Dynamic Data Routing Based on Customer SLA and Priority Workflow

This workflow automates the critical bottleneck of manually sorting and delivering data after downlink. It tags each data product with customer, SLA tier, and priority, then routes it to the appropriate processing pipeline, cloud storage bucket, and notification system. The operational upside comes from eliminating manual triage, ensuring premium SLAs are met with automated fast lanes, and optimizing cloud compute costs by avoiding over-processing for low-priority data. Implementation requires integration with CRM (e.g., Salesforce) for customer metadata and billing systems for SLA validation.

Architecturally, the core is an orchestrator (e.g., LangGraph) that ingests downlink completion events, enriches them with CRM data, and executes routing logic. Controls are essential: data quality checks trigger exceptions to a human queue, and all routing decisions are logged for SLA auditability. Deployment involves integrating with ground segment software, cloud object storage (AWS S3, GCP Cloud Storage), and messaging services. The result is a measurable reduction in delivery latency for high-value customers and a scalable, hands-off data delivery operation.

Post-downlink, raw data is a perishable asset. Manual sorting and prioritization create bottlenecks, delaying delivery for premium clients and wasting cloud compute on low-priority batches. This workflow automates that entire chain. Upon ingestion, an orchestrator agent immediately tags each data product with metadata (customer ID, product type, collection time) and queries integrated CRM and billing systems to resolve the applicable SLA—defining the required processing latency and delivery endpoint.

The router then dispatches the product to the appropriate cloud pipeline—fast-track GPU clusters for urgent analytics, standard batch queues, or low-cost archival storage. A parallel notification agent triggers alerts upon pipeline entry and completion, updating the customer portal and logging the event to the billing system for SLA compliance auditing. Implementation requires robust integration with platforms like Salesforce or SAP, cloud orchestration (e.g., Kubernetes), and configurable approval gates for any SLA exceptions or routing overrides to maintain operational control.

This workflow automates the critical bottleneck of manually sorting and prioritizing terabytes of downlinked data products. By integrating with CRM and billing systems like Salesforce or SAP, each data packet is instantly tagged with customer tier and SLA terms upon ingestion. The orchestrator then applies routing logic, sending premium, time-sensitive imagery to high-performance GPU clusters for rapid processing while batch analytics are queued for spot instances. This directly translates to faster revenue realization for high-margin customers and a 20-40% reduction in wasted cloud compute spend through intelligent workload placement.

Implementation requires a phased rollout, starting with tagging logic and a simple two-tier routing rule before integrating the full cost-optimization engine. The core orchestrator, built on LangGraph or Temporal, must integrate with cloud storage (AWS S3, GCP Cloud Storage), processing platforms (AWS Batch, Kubernetes), and notification services. Critical controls include a human-in-the-loop queue for low-confidence classifications, an exception handler for failed processing jobs, and comprehensive observability via Datadog or Grafana to monitor SLA compliance, pipeline latency, and cost metrics in real-time.

This workflow automates the critical bottleneck of manually sorting and prioritizing terabytes of downlinked imagery and sensor data. By tagging each data product with customer, SLA tier, and priority upon ingestion, the system dynamically routes it to the appropriate cloud processing pipeline or storage bucket. This ensures premium customers receive sub-hour delivery for time-sensitive analytics, while standard batches are processed via cost-optimized, slower compute instances. The operational upside comes from guaranteed SLA compliance, optimized cloud spend, and the elimination of manual triage labor for operations teams.

Implementation integrates with the ground segment data lake, using metadata from the mission planning and CRM systems (like Salesforce) to apply routing rules. The orchestrator, built with LangGraph or Apache Airflow, manages the pipeline execution and exception handling. Required controls include human-in-the-loop review gates for routing failures, real-time monitoring of pipeline health and SLA adherence, and immutable audit logs for compliance. Phased rollout starts with a single customer tier and one pipeline before scaling to the full constellation and product suite.