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An AI system that detects emotions from voice in real time, using Wav2Vec2 and a custom PyTorch classifier. Useful for applications like call centers, where understanding customer sentiment from speech alone can help teams respond better and flag issues early
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🚀 Continuing my backend development journey.
Recently I've been focusing on building REST APIs with Python, Django REST Framework, PostgreSQL, Docker, Git, and Linux.
Every project helps me improve API design, database modeling, clean architecture, and maintainable backend code.
I'm continuously learning and looking forward to new backend development opportunities.
#Python #Django #BackendDevelopment
Echo is a multi-tenant SaaS for AI customer support with realtime chat, an AI voice agent, and AI automation. I was the primary developer on the full build, frontend, backend, and the AI layer. The AI is the core. It runs OpenAI, Claude, Gemini, and Grok through one multi-model setup, so a client can switch providers without a rewrite. A RAG pipeline connected to a vector database grounds every answer in the client's own content, so the chatbot never returns generic output. VAPI powers the voice agent, so customers can speak to support on a live call. Each tenant gets its own AI agent built on its own documents. The stack is Next.js 15 and React 19 inside a Turborepo monorepo, with separate apps for the dashboard, the embeddable chat widget, and backend services. Realtime chat runs on Convex. Clerk handles auth. API keys are encrypted per tenant through AWS Secrets Manager, so no two clients share credentials or data. Launch day held 60 live conversations at once with zero dropped sessions. What the client gets: an AI chatbot and voice agent that answer from their own content, work across multiple LLM providers, and stay isolated and secure per tenant.:
Perfect
Meet AI is a video conferencing SaaS where AI agents join live meetings as active participants and respond in real time. I built the full application stack.
The hard part is the real-time layer. The backend provisions an AI agent the moment a meeting starts and keeps it running for the full call. Speech runs through a transcript pipeline and generates responses through the OpenAI API in near real time, so the agent replies while the conversation is still moving, not minutes later. After the meeting, the backend writes a structured summary through the same API, handled async by Inngest so nothing blocks the live app.
The stack is Next.js 15 and React 19 with the Stream Video and Stream Chat SDKs, tRPC and Drizzle ORM on Neon PostgreSQL, and Better Auth across the stack.
The platform runs 30 simultaneous live sessions, each with its own active AI agent, with no backend slowdown.
What the client gets: live AI agents that take part in real meetings, plus automatic summaries, on a stack built to hold many sessions at once.
Perfect
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