Earthview is an environmental technology company specializing in IoT-powered emission monitoring and compliance solutions. Its BluBird IoT network enables organizations in the oil, gas, and energy sectors to detect and respond to methane and other greenhouse gas emissions at scale.

Earthview needed to streamline how its field technicians respond to emission events detected by its BluBird IoT devices. The existing manual workflows struggled with: Large volumes of low- and high-severity alarms, with many false positives.

• Inefficient technician dispatching, leading to delayed responses.
• Limited ability to balance technician workloads across large geographies.
• Complex integration of IoT telemetry, historical data, and vehicle locations.

Earthview required a scalable solution that could intelligently prioritize incidents, optimize routing, and automatically communicate assignments to technicians.

AWS was chosen for its serverless-first design, security features, and ability to scale elastically with IoT-driven workloads. Its managed ML hosting, event orchestration, and messaging services provided the foundation for automating Earthview’s entire emissions response workflow. With multi-AZ deployments, private networking, and IaC-based provisioning, AWS offered both resilience and compliance with enterprise standards.

SnapSoft implemented a fully serverless, event-driven emission routing engine designed for intelligence, scalability, and automation:

• Machine Learning Severity Prediction: An XGBoost model hosted in Amazon SageMaker evaluates incoming alarms from DynamoDB, enriched with historical context, to assign severity scores and reduce false positives.

• Automated Routing Optimization: A routing engine, powered by AWS Lambda and Mapbox APIs, balances workload distribution and minimizes technician travel time by considering live GPS data from vehicles, static site metadata from PostgreSQL, and real-world road constraints.

• Technician Communication: Every four hours, the system automatically sends assignments to technicians via Amazon SES, attaching KML files with GPS routes and contextual details for navigation.

• Secure and Resilient Architecture: All compute runs in private subnets with VPC endpoints, RDS is accessed via VPC peering, and outbound traffic to Mapbox is securely routed through a NAT gateway. IAM, Secrets Manager, and S3 encryption enforce strict security practices.

• Infrastructure as Code: The entire solution is provisioned via Terraform, ensuring reproducibility, governance, and auditability.

• Amazon DynamoDB (IoT telemetry, timeseries incident logs)
• Amazon RDS for PostgreSQL (static organizational and fleet metadata)
• Amazon SageMaker (ML inference for severity prediction)
• AWS Lambda (serverless microservices for severity prediction and routing)
• Amazon EventBridge (4-hour orchestration trigger)
• Amazon SNS (decoupled microservice messaging)
• Amazon SES (technician notifications)
• Amazon S3 (KML and JSON result storage, auditability)
• AWS Secrets Manager (secure credential storage)
• Amazon VPC (private subnets, NAT gateway, VPC endpoints, peering)
• Amazon CloudWatch (monitoring and logging)

Beyond operational efficiency, the Earthview–SnapSoft solution makes a direct contribution to environmental, social, and governance (ESG) goals. By enabling real-time detection and faster remediation of methane and greenhouse gas leaks, the system helps industries significantly reduce their carbon footprint. This proactive approach not only safeguards regulatory compliance but also supports global climate commitments.

On a societal level, reducing harmful emissions protects the health of surrounding communities, improves air quality, and contributes to long-term environmental stewardship. The intelligent routing optimization further minimizes technician travel, lowering vehicle-related emissions and promoting greener field operations.

Together, these innovations demonstrate how digital transformation can advance both business outcomes and planetary well-being, setting a benchmark for responsible technology adoption in the energy sector.

• Intelligent Incident Prioritization: ML-driven severity prediction ensures that high-impact emission events are addressed first, reducing false positives.
• Optimized Technician Dispatching: Route optimization with real-world travel times minimizes technician travel, increases efficiency, and balances workloads.
• Automated, Scalable Operations: EventBridge and Lambda ensure the system runs continuously without manual intervention, scaling automatically with data volume.
• Improved Technician Support: Automated emails with KML files provide clear, actionable routing instructions to technicians every four hours.
• Security-First, Compliant Design: Private subnets, VPC endpoints, IAM, and encryption ensure operational resilience and compliance.
• Reproducible Infrastructure: Terraform provisioning guarantees consistent deployments and simplifies operational governance.