AI-Powered Workflow for Predictive Collision Probability Scoring

Basic conjunction data messages (CDMs) provide a deterministic risk snapshot, but they lack the probabilistic depth needed for efficient operational triage. A custom predictive scoring workflow ingests raw CDMs, covariance data, and high-fidelity propagator outputs to model probability density functions over time. This architecture reduces false alarms by 60-80% by filtering high-covariance, low-probability events, allowing human analysts to focus on credible threats. The business value is direct: it cuts daily screening labor, prevents unnecessary fuel-burning maneuvers, and provides a quantifiable risk basis for go/no-go decisions.

Implementation integrates with systems like AGI's STK or custom propagators via APIs, with scoring logic deployed as containerized microservices for scalability. The workflow must include governance controls: scoring model versioning, human review gates for high-consequence alerts, and immutable audit trails for regulatory and insurance reporting. This moves operators from reactive alert-chasing to a proactive, risk-prioritized operating model, directly protecting asset value and mission continuity. The architecture is built for continuous refinement, feeding post-maneuver outcomes back into the scoring models.

This pipeline automates the critical bottleneck of manually sifting through hundreds of daily Conjunction Data Messages (CDMs) to find credible threats. By implementing specialized agents for data ingestion, covariance realism assessment, and Monte Carlo simulation, it reduces analyst screening time by over 80%. The business value comes from preventing alert fatigue, lowering operational labor costs, and ensuring high-value satellite assets receive timely attention based on a nuanced, fuel-aware risk score rather than simple miss distance.

Implementation integrates high-fidelity propagators (e.g., Orekit, GMAT) and connects to downstream systems via APIs. The scoring engine weighs collision probability against fuel cost for avoidance and potential mission impact, outputting a ranked list with clear rationale. Controls are essential: a human-in-the-loop gate approves any automated action, while the explainability log records all agent decisions for regulatory audit. This architecture is deployed as a containerized microservice suite, often integrated with operator dashboards like COMSPOC or custom-built mission control systems.

Phase 1 establishes the core data and scoring pipeline. We implement a robust ingestion layer for TLE/OD streams from SOCRATES, Space-Track, and commercial feeds, normalizing data into a unified catalog. A baseline probability engine, using covariance-realistic Monte Carlo methods, runs initial screenings. This MVP delivers prioritized alerts to a dashboard, immediately reducing analyst screening time by filtering out high-covariance false positives, providing the first 60-80% of operational time savings while proving the data foundation.

Phase 2 integrates the scoring output with action. We build a maneuver recommendation module that ingests probability scores, satellite telemetry (via TMF), and fuel models to generate fuel-aware delta-V options. This phase includes human-in-the-loop approval gates and command sequence generation. Phase 3 expands to fleet-wide optimization, coordinating maneuvers across constellations to avoid chain reactions, and automates regulatory report generation for FCC/NOAA compliance. Each phase is governed by pre-defined KPIs on false-positive rate, fuel savings, and report generation time.

A production scoring workflow automates the fusion of ephemeris data, covariance realism, and Monte Carlo simulations to generate prioritized, fuel-aware risk scores. This directly reduces operator alert fatigue by filtering out low-probability conjunctions, allowing teams to focus resources on genuine threats. The architecture must integrate high-fidelity propagators (like Orekit or GMAT) and ingest feeds from SOCRATES, Space-Track, or commercial SSA providers, with strict data quality checks and lineage tracking to support auditability.

Rollout should be phased, beginning with shadow mode where scores are generated but not acted upon, compared against legacy analyst decisions. Subsequent phases introduce the workflow into the alert triage loop with mandatory human-in-the-loop approval for any maneuver recommendation. The final phase, enabling autonomous scoring for low-risk, fuel-optimal maneuvers, requires rigorous testing of guardrails and fail-safes. Continuous monitoring tracks model drift in probability estimates and flags deviations for retraining, ensuring the system adapts to a changing debris environment without compromising safety.

A predictive collision probability scoring workflow automates the transformation of raw conjunction data messages (CDMs) into actionable risk intelligence. It replaces manual analyst interpretation with a system that ingests CDMs, Two-Line Elements, and covariance data from sources like SOCRATES or Space-Track. The core business value is an 80% reduction in false-positive alerts, directly lowering operational overhead and preventing costly, unnecessary evasive maneuvers that waste precious satellite propellant and mission time.

Implementation integrates high-fidelity propagators (e.g., Orekit, GMAT) and Monte Carlo simulations within a LangGraph or custom Python orchestration layer. The architecture requires strict controls: scoring logic must be versioned, all simulations logged for audit, and high-consequence scores routed for mandatory human review before action. This creates a production-grade decision-support system that improves risk prioritization while maintaining the governance required for satellite operations and insurance compliance.