A modularized SDK library for Amazon Selling Partner API (fully typed in TypeScript)

Understand how your brand shows up in LLM responses.

A Visual social listening engine using Python, YOLOv8, and MySQL. analyzes user generated content to detect brand logos and consumption contexts for marketing insights.

Enterprise-grade consumer sentiment discovery engine: ingest multi-platform data, extract themes, score evidence, and generate client-ready DOCX reports.

AI skills for Amazon sellers & agencies on Claude — Amazon PPC/advertising (ACoS, TACoS), search-term harvesting & negative keywords, bulk-file coverage, SQP/Search Query Performance, title generator (75-char + Item Highlights 2026), Category Listings Report catalog audit, listing compliance, variation review-split auditing.

Real-Time Social Media Sentiment Analysis Pipeline using PySpark and TextBlob NLP. Classifies brand mentions as POSITIVE NEGATIVE or NEUTRAL. Built with Python MySQL Hadoop HDFS PySpark Hive and Apache Airflow.

Track brand visibility inside AI-generated recommendations from LLMs like Gemini by analyzing mentions, sentiment, and competing brands.

Brand visibility inside AI answer engines. Mixture-of-Agents across 5 LLMs, XGBoost scoring, embedding-based semantic drift detection. Deployed on AWS ECS Fargate with full CI/CD.

Launch Fast remote MCP bridge for Amazon FBA research, seller analytics, Brand Analytics, Amazon Ads, supplier research, IP checks, and rank tracking.

RoBERTa NLP sentiment analysis of 10,000+ Reddit posts on the Spotify × FC Barcelona partnership — marketing analytics + brand perception insights

ML-powered brand detection and market analytics system. Uses TensorFlow 2 object detection and web scraping to monitor e-commerce shelf presence, pricing, and brand visibility.

Web scraping and analysis of sneaker listings from Amazon.sg to uncover pricing, brand strategy, and customer rating insights.

Real-time brand search trend monitor — interactive Streamlit dashboard powered by Google Trends, Matplotlib & Seaborn