Work
Case studies from the founder's work as a Cloud FinOps Engineer at a global entertainment technology company and as an independent cloud engineer.
These describe the founder's individual experience. They are not the company's past performance.
AutoForecast
The company needed cloud forecasts it could budget against. Robert created AutoForecast, an AI-based forecasting method accurate to within 2%. It was adopted across the company and released $25 to 30 million of infrastructure budget for other priorities.
Automated budgeting
With AutoForecast in place, he automated the company's entire budgeting and forecasting process on top of it.
Rightsizing tool
An internal tool that analyzes Kubernetes services, EC2 instances and the applications running on them in depth, then recommends the right capacity for each.
AI spend in one view
Usage and cost from Anthropic, OpenAI, GitHub Copilot, Cursor and Amazon Bedrock brought into a single view and allocated across teams.
FinOps AI agent
A serverless AI agent with its own function-calling framework, retrieval with vector search and conversation memory. It answers AWS cost analysis questions with over 90% accuracy.
Cost data pipelines
End-to-end pipelines on AWS Lambda, Glue and Athena that process Cost and Usage Reports, detect anomalies and feed FinOps dashboards in Amazon QuickSight, using the FOCUS format.
Cloud engineering for 20+ clients
From 2021 to 2024, Robert worked as an independent cloud engineer for more than 20 clients.