see how other teams are approaching AI knowledge management. What's been your biggest challenge when building AI products? #AI #ArtificialIntelligence #CustomerSupport #AIAgents #SaaS #ProductDevelopment #Automation #OpenAI #Claude #BusinessAI #TvaronTechnologies
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Cover image for I built something wild for
I built something wild for the Paper Challenge🚀 It turns any GitHub repo into a visual case study — instantly. No writing. No designing. Just drop a repo link… and the canvas builds itself. That’s CodeSage💯 Most devs have solid projects… but they’re buried in README files nobody reads. CodeSage changes that. It reads your actual code — architecture, stack, commits — and transforms it into a clean, dark, presentation-ready case study. The real unlock? Paper MCP. I connected my AI agent directly to the canvas. So instead of designing manually… the agent builds everything: • Sections • Layout • Insights • Structure All generated directly on the canvas. No drag. No drop. Just output. Pick any repo → get 3 analysis modes: *Each one gives → Complexity score → PR velocity → Language breakdown → Full Refactor Roadmap Try CodeSage: https://codesage-871524277866.us-central1.run.app/ Tech stack: Paper MCP · Gemini API · GitHub API · Antigravity
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