AI Dispatch CoPilot

AI-Assisted Dispatch Intelligence on AWS

Sphere's AI Dispatch Copilot is a professional services offering for organizations designing and implementing AI-assisted dispatch and operational decision-support solutions on AWS.

Sphere engineers the intelligence layer that connects incoming call or event data, unit telemetry, historical incident information, and operational signals with existing dispatch and CAD workflows. The system analyzes this information in real time and presents recommendations to dispatch personnel without replacing the dispatcher's existing system or decision-making authority.

Solutions are deployed within the customer's AWS environment and can incorporate AWS services for machine learning and generative AI, real-time data processing, storage, APIs, monitoring, and security. Depending on customer requirements and architecture, implementations may use services such as Amazon SageMaker, Amazon Bedrock, Amazon Kinesis, AWS Lambda, Amazon S3, Amazon DynamoDB, and Amazon CloudWatch.

Human-Controlled Decision Support

AI Dispatch Copilot is designed to support, not replace, trained dispatch personnel. The system can prioritize incoming events, evaluate available resources, identify operational patterns, and surface recommended actions within the dispatcher's existing workflow. Final dispatch and routing decisions remain with authorized personnel.

Demand and Operational Intelligence

The solution can analyze historical and real-time operational data to identify demand patterns and emerging capacity constraints. This gives dispatch teams additional visibility into workload, resource availability, and changing operational conditions before service levels are affected.

Integration with Existing Dispatch Systems

Sphere designs integrations around the customer's existing CAD, dispatch, telemetry, and operational systems. The goal is to add an AWS-based intelligence layer without requiring replacement of the customer's core dispatch platform.

Sphere's Five-Step Implementation Process

Technical discovery and assessment of the customer's existing dispatch, CAD, data, and AWS environment.

Data integration and development of AI and machine learning workflows using historical and operational data.

Configuration of dispatcher-facing recommendations, workflows, security controls, and user experience, followed by iterative testing.

Supervised pilot using a defined operational scope with ongoing performance evaluation and refinement.

Production deployment, monitoring, documentation, and optimization.

Sphere works with the customer to define appropriate security, access-control, data-retention, monitoring, and human-review requirements for the deployment.

Any AWS infrastructure or AWS service usage charges incurred within the customer's AWS account are separate from Sphere's professional services fees and are the responsibility of the customer.