Automations
This pillar covers space traffic workflows that track conjunction risk, model trajectories, and recommend or trigger fuel-aware evasive maneuvers as orbital congestion rises. The content should explore collision avoidance logic, orbital data fusion, and operational decision support for satellite operators facing growing debris complexity.
This foundational page details a custom, end-to-end agentic workflow that ingests SSA data, models conjunction risk, and orchestrates fuel-aware evasive maneuvers for satellite operators. It explains the architecture for integrating sensor feeds, collision probability engines, and maneuver planning logic to reduce operational overhead and collision risk, while maintaining human-in-the-loop controls for high-stakes decisions.
This page covers the custom automation workflow for 24/7 screening of satellite ephemerides against debris catalogs to generate prioritized alerts. It details the business case of reducing analyst screening time by 80%, the architecture for real-time data fusion and probability scoring, and the integration of false-positive filters to prevent alert fatigue in operations centers.
This page explains a custom workflow where specialized agents ingest, normalize, and correlate data from disparate sources like Space Fence, optical telescopes, and commercial feeds to maintain a single operational picture. It focuses on the architecture for resolving conflicting observations, updating object catalogs, and providing a clean data pipeline for downstream risk analysis, directly improving tracking accuracy and decision confidence.
This page details the custom AI workflow that identifies new, uncatalogued debris objects from sensor tracks and classifies them by origin (e.g., breakup, collision). It covers the business value of faster threat identification, the technical architecture combining anomaly detection models with orbital propagation, and the integration with alerting systems to notify operators of new risks in their orbital regimes.
This page outlines the custom implementation of a scoring engine that goes beyond basic conjunction data to model probability density functions, covariance realism, and long-term risk. It explains how this workflow reduces false alarms, prioritizes operator response, and integrates high-fidelity propagators and Monte Carlo simulations into a production decision-support system.
This page focuses on the custom workflow for fleet-wide operators, automating the correlation of debris risk with individual satellite telemetry and health status. It details the architecture for ingesting fleet data, generating asset-specific risk summaries, and routing alerts based on satellite capability (e.g., ailing satellites get higher priority), improving fleet resilience and operational planning.
This page details the architecture for an automated system that calculates optimal delta-V maneuvers under propellant, power, and mission constraints. It explains the business impact of extending satellite lifespan through fuel conservation, the integration of high-fidelity orbit propagators and optimizer agents, and the generation of executable command sequences for operator review and upload.
This page covers the complex custom workflow for constellation operators to plan synchronized evasive maneuvers that avoid creating new conjunctions between their own assets. It explains the need to prevent operational fratricide, the multi-agent architecture for simulating fleet-wide trajectory changes, and the orchestration logic to produce a conflict-free maneuver plan for dozens of satellites.
This page explains the critical custom workflow that automatically verifies a planned maneuver's safety and confirms its successful execution. It details the architecture for running pre-maneuver 'what-if' simulations against the debris catalog and for comparing post-maneuver telemetry to predicted states, ensuring operational integrity and providing a defensible audit trail for regulators and insurers.
This page addresses the high-stakes automation required when a conjunction is imminent and human response is too slow. It outlines the custom architecture for a rules-based and ML-driven agent that evaluates final risk, satellite health, and fuel reserves to authorize a final avoidance burn, including the stringent guardrails, explainability layers, and post-event reporting required for safe implementation.
This page details a custom decision-support workflow where an AI agent evaluates multiple maneuver options, quantifying trade-offs between collision probability, propellant cost, and data/productivity loss. It explains how this system replaces manual spreadsheet analysis, its multi-criteria optimization architecture, and its role in supporting faster, more economically sound operator decisions.
This page covers the custom workflow for autonomously planning and scheduling disposal maneuvers to comply with 25-year rule and specific orbit regulations. It details the business imperative of avoiding regulatory penalties and preserving orbital slots, the architecture for long-term orbit propagation under solar and drag uncertainty, and the integration with mission planning systems to reserve fuel and schedule ground passes.
This page explains the technical implementation of a data-ingestion and fusion layer that harmonizes classified, proprietary, and open-source SSA data feeds. It focuses on the architecture for secure data handling, schema normalization, confidence weighting, and creating a unified, high-accuracy object catalog that provides a competitive advantage in risk assessment.
This page details the foundational, yet critical, custom workflow for continuously pulling Two-Line Element (TLE) and Orbit Determination (OD) data from public and private sources, validating it, and converting it into a standardized format for internal systems. It covers the business need for reliable data pipelines, the error-checking and outlier-detection logic, and the integration with propagation engines.
This page outlines the custom workflow that automates the generation and submission of standardized conjunction reports to services like SOCRATES (Space Data Association) or CARA (NASA's Conjunction Assessment Risk Analysis). It explains how this reduces manual reporting labor, ensures compliance with data-sharing agreements, and improves the quality and timeliness of the shared space situational awareness ecosystem.
This page focuses on the custom document automation workflow that pulls technical conjunction data, applies regulatory templates (e.g., for FCC, NOAA), and generates audit-ready reports. It details the architecture combining RAG for regulation retrieval, LLM agents for narrative drafting, and human approval gates, significantly reducing the legal and compliance burden for satellite operators.
This page details the custom implementation of an intelligent operations dashboard where AI agents prioritize, summarize, and recommend actions for incoming conjunction alerts. It explains the user experience benefits, the backend architecture for real-time data streaming and agentic reasoning, and how it cuts through noise to let human operators focus on the highest-value decisions.
This page covers the sensitive custom workflow for securely and automatically sharing conjunction data and maneuver intentions with other satellite operators. It addresses the business need for collaborative collision avoidance, the architecture for encrypted communication protocols, agentic negotiation of 'right-of-way', and maintaining privacy over proprietary orbital information.
This page explains the custom workflow for rapidly modeling the long-term debris environment and collision risk following a new launch or an on-orbit breakup event. It details the architecture for agentic scenario generation, massive parallel propagation, and risk hotspot identification, providing strategic insights for constellation planners and space traffic managers.
This industry-specific page details the custom automation workflow that blends routine station-keeping maneuvers with reactive collision avoidance for large LEO telecom constellations. It explains the unique challenge of managing thousands of satellites, the architecture for integrated maneuver planning that minimizes total fuel burn and service disruption, and the fleet-wide optimization logic.
This page for Earth observation operators covers the custom workflow that prioritizes the protection of expensive, high-resolution imaging satellites. It details the architecture for tighter conjunction thresholds, integrated planning that avoids maneuvering during critical imaging passes, and specialized agents that weigh the value of the scheduled observation against the collision risk.
This page outlines the custom automation workflow for government and defense entities to continuously monitor designated protected orbital zones (e.g., around critical assets) for unauthorized approaches. It covers the architecture for ingesting national SSA data, automatically classifying objects, generating violation alerts, and supporting diplomatic or military response protocols.
This page for launch providers details the custom workflow for automatically tracking a launch vehicle's upper stage and deployment debris immediately after launch. It explains the business need for verifying insertion orbits and early collision risk, the architecture for rapid correlation of new tracks with launch telemetry, and integration with public conjunction warning systems.
This page explains the custom workflow for insurance underwriters, where AI agents continuously ingest SSA data and operator health status to dynamically score the risk profile of insured assets. It details the architecture for calculating probabilistic loss models, triggering premium re-evaluations or policy condition changes, and providing data-driven insights for portfolio management.
This page covers the specialized custom workflow for missions involving close proximity operations, where the servicer and client satellite create a complex dual-object risk profile. It details the architecture for ultra-precise relative navigation, dynamic keep-out zone calculation, and automated thruster adjustments to avoid micro-debris while performing docking or repair tasks.
This page addresses the budget and capability constraints of CubeSat operators, detailing a lightweight, cost-effective custom workflow for managing collision risk across a small fleet. It explains the architecture for leveraging commercial SSA APIs, implementing simple but robust decision rules, and automating communication for coordination, making professional debris management accessible to academia and startups.
This page for space traffic management entities details the custom workflow to act as a neutral data hub, automating the ingestion, anonymization, and distribution of conjunction data among international operators. It focuses on the architecture for data governance, standardization, and agentic routing of alerts to relevant parties, enhancing global space safety coordination.
This page details the custom workflow that replaces the manual, daily task of sifting through hundreds of conjunction data messages (CDMs). It explains the agentic architecture for parsing CDMs, applying domain-specific rules and ML models to filter out false positives (e.g., high covariance events), and presenting a shortlist of credible risks to analysts, saving hours of labor daily.
This page covers the custom workflow that automates the tedious process of associating new sensor observations ("tracks") with objects already in the catalog. It details the business value of maintaining catalog integrity, the technical architecture for track-orbit correlation algorithms, and the agentic handling of ambiguous cases for human review, drastically reducing analyst workload.
This page outlines the custom, playbook-driven workflow that is triggered when a 'red' alert is received. It details the sequence of automated actions: fetching latest data, executing rapid re-assessment, notifying pre-defined response teams via multiple channels, and preparing the initial maneuver analysis package, compressing response time from hours to minutes.
This page details the custom workflow where an AI agent handles the initial, structured communication with other operators when a conjunction involves mutual risk. It explains the architecture for drafting templated messages, securely sending them via approved channels, parsing responses, and escalating complex negotiations to human operators, streamlining a traditionally slow and manual process.
This page covers the advanced predictive workflow that analyzes satellite telemetry, material aging models, and historical breakup data to forecast potential fragmentation events. It details the business case for proactive warning, the architecture of simulation agents that model the resulting debris cloud dispersion, and the integration of these forecasts into long-term risk models.
This page explains the custom workflow for projecting the orbital debris environment decades into the future, considering solar activity, atmospheric drag, and collision probabilities. It details the architecture for massive Monte Carlo simulations, the agentic analysis of resulting density maps, and how this supports strategic planning for constellation design and regulatory policy.
This page details the custom workflow for simulating the chain-reaction collision scenario known as the Kessler Syndrome. It explains its value for risk communication and long-term policy, the architecture of agent-based simulation where debris objects interact, and how the outputs inform the criticality of active debris removal and stricter mitigation standards.
This page covers the design-phase custom workflow that uses AI to optimize the orbital parameters (altitude, inclination, phasing) of a proposed constellation for inherent debris resilience. It details the architecture for simulating the constellation against historical and projected debris, using evolutionary algorithms to find patterns that minimize lifetime collision probability.
This page details the custom workflow that integrates debris risk management with a satellite's entire lifecycle, from launch to disposal. It automates the tracking of propellant budget against disposal needs, monitors compliance with disposal timelines, and triggers alerts if a satellite is at risk of becoming non-compliant, protecting the operator from regulatory and financial penalties.
This page focuses on the custom optimization workflow that looks across an entire fleet's upcoming conjunction risks to find opportunities for fuel-saving coordination. It details the architecture for a central planner agent that evaluates combined maneuver plans, identifying scenarios where one satellite's maneuver can protect multiple others, directly extending fleet operational life and value.
This page explains the custom workflow that automates the complex economic decision of whether to execute an evasive maneuver. The AI agent quantifies the cost of fuel and lost mission time against the financial risk of collision (including satellite replacement, liability, insurance). It details the architecture for integrating cost models, risk probabilities, and business rules to provide a clear 'recommendation with rationale'.
This page details the custom workflow that projects future fuel needs for collision avoidance based on the debris environment and mission plan. It goes beyond simple tracking to use predictive models of conjunction frequency, advising on fuel reserves, and triggering planning for propellant replenishment (e.g., via in-orbit servicing) years in advance, a critical capability for asset management.
This page covers the custom regulatory workflow that continuously monitors an operator's activities (launch, operations, disposal) against frameworks like the UNOOSA/IADC guidelines. It details the architecture for ingesting regulatory text, mapping it to operational data, generating compliance checklists, and flagging potential violations for review, transforming a manual audit process into a continuous assurance system.
This page details the custom workflow that automates the generation of the detailed collision risk analysis required by regulators like the FCC before granting a launch license. It explains how this accelerates the licensing process, the architecture for simulating the launch trajectory and early orbit phase against the debris catalog, and producing the standardized report output.
This page addresses the critical need for explainability and liability protection, detailing the custom workflow that automatically creates a immutable, detailed record for every AI-recommended or AI-triggered maneuver. It covers the architecture for capturing input data, model reasoning, decision logic, and outcome, producing a defensible package for regulators, insurers, and internal review boards.
This page explains the custom workflow that creates a feedback loop between conjunction risk and satellite subsystem health. It details the architecture for correlating debris proximity events with subsequent anomalies in telemetry, helping to diagnose potential micro-impact damage and informing the health-aware risk assessment for future maneuvers.
This page covers the low-level custom workflow where maneuver plans are translated into specific ACS commands and validated against the satellite's dynamic constraints. It details the technical integration between the high-level planning agent and the flight software, ensuring that commanded thrust vectors are feasible and do not violate torque, pointing, or power limits.
This page details the custom workflow that optimizes the scheduling of communication passes with ground stations by factoring in conjunction events. It avoids scheduling critical command uploads or data downlinks during high-risk periods or immediately post-maneuver when telemetry may be anomalous, improving overall mission reliability and data return.
This page covers the custom workflow triggered after a satellite executes an avoidance maneuver, automatically re-optimizing its mission schedule. The AI agent assesses the new orbit, reschedules payload operations (e.g., imaging targets, communication slots), and updates ground station contacts, recovering lost mission value and restoring operational normalcy faster than manual planning.
This page details the large-scale custom workflow for mega-constellation operators, where a hierarchical multi-agent system manages risk across hundreds or thousands of assets. It explains the architecture for distributed risk assessment, centralized conflict resolution, and the orchestration of coordinated responses, which is impossible to manage manually and is key to scalable space operations.
This page covers the cutting-edge custom workflow where AI models run directly on the satellite to process navigation data and execute emergency maneuvers without ground contact. It details the business case for surviving un-trackable debris, the architecture for lightweight, radiation-hardened inference engines, and the stringent safety gates required for trusted autonomous action.
This page explains the custom workflow for satellites equipped with onboard optical or radar sensors to detect nearby objects. It details the architecture for processing this raw sensor data on the ground (or on-board), correlating it with the catalog, and updating risk assessments with proprietary, high-precision relative navigation data, offering a significant advantage over relying solely on external SSA.
How We Work
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
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We understand the task, the users, and where AI can actually help.
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We define what needs search, automation, or product integration.
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We implement the part that proves the value first.
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We add the checks and visibility needed to keep it useful.
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