Pilot Energy Accelerates Energy Purchasing Decisions with a Cloud-Native Analytics Platform on AWS
Industry: Energy
THE CHALLENGE
Scaling Expert Energy Advisory Without Operational Risk
Pilot Energy’s advisory business relied on deep domain expertise to model energy consumption, evaluate supplier offers, and simulate purchasing strategies for large commercial and public-sector customers. As the firm grew, its core advisory workflow became increasingly constrained by a legacy process.
Generating a complex purchasing recommendation for a single customer could take up to four hours, limiting throughput and slowing decision-making in fast-moving energy markets.
Key challenges included:
- Single Point of Failure: Critical logic lived in a single spreadsheet understood by only one senior advisor.
- Slow Turnaround: Long analysis cycles reduced the number of clients advisors could support.
- Operational Risk: anual data entry and complex formulas increased the risk of costly errors.
- Growth Constraints: The workflow did not scale to additional advisors or higher client volume.
The Hidden Pain Points
- Key-Person Dependency: If the primary advisor was unavailable or the spreadsheet became corrupted, Pilot Energy’s ability to deliver recommendations was at risk.
- Error Sensitivity: A single mis-keyed value or incorrect formula could lead to inaccurate pricing scenarios and erode customer trust.
- Limited Scalability: The manual workflow prevented concurrent work and capped the firm’s advisory capacity.
ZetaMinusOne’s Solution
Core Features of the Solution
1. Modern Web-Based Advisor Interface
A Next.js front end provides advisors with an intuitive interface to upload consumption data, define purchasing strategies, and run complex scenarios without manual spreadsheets.
2. Cloud-Native Compute Without Server Management
A containerized C#/.NET 8 backend runs on Amazon ECS with AWS Fargate, eliminating the need to manage EC2 instances, operating systems, or cluster capacity.
3. Elastic, Cost-Efficient Data Storage
Amazon Aurora Serverless v2 (PostgreSQL-compatible) automatically scales database capacity based on demand, aligning infrastructure cost with Pilot Energy’s 9-to-5 usage pattern.
4. Asynchronous Analytics Processing
Long-running or compute-intensive calculations are decoupled from the user interface using Amazon SQS and AWS Lambda, keeping the application responsive during peak usage.
Key Technical Features
Scalable Application Architecture
User traffic flows through an Application Load Balancer (ALB) into a containerized backend running on ECS with Fargate, enabling seamless scaling as advisor usage increases.
Serverless Database with Usage-Based Scaling
Aurora Serverless v2 automatically scales up during business hours and down during evenings and weekends, minimizing total cost of ownership while maintaining enterprise-grade reliability.
Decoupled Background Processing
Heavy analytics workloads are queued in Amazon SQS and processed asynchronously with AWS Lambda, preventing bottlenecks and ensuring consistent user experience.
Secure, Observable, and Automated Operations
The platform uses Amazon S3 for logs and backups, Amazon ECR for container images, and AWS Secrets Manager for secure credential management. Amazon CloudWatch, integrated with Arctic Wolf, provides monitoring and observability, while Terraform and GitHub Actions enable infrastructure-as-code and CI/CD across environments.
THE IMPACT
Faster Decisions, Lower Risk, and a Scalable Advisory Platform
The Power Up platform fundamentally transformed Pilot Energy’s advisory operations—dramatically improving efficiency, eliminating key-person risk, and enabling sustainable growth without increasing operational overhead.
Results at a Glance
5–10 minutes per analysis.
work simultaneously without interference.
and optimization features.
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