I can develop various object detection & recognition systems such as human face recognition, traffic symbol detection, multi-object detection, pedestrian detection, hand gesture detection, animal detection, and other similar applications. I am proficient in using various object detection & computer vision libraries/frameworks such as YOLO, OpenCV, TensorFlow, PyTorch, Detectron, and other frameworks.

I have extensive experience in implementing automatic document processing using top frameworks like Tesseract OCR, EasyOCR, Amazon Textract, Google Cloud Vision, Microsoft Azure OCR, PaddleOCR, OpenCV, and LayoutLM. My expertise allows me to build high-quality OCR-based solutions that can seamlessly integrate into existing websites or mobile applications. - I specialize in leveraging Tesseract OCR and EasyOCR for open-source and customizable document processing solutions. - For cloud-based scalability and advanced AI capabilities, I work with Amazon Textract, Google Cloud Vision, and Microsoft Azure OCR. - When dealing with complex document layouts and structured data extraction, I utilize PaddleOCR and LayoutLM for superior accuracy. - By combining OCR with OpenCV, I enhance pre-processing techniques like noise reduction, binarization, and text alignment for improved recognition. My solutions ensure high accuracy, efficiency, and automation, reducing manual effort in document processing workflows. Whether for invoices, medical records, legal documents, or forms, I can develop a robust OCR-powered system tailored to specific business needs.

I have developed multiple AI agents, including an AI Question-Answering Agent, an FAQ Answering Agent, an AI Caller Agent, and other intelligent systems like AI Chatbots, AI Recommendation Agents, and Medical Diagnosis Agents. With my extensive experience, I can build, fine-tune, and optimize these agents for various applications. To develop these AI agents, I have mastered key libraries and frameworks such as LangChain, LlamaIndex, FastAPI, Flask, Django, and Node.js for backend development. Additionally, I leverage OpenAI, Hugging Face, VectorDBs like FAISS and Weaviate, and orchestration tools for seamless deployment. My expertise in fine-tuning LLMs, integrating vector search, and leveraging medical embeddings enables me to build AI agents tailored for specific industries, including healthcare and finance. I ensure that the AI agents I develop are scalable, efficient, and optimized for real-time interactions while maintaining accuracy and robustness in responses. These AI agents can be seamlessly integrated into existing websites, mobile applications, or enterprise systems, enhancing automation and user engagement. I also specialize in optimizing AI agents for minimal latency and high performance, utilizing techniques like prompt engineering, retrieval-augmented generation (RAG), and caching mechanisms. My AI Caller Agent is designed to handle automated phone interactions, using speech recognition and NLP to facilitate seamless conversations and task automation. With my deep understanding of LLMs, vector search, and real-time processing, I can create AI agents that provide accurate, contextual, and intelligent responses tailored to specific use cases.

I have extensive expertise in developing AI models that generate 3D models from 2D images with high accuracy and efficiency. My approach involves generating depth images from 2D inputs and then constructing detailed 3D models by combining RGB and depth data. I leverage cutting-edge AI/ML frameworks such as TensorFlow, PyTorch, Open3D, Deep Learning Super Sampling (DLSS), and NVIDIA Omniverse for optimal performance. These frameworks enable me to implement advanced neural architectures and depth estimation techniques to enhance 3D model reconstruction. With my deep knowledge of AI and computer vision, I ensure that the generated 3D models are highly realistic and suitable for various applications.

My projects: 🔺 Customer Segmentation & Personalization – Using AI-driven clustering (K-means, DBSCAN) and NLP techniques to segment customers based on purchasing behavior, transaction history, and engagement patterns. 🔺 Churn Prediction & Retention Strategies – Developing predictive models with XGBoost, Random Forest, or LSTM to identify high-risk customers and optimize retention strategies. 🔺 Marketing Attribution Modeling – Implementing Multi-Touch Attribution (MTA) and Markov Chain models to analyze campaign effectiveness and optimize ad spend. 🔺 Sales Forecasting & Demand Prediction – Leveraging time-series forecasting methods (ARIMA, Prophet, LSTMs) to predict future sales and improve inventory management. 🔺 Customer Lifetime Value (CLV) Analysis – Building ML models (Gradient Boosting, Deep Learning) to estimate CLV, helping businesses focus on high-value customers. 🔺 Real-Time AI-Powered Campaign Optimization – Utilizing reinforcement learning and automated A/B testing to optimize ad placements and marketing spend dynamically. My frameworks: 🔶 LangChain & LlamaIndex – For AI-driven customer insights, document retrieval, and marketing knowledge bases. 🔶 TensorFlow & PyTorch – For deep learning-based models in churn prediction, CLV, and personalization. 🔶 Scikit-learn & XGBoost – For traditional ML applications in segmentation, attribution modeling, and forecasting. 🔶 Apache Spark & Dask – For scalable big data processing in marketing analytics pipelines. 🔶 Hugging Face Transformers – For NLP-based customer feedback analysis and sentiment tracking.