Mudassir Altaf

I built an intelligent chatbot integrating Llama 2, LangChain, Hugging Face embeddings, and vector databases to enable document retrieval and context-aware responses. This solution delivered accurate Q&A across large knowledge bases, boosting customer self-service rates and reducing manual support efforts.

I developed a retail sales forecasting solution to address the challenge of predicting product demand and optimizing inventory levels. The project focused on enabling more accurate demand planning, minimizing stockouts, and reducing overstock-related costs.

The system was built on historical sales, pricing, and inventory data. I performed exploratory analysis to identify demand trends, seasonal patterns, and correlations between variables. From this, I engineered time-based features (day, week, month, quarter), lag variables, and rolling-window statistics to capture underlying sales dynamics.

Multiple models were implemented and benchmarked, including Linear Regression and Random Forest for interpretable baselines, XGBoost for advanced ensemble performance, and LSTM networks for sequence modeling. Models were evaluated on R², MSE, RMSE, and MAE, along with computational efficiency in training and prediction.

The outcome was a robust forecasting pipeline capable of delivering accurate sales predictions and actionable insights for retail decision-makers. Key findings included the influence of pricing strategies and inventory availability on sales, providing guidance for procurement and stock management.

I developed a multi-tenant AI chatbot platform for customer support using LangGraph, LangChain, Redis, and Qdrant. I implemented tenant-specific retrieval and hybrid memory pipelines, allowing each business to maintain its own knowledge base. Additionally, I designed a modular onboarding process for new clients, cutting setup time from weeks to hours and improving support efficiency by 40%.

I developed a voice-enabled AI chatbot that transcribes audio using speech-to-text models such as Whisper and AssemblyAI and generates real-time responses with large language models. By integrating retrieval-augmented generation (RAG) pipelines for knowledge-grounded Q&A, I enhanced accessibility and boosted client engagement in the eCommerce and service industries.

I developed an AI-powered image classification system to detect both common and uncommon crop diseases, addressing a critical challenge in modern agriculture: early disease identification to prevent yield loss. The goal was to support farmers and agricultural specialists with an automated tool capable of analyzing plant leaf images and providing reliable diagnostic feedback.

The project involved preparing a large dataset of crop images, organizing and splitting them into training and testing sets, and ensuring class balance for accurate model training. Using transfer learning with DenseNet121, I built a convolutional neural network capable of classifying multiple disease categories. The model architecture combined a pre-trained backbone with custom fully connected layers for multi-class prediction.

To optimize performance, I applied normalization, caching, and prefetching strategies for efficient training.

The model achieved high accuracy on unseen test data and provided robust predictions with class-specific probabilities. Additionally, I implemented a prediction pipeline to process new images and display results with confidence scores, making the system deployable as a practical decision-support tool.