AES: Scaling Energy Safety Audits with Anthropic’s Claude on Google Cloud
Customer Company Size
Large Corporate
Region
- America
Country
- United States
Product
- Vertex AI
- Anthropic’s Claude models
Tech Stack
- Google Cloud
- Vertex AI
- Anthropic’s Claude
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Cost Savings
- Customer Satisfaction
- Productivity Improvements
Technology Category
- Analytics & Modeling - Machine Learning
- Platform as a Service (PaaS) - Data Management Platforms
Applicable Industries
- Utilities
Applicable Functions
- Quality Assurance
- Business Operation
Use Cases
- Remote Asset Management
Services
- Cloud Planning, Design & Implementation Services
- System Integration
About The Customer
AES is a prominent American utility and power generation company with extensive operations across North and South America. With over four decades of experience in the energy sector, AES is committed to transitioning the world to clean, renewable energy. The company designs, owns, and operates power plants, generating and selling electricity to end users and intermediaries, including utilities companies and industrial facilities. AES prioritizes safety, conducting more than 1,500 safety management program audits annually to ensure adherence to standards and compliance across its sites.
The Challenge
AES, a US-based energy company, faced challenges in conducting over 1,500 safety management program audits annually. Each audit required approximately 100 employee hours, involving document reviews and checks for thoroughness and accuracy. The company needed a solution to automate decision-making and tasks, aiming for a process at least 100 times faster than human capabilities. The goal was to generate accurate results quickly and reliably, without constant monitoring or re-evaluation.
The Solution
AES selected Anthropic’s Claude models on Vertex AI to automate and optimize its safety audit process. The choice was based on Claude's ability to handle large volumes of API calls and synchronize agents for complex tasks. Integration with Google Cloud tools, such as Vertex AI, provided AES with a scalable infrastructure to experiment, deploy, and manage models. The Vertex AI Model Garden offered access to state-of-the-art models, simplifying deployment and accelerating development. Within two months, AES operationalized AI agents, significantly improving the audit process, especially in document reviews involving up to 400 pages. The agents also facilitated multilingual document analysis, reducing human effort and maintaining focus on details.
Operational Impact
Quantitative Benefit
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