ResearchMind — AI Research Agent | Full-stack agentic AI system that autonomously decides whether to search uploaded PDFs or the live web to answer questions. Built with LangChain tool-calling agents, FAISS vector search, Tavily real-time web search, FastAPI, and React. Every answer includes page citations and clickable URLs. Dockerized with Docker Compose, backend on Railway, frontend on Vercel. Live at researchmind-sigma.vercel.app
The abstract linework gives the avatar a distinct character without leaning on a face—especially like how the data labels make the concept feel functional.
Another productive day — this time showcasing my Hand Gesture Mouse Control project. A Python-based computer vision project that lets you control your computer mouse using hand gestures in real time.
👆 Index finger → Move the cursor
👌 Pinch index + middle finger → Click
Built...
AI Lead Qualification & Appointment Automation System
AI Lead Conversion Platform — Telegram, CRM & Automated Scheduling
Designed and developed an AI-powered lead conversion system that handles incoming Telegram conversations from first contact to qualification, CRM synchronization, and appointment booking.
The AI works with conversation history, lead profile data, unanswered questions, current funnel stage, available actions, and verified CRM data to determine the next appropriate step and generate structured responses.
The system automatically creates and updates leads in the CRM, manages qualification stages, books meetings, generates Yandex Telemost links, sends reminders, handles rescheduling, and escalates exceptional cases to a human manager.
The architecture includes dialogue state management, message deduplication and batching, PostgreSQL persistence, concurrency protection, validation before actions are executed, and safeguards against sending unverified or prohibited information.
This is not just an AI chatbot — it is an automated sales workflow that connects Telegram, AI, CRM, scheduling, and human escalation into one controlled process.
AI makes decisions within controlled business rules instead of operating as an unrestricted chatbot.
The concurrency safeguards and validation-before-actions detail is a strong distinction here—booking systems usually fail at the edges, not in the happy path. I’d expose a compact lead state to the operator (stage, next action, last unanswered question) so a human can resume...