Case Studies
Deep-Dive Technical Case Studies
Explore how I approach complex technical problems, make architectural decisions, and learn from real-world implementation challenges.
Agentic AI Workforce
Coordinated LLM Agents that Automate Enterprise Operations
How I designed an agentic workforce on Databricks that automates workflows and manages tasks for a Fortune 500 client — cutting operational errors 30% and bottlenecks 77% while growing their analytics platform 72%.
Impact: Cut operational errors 30% and workflow bottlenecks 77% for a Fortune 500 client — and grew their analytics platform 72%.
Real-Time IoT Data Platform
Event-Driven Architecture for Sub-5-Second IoT at 99.99% Uptime
How I architected a real-time, event-driven API platform for IoT data achieving 99.99% uptime and sub-5-second end-to-end latency — building on predictive-maintenance work that cut equipment downtime 83%.
Impact: 83% less equipment downtime and ~10% lower backend cost, on a platform holding 99.99% uptime with sub-5-second insight.
ML Energy Forecasting
Time-Series Forecasting that Cut Energy Costs by $2M
How I built a machine-learning forecasting model for industrial energy consumption that reduced costs by $2M in a year — on consolidated data pipelines that cut redundancies 80% and project overhead 50%.
Impact: $2M in energy costs saved in one year, built on pipelines that cut data redundancy 80% and project overhead 50%.
Real-Time Object Detection
Building a Multi-Model Computer Vision System
How I built a real-time object detection system using YOLOv8 and TensorFlow.js, balancing accuracy, performance, and user experience across browser and server-side inference.
Impact: Real-time detection with zero inference infrastructure — webcam analysis runs entirely in the visitor's browser (~30 FPS on development hardware), so the demo scales at no cost.
Multi-Model NLP Pipeline
Sentiment Analysis, NER, and Keyword Extraction
Designing and implementing a production-ready NLP pipeline that combines spaCy, DistilBERT, and TF-IDF for comprehensive text analysis with efficient caching and error handling.
Impact: Automates what manual tagging could never keep up with: sentiment, entities, and keywords for every ingested article — caching cuts repeat-processing latency from ~230ms to under 10ms on this site's deployment.
Multi-Source Data Pipeline
Automated Ingestion, Processing, and Monitoring
Building a scalable data pipeline that ingests from Reddit and News APIs, with automated scheduling, robust error handling, and comprehensive observability.
Impact: Keeps the live analytics pages fresh with no manual work — 50K+ records a day at 99.8% pipeline uptime on this site's deployment, with every run recorded for observability.
Production LLM Platform
Shipping a Public AI Chat with Enterprise-Grade Guardrails
How I put an LLM-powered assistant on the open internet safely: layered rate limits, a hard daily token budget, prompt-injection hardening, a runtime kill switch, and graceful degradation — every guardrail verifiable in this repository's code and tests.
Impact: Caps worst-case daily LLM spend at a fixed 200K-token ceiling across three public demos — with per-IP and global rate limits, bounded concurrency, and a runtime kill switch, all verifiable in this repo.
Want to See More?
Explore the live implementations of these projects and dive into the interactive demos.