Leading Battery Storage Company Uses AWS-Powered AI ROC Agent to Transform Mission-Critical Support Operations
Industry: Energy
THE CHALLENGE
Scaling 24/7 ROC Support Without Increasing Risk or Headcount
As a leading battery storage company expanded its managed energy portfolio, its Remote Operations Center (ROC) faced mounting operational pressure. Operators were responsible for monitoring dozens of battery storage sites while managing a high volume of open support cases—each requiring deep context across Salesforce, emails, vendor documentation, operational data, and internal notes.
Key issues included:
- High Cognitive Load: Operators had to reconstruct context across multiple systems for every ticket.
- Inconsistent “Best Next Action”: After initial troubleshooting, escalation and next steps were slow and varied by operator.
- Manual Shift Handoffs: 12-hour shifts required rapid ramp-up; handoffs were informal and error-prone.
- Heavy Manual Monitoring: Nightly checks across ~85 sites increased fatigue and the chance of missed signals.
- Limited Transparency: Customers had lower visibility into actions taken, impacting trust and perceived value.
The Hidden Pain Points
- SLA Risk & Support Cost Growth: Increasing case volume and battery storage assets raised the risk of delayed escalation and SLA breaches.
- Scaling Constraint: Operational strain limited the company’s ability to expand lifecycle services and partner programs while maintaining quality.
- Customer Confidence: Limited visibility into support actions reduced perceived value—even when ROC effort was high.
ZetaMinusOne’s Solution
Core Features of the Solution
1. AI ROC Agent – Phase 1 (Ticket Assistant)
Operators ask natural-language questions about active Salesforce cases and receive context-rich answers grounded in historical records.
2. Relevant Precedent Retrieval (RAG)
AI ROC Agent retrieves similar past tickets, dispatch records, and conversations to help operators quickly compare patterns and actions taken.
3. Case Summaries & Rapid Ramp-Up
The agent generates concise summaries of case history and key actions, improving shift handoffs and reducing time to understand “what happened.”
4. Production-Grade Security & Delivery
Web access is delivered via CloudFront with WAF protection and Cognito-based authentication for secure ROC and internal stakeholder access.
Key Technical Features
Serverless, Event-Driven Ingestion
Scheduled ingestion of Salesforce support data using Amazon EventBridge and AWS Lambda, writing normalized structured content to Amazon S3.
Orchestrated Indexing Pipeline
AWS Step Functions coordinates validation, transformation, embedding generation, and indexing, ensuring reliability and repeatability.
Vector Search + Hybrid Metadata
Amazon OpenSearch Service stores vector embeddings for semantic similarity search, while Amazon DynamoDB stores case metadata (case ID, site, owner, timestamps) to enable filtering and hybrid retrieval.
RAG Inference on Amazon Bedrock
At query time, relevant context is retrieved and assembled into a system-prompted context window, then sent to a selected foundation model on Amazon Bedrock for concise, grounded answers.
THE IMPACT
Faster Resolution, Higher Throughput, and a Scalable AI Foundation
The AI ROC Agent improved operational efficiency and support quality by reducing time spent searching for context, accelerating decision-making, and establishing a scalable platform for future AI capabilities across the leading battery storage company’s operations.
Results at a Glance
(from ~4–6 hours to ~2.5 hours).
(from ~4 to 5+ complex tickets per 12-hour shift).
with ~40% lower operational burden compared to self-managed approaches.
and next-best-action capabilities (Phase 3)
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