Agentic System for Enterprise Brand Intelligence
- RAG Systems
- Multi-Agent Architecture
- Sentiment Analysis
The Challenge: To create a centralized intelligence dashboard for a large employer branding client, synthesizing data from competitive platforms and internal documents.
The Solution: I architected a multi-agent system that scraped, aggregated, and analyzed competitor metrics and sentiment. The core feature was a RAG Chatbot trained exclusively on proprietary EVP documents, ensuring every response adhered to the client’s official brand voice and style. This provided executives with immediate, data-driven content strategies.
Geospatial SEO & Agentic Content Automation
- Agentic SEO Automation
- Geospatial Data
- Content Pipeline
- WordPress Integration
The Challenge: Create a scalable, automated system to generate hyper-local, geo-targeted content that significantly improves competitive search rankings. The Solution: I built an SEO automation pipeline that automatically generated and published blog
The Solution: I built an SEO automation pipeline that automatically generated and published blog posts to a WordPress platform. This system utilized sophisticated logic to interlink city-level articles with nearby Points of Interest (POI) and relevant media to boost SEO authority. Furthermore, I developed endpoints to display competitive scores and average rankings as dynamic markers on a Google Map interface, providing a visual, data-driven guide for the client’s strategy.
Federated Learning for Robust Distributed Training
- Federated Learning
- Distributed ML
- Non-IID Data
- Custom Aggregation
- Research
The Challenge: Conduct advanced research into building robust machine learning models when training across distributed “edge devices” where data sets are non-uniform (non-IID data)
The Solution: I designed and implemented a simulation environment using Dirichlet probability distribution to simulate edge data environments. The core achievement was the development and testing of custom aggregation algorithms to stabilize and improve the global model’s accuracy, demonstrating expertise in future-proof, privacy-preserving, and scalable distributed ML techniques.
