Farhan Khan
AI Specilist, AI Automation, Chatbots, Business, Workflow
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E-Assistant is an autonomous AI shopping agent designed to streamline the consumer decision-making process. By simultaneously querying multiple e-commerce platforms, it utilizes a proprietary value-ranking algorithm to provide real-time product comparisons based on price, rating, and review volume.
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Automated Video Dehazing & Atmospheric Haze Simulation System
š Project Overview
This advanced Computer Vision project is designed to address visibility challenges in adverse weather conditions. The system features a dual-module architecture: it can synthetically inject realistic atmospheric fog/haze into crystal-clear video streams for dataset generation, and conversely, restore heavily degraded, foggy videos into crisp, high-visibility outputs in real-time.
š ļø Core Functionality & Modules
Module 1: Atmospheric Haze Simulation
Purpose: Generates synthetic datasets to train and benchmark object detection models (like YOLO) for bad weather conditions.
How it works: Implements mathematical scattering models to calculate depth maps and overlay a realistic layer of dense fog or smoke over clean video frames.
Module 2: Real-Time Video Dehazing
Purpose: Restores clarity and vivid color to video streams captured in low-visibility environments.
How it works: Leverages physics-based Computer Vision algorithms (such as Dark Channel Prior - DCP) or Deep Learning frameworks to estimate atmospheric light, eliminate transmission noise, and reconstruct the scene's original contrast.
šÆ Use Cases & Applications
Autonomous Vehicles: Enhances the sight and reliability of self-driving car sensors in dense fog.
Smart Surveillance (CCTV): Improves security monitoring and facial recognition accuracy under harsh outdoor weather.
Drone Navigation: Aids aerial drones in safely navigating through smoke, dust storms, or low-lying clouds.
š» Tech Stack Used
Language: Python
Libraries: OpenCV, NumPy, Matplotlib, PyTorch / TensorFlow (if deep learning was applied)
Concepts: Image Processing, Atmospheric Scattering Models, Feature Restoration, Video Pipeline Optimization
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Automated Multi-Agent AI Support & Lead Triage Pipeline
Are your high-ticket clients waiting hours for an email response? This intelligent multi-agent n8n workflow instantly screens, analyzes, and responds to customer emails in real-time, utilizing advanced RAG (Retrieval-Augmented Generation) to deliver human-like support instantly.
Project Overview:
This is an enterprise-grade AI automation system designed to eliminate manual customer support queues. Instead of simple auto-replies, it uses a multi-agent routing structure combined with a dynamic knowledge base to handle complex inquiries autonomously.
How It Works (Under the Hood):
Instant Inbound Triage: A Gmail Trigger catches incoming emails instantly, extracting raw content for processing.
AI Intent Classification: An initial OpenAI model acts as a gatekeeper, analyzing the email to determine if it is a valid customer support request or irrelevant noise.
Conditional Routing: An advanced router splits the path: non-support emails receive a polite automated Telegram update, while actual support tickets are routed to the main AI engine.
Context-Aware AI Agent: The core Customer Support Agent is equipped with an OpenAI Chat Model, conversational memory, and a custom Vector Store Tool.
Pinecone RAG Integration: The agent queries a Pinecone Vector Database (powered by OpenAI Text Embeddings) to fetch real-time, accurate company documentation and context, eliminating hallucinations.
Automated Action & Response: Once the resolution is drafted, the system automatically creates a draft in Gmail for review and sends an instant internal notification via Telegram.
Why This Wins Clients (The Value Pitch):
Zero Hallucinations: Connected to a live vector database (Pinecone) so the AI only speaks from approved company data.
Reduced Overhead: Cuts down customer support response times from hours to under 60 seconds.
Production-Ready Architecture: Designed with modern n8n AI sub-nodes, structured tools, and modular scaling capabilities.
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Real Time SMS Spam Detection/Classification System
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