Gig Buck Portfolio

Welcome to our comprehensive Development Portfolio

Showcasing our expertise as Data Analysts, Data Scientists, AWS AI Specialists, and n8n Workflow Automation Experts. This portfolio highlights our proven track record in delivering cutting-edge, scalable solutions across data analytics, machine learning, cloud-based AI, and automated workflows.

Our past projects demonstrate technical proficiency in building predictive models, AI-powered applications, real-time data pipelines, and secure, automated workflows using tools like Amazon SageMaker, Amazon Bedrock, n8n, and Python. From crafting personalized recommendation systems to architecting secure AWS cloud solutions, we’ve empowered clients in industries like e-commerce, healthcare, and media to achieve data-driven success.

For our clients, we assure transparency, reliability, and quality. Each project reflects our commitment to solving complex challenges with tailored, high-impact solutions. Explore our portfolio to see how we’ve transformed raw data into actionable insights, streamlined operations through automation, and deployed robust AI systems that drive measurable results. Let our past work inspire confidence in our ability to deliver your next project with precision and excellence.

AI‑Driven Customer Support Bot

AI/ML AWS

Summary: Serverless chatbot that resolves 85 % of queries automatically, reducing support workload dramatically.

Tech Used: Python, AWS Lambda, Amazon Lex, DynamoDB

Outcome: Saved 40 staff‑hours per month; customer CSAT +12 %.

Data Pipeline & BI Dashboard

Data Engineering Analytics

Summary: End‑to‑end pipeline feeding a realtime sales dashboard for executives.

Tech Used: AWS Glue, S3, Athena, Looker Studio

Outcome: Insight latency cut from 24 h to < 1 h; revenue trends now visible in near‑realtime.

Predictive Maintenance for Industrial Equipment

AI/ML Data Science

Summary: Developed a predictive maintenance model using machine learning to forecast equipment failures, reducing downtime by 30% and saving $100,000 in maintenance costs.

Tech Used: Python, scikit-learn, TensorFlow, Pandas, NumPy, Matplotlib

Outcome: Reduced equipment downtime by 30% Saved $100,000 in maintenance costs Improved overall equipment effectiveness (OEE) by 15%