Case Study: Essess achieves petabyte-scale thermal-imaging storage and rapid on-demand scalability with Amazon Web Services

Essess - Customer Case Study

Essess is a SaaS company that uses vehicle-mounted thermal-imaging to map the energy performance of buildings and grid assets, helping utilities, government agencies, and building owners detect leaks and prevent energy loss. As a startup collecting more than a petabyte of data per vehicle each year and running bursty machine-learning workloads that sometimes require hundreds of instances, Essess needed a cost-effective, scalable way to store, transfer, and process huge volumes of imagery without tying up capital in underutilized hardware.

By launching on AWS and using Snowball for bulk data ingest, Amazon S3 for storage, Amazon RDS for PostgreSQL, and CloudFormation for rapid deployments, Essess simplified data transfer and storage, automated database provisioning, and scaled compute on demand. The result: reliable, fast data ingestion, the ability to spin up large ML clusters instantly, lower costs through pay-as-you-go pricing, and faster product turnaround that delivers rapid, actionable insights to customers.

Essess

John Morrissey

Director of Software Engineering