Continuous Conjunction Data Message (CDM) Assessment and Risk Scoring Workflow

This workflow automates the ingestion of Conjunction Data Messages (CDMs) from services like Space-Track or commercial providers, applying proprietary risk-scoring models that factor in covariance, miss distance, and probability of collision. It eliminates the manual, repetitive task of sifting through hundreds of low-risk alerts to find the few high-probability events, directly reducing operator fatigue and accelerating response time. The operational upside comes from preserving satellite lifespan by enabling fuel-optimal, timely collision avoidance maneuvers, while ensuring compliance with space traffic management regulations through a complete decision log.

Implementation integrates with orbital propagation tools (e.g., AGI STK, custom propagators) and flight dynamics software via APIs. The architecture requires robust exception routing for data gaps or model uncertainty, mandating human-in-the-loop approval gates before any maneuver command is generated. Observability is built in, logging every CDM, score, and action for post-event analysis and regulatory audit. This creates a production-grade, automated layer that scales with constellation size, turning a high-stakes operational bottleneck into a managed, controlled process.

This workflow automates a critical, repetitive bottleneck for flight dynamics teams: manually assessing hundreds of daily CDMs from services like Space-Track. By applying custom risk-scoring models that factor in covariance data, miss distance, and probability of collision (Pc), it flags only high-risk events for human review. The operational upside is a 70-90% reduction in manual screening time, faster response to genuine threats, and a defensible audit trail for regulators like the FCC, directly protecting asset value and license status.

Implementation integrates with orbital propagation tools (e.g., AGI's STK, custom propagators) via API to validate maneuver options. The orchestrator, built on LangGraph, manages state, routes exceptions, and updates the system of record (e.g., a PostgreSQL database linked to Jira). Required controls include confidence thresholds for automated scoring, mandatory human-in-the-loop approval for any collision avoidance maneuver (CAM), and full observability into the decision chain for post-event analysis and regulatory reporting.

This workflow automates the high-volume, high-stress process of evaluating potential satellite collisions. It ingests CDMs from sources like Space-Track.org and commercial providers, applies proprietary risk-scoring models that factor in covariance data, mission criticality, and fuel budgets, and flags events requiring human review or automated maneuver planning. The operational upside comes from reducing manual screening burden on flight dynamics teams by over 80%, standardizing risk assessment to prevent oversight, and creating an immutable audit trail for regulators like the FCC. Implementation requires robust integration with orbital propagators (e.g., AGI's STK, FreeFlyer), telemetry databases, and command and control systems.

Architecturally, the system is built on an event-driven orchestrator like LangGraph or Apache Airflow, managing a pipeline of specialized agents for parsing, scoring, and planning. Critical controls include a digital twin simulation for maneuver validation before any command uplink, mandatory human-in-the-loop approval gates for high-risk responses, and comprehensive observability logging for every CDM processed and decision made. Rollout is phased, starting with ingestion and scoring only, then layering on automated planning for low-risk maneuvers, and finally integrating full closed-loop response for high-confidence, fuel-optimal collision avoidance. This phased approach builds operational confidence while delivering immediate value in alert triage.

The continuous CDM assessment workflow automates the ingestion of conjunction data messages from services like Space-Track or commercial providers, applying proprietary risk-scoring models to prioritize threats. This eliminates the manual sifting and subjective triage performed by flight dynamics engineers, standardizing risk assessment across the fleet. The operational upside is a drastic reduction in mean time to decision for high-probability events, preserving propellant through earlier, more fuel-efficient maneuver planning and creating a defensible audit trail for regulatory compliance with space traffic management guidelines.

Implementation requires integrating the orchestrator—built on frameworks like LangGraph for state management—with orbital propagation tools (e.g., AGI's STK, open-source libraries), the satellite command and control system, and ticketing platforms like Jira for alert routing. Critical controls include human-in-the-loop approval gates for any maneuver command, confidence scoring for model outputs, and immutable logging of all CDMs, scores, and actions for regulatory audit. The rollout must be phased, starting with a shadow mode to validate scoring logic against historical conjunction events before enabling live command routing.