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Cover image for Project Title:- Custom Enterprise RAG
Project Title:- Custom Enterprise RAG Assistant | Chat with Documents & PDFs Project Overview: DocuMind is an enterprise-grade Retrieval-Augmented Generation (RAG) assistant designed to eliminate manual data extraction and prevent AI hallucinations. It converts static corporate files (PDFs, reports, resumes, contracts) into an interactive, grounded knowledge engine that provides answers backed by direct page-level citations. The Problem Solved: * Manual Data Hunting: Eliminates hours spent reading through dense, complex documentation. AI Hallucinations: Constrains LLM outputs strictly to uploaded context, ensuring reliable, factual data. Lack of Auditability: Provides exact file names and page references for compliance and verification. Key Technical Features: * Dynamic Indexing: Fast chunking and local vector embedding using Hugging Face models (all-MiniLM-L6-v2). High-Accuracy Vector Search: ChromaDB integration for persistent vector storage and low-latency similarity retrieval. Decoupled Architecture: Asynchronous FastAPI backend paired with a clean, responsive Streamlit chat frontend. Contextual Synthesis: Powered by Google Gemini (gemini-2.5-flash) for cost-effective inference. Tech Stack: Python, FastAPI, LangChain, ChromaDB, Hugging Face, Google Gemini, Streamlit.
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Cover image for LookInsight AI : AI Customer Support Automation System Full-...
LookInsight AI : AI Customer Support Automation System Full-stack AI system that automates customer support workflows. What it does: → Classifies customer intent (complaint, inquiry, refund, support) → Detects urgency and routes to the right team → Looks up customer history via knowledge graph → Generates personalized response drafts in seconds Key features: • Real-time processing pipeline with live visualization • Multi-channel support (Email, Twitter, Slack, WhatsApp) • Business rules engine for tier-based treatment (VIP, Premium, Regular) • Neo4j knowledge graph for customer context Tech stack: Python, FastAPI, OpenAI GPT-4, Neo4j, Next.js, TypeScript Live demo: https://lookinsight.ai Skills: Python AI OpenAI FastAPI Next.js Customer Support Automation Link: https://lookinsight.ai
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Cover image for Most sales teams don't struggle
Most sales teams don't struggle because of a lack of leads. They struggle because finding, qualifying, researching, and reaching out to prospects is still largely manual. HunterOS is an AI-powered sales prospecting and outreach platform designed to automate the top of the sales funnel. It continuously identifies potential customers, gathers relevant business intelligence, qualifies opportunities, and prepares personalized outreach at scale. Instead of spending hours searching for prospects, sales teams receive a pipeline of high-potential opportunities ready for engagement. The result: ✓ Faster prospect discovery ✓ Reduced manual research ✓ Higher sales productivity ✓ Consistent pipeline generation ✓ More time spent closing deals HunterOS transforms prospecting from a repetitive task into an intelligent, automated process. #SalesAutomation #LeadGeneration #AIAutomation #BusinessGrowth #SalesOps #HunterOS
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Cover image for AI-Powered E-Waste Management Platform: Sustainable Tech Sol...
Project Goal: Designed and developed a full-stack, AI-powered e-waste management platform to promote responsible electronic waste disposal while enabling value recovery, recycling discovery, and convenient pickup services. • Built RESTful backend APIs using FastAPI / Node.js for authentication, classification workflows, facility management, pickup scheduling, and marketplace operations • Integrated an AI image classification model to categorize e-waste into recyclable, reusable, or hazardous types • Implemented a facility locator with map integration to identify nearby certified recycling centers • Developed a rule-based value estimation engine for resale and scrap pricing • Created a slot-based pickup scheduling system with confirmation flow • Integrated Razorpay payment gateway for secure online transactions
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Cover image for 𝗛𝗲𝗮𝗱𝗹𝗶𝗻𝗲: Most Voice AI agents
𝗛𝗲𝗮𝗱𝗹𝗶𝗻𝗲: Most Voice AI agents still feel like glorified walkie-talkies. So I built one that actually listens like a human. Most voice bots today follow a rigid, linear loop: You speak ➔ Wait ➔ Bot speaks. If you dare interrupt them mid-sentence? They either completely ignore you or their context buffer gets totally mangled. 𝗧𝗼 𝘀𝗼𝗹𝘃𝗲 𝘁𝗵𝗶𝘀, I engineered Astra Duplex Agent — a sub-50ms, ultra-low latency, full-duplex conversational Voice AI built to handle natural human interruptions seamlessly. Here is what went into building the architecture under the hood: True Full-Duplex WebSockets: Built on FastAPI, allowing real-time bi-directional audio streaming instead of turn-based HTTP requests. Edge Voice Activity Detection (VAD): Deployed Silero VAD using ONNX Runtime to catch user barge-ins at the edge with near-zero overhead. LangGraph State Machine: Instead of a simple monolithic script, the conversation logic is modeled as a state graph, giving precise control over execution flow. Partial State Tracking & Memory Persistence: This was the trickiest part. When an interruption happens, Astra doesn't just stop audio playback — an async kill-switch halts Groq's LLaMA-3.1 mid-token, calculates the exact partial sentence actually spoken by the TTS, and saves only that partial context to an Upstash Redis buffer. Sub-50ms Inference & Streaming: Leveraged Groq LPU (LLaMA-3.1 + Whisper Large v3) paired with ElevenLabs Turbo v2.5 streaming. The result? An agent that you can interrupt mid-thought, change topics with on the fly, and ask follow-up questions without it losing historical context. Containerized with Docker and live on Render & Vercel. Check out the full architecture & live demo below! https://lnkd.in/eC6yak3i (https://lnkd.in/eC6yak3i)https://lnkd.in/eN38HGZD (https://lnkd.in/eN38HGZD)#VoiceAI (https://www.linkedin.com/search/results/all/?keywords=%23voiceai&origin=HASH_TAG_FROM_FEED) #GenerativeAI (https://www.linkedin.com/search/results/all/?keywords=%23generativeai&origin=HASH_TAG_FROM_FEED) #LangGraph (https://www.linkedin.com/search/results/all/?keywords=%23langgraph&origin=HASH_TAG_FROM_FEED) #SystemDesign (https://www.linkedin.com/search/results/all/?keywords=%23systemdesign&origin=HASH_TAG_FROM_FEED) #FastAPI (https://www.linkedin.com/search/results/all/?keywords=%23fastapi&origin=HASH_TAG_FROM_FEED) #MachineLearning (https://www.linkedin.com/search/results/all/?keywords=%23machinelearning&origin=HASH_TAG_FROM_FEED) #WebSockets (https://www.linkedin.com/search/results/all/?keywords=%23websockets&origin=HASH_TAG_FROM_FEED) #Python (https://www.linkedin.com/search/results/all/?keywords=%23python&origin=HASH_TAG_FROM_FEED) #AIENGINEERING (https://www.linkedin.com/search/results/all/?keywords=%23aiengineering&origin=HASH_TAG_FROM_FEED)
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