Automations
This pillar focuses on space operations workflows that automate telemetry monitoring, power management, payload scheduling, and ground station coordination across large satellite fleets. The content should show how custom constellation automation improves mission continuity, lowers operational overhead, and supports the scaling demands of commercial space infrastructure.
This foundational page details the end-to-end architecture for automating core constellation operations, from telemetry ingestion to command execution. It explains how a custom multi-agent workflow reduces operator headcount, improves mission continuity, and provides a blueprint for integrating disparate flight dynamics, mission planning, and ground station systems into a cohesive, autonomous control loop.
This page covers the automation of ingesting high-volume telemetry streams, applying statistical and ML models to detect anomalies, and intelligently routing alerts to the correct engineering sub-team. The workflow reduces mean time to detection, prevents alert fatigue, and requires a robust architecture for data streaming, model serving, and integration with ticketing systems like Jira or ServiceNow.
This page explains a workflow that moves beyond reactive monitoring to predict component failures (e.g., battery, reaction wheel) using historical telemetry and survival analysis models. It details how automating these predictions triggers pre-emptive maintenance scheduling or safe-mode transitions, protecting asset lifespan and reducing unplanned service interruptions for constellation operators.
This page outlines an autonomous workflow that continuously optimizes battery state-of-charge across a constellation, balancing solar input, eclipse periods, and payload demand. The architecture integrates power system models, weather forecasts for solar flux, and constraint-based optimization to maximize battery health and ensure continuous operations, directly impacting satellite longevity and reducing replacement costs.
This page details the agentic system that autonomously commands satellite attitude to optimize solar panel orientation relative to the sun, factoring in mission constraints and thermal limits. The workflow automates a repetitive, calculation-intensive task to maximize energy generation, a critical factor for power-intensive constellations, and integrates with flight dynamics software for command validation.
This page covers the automation of forecasting upcoming eclipse events for each satellite, modeling their energy impact, and pre-emptively adjusting power loads or scheduling non-critical tasks. This workflow eliminates manual planning overhead, prevents brownouts, and requires integration with orbital propagators and power system simulators to create a proactive energy management layer.
This page explains the multi-agent workflow for automatically ingesting imaging requests, evaluating constraints (cloud cover, satellite visibility, memory), and generating an optimized, conflict-free tasking schedule. It shows how this automation increases constellation utilization and revenue throughput by replacing manual schedulers with an AI-driven system that connects to customer portals and mission planning software.
This page dives into the complex orchestration logic needed to balance competing customer requests with different service-level agreements (SLAs) and priorities. The custom workflow automates the trade-off analysis, schedule generation, and conflict resolution, directly tying the build to improved customer satisfaction and higher-margin capacity allocation for commercial imagery providers.
This page details an agile workflow where AI agents monitor external feeds (e.g., news, weather, AIS) for events of interest (wildfires, ship movements), evaluate satellite availability, and autonomously generate and validate re-tasking commands. This automation captures high-value, time-sensitive data that manual operations would miss, creating a competitive advantage for intelligence and monitoring services.
This page covers the automation of planning and executing seamless handovers between global ground stations as satellites pass overhead. The workflow optimizes for cost, data latency, and antenna availability, replacing brittle manual schedules with a dynamic system that integrates with ground station provider APIs (like AWS Ground Station or KSAT) and network management tools.
This page explains the agentic system that analyzes satellite orbits and ground station calendars to book optimal contact windows, automatically resolving conflicts across a multi-satellite fleet. This workflow reduces ground segment costs, maximizes data downlink opportunities, and requires a bidding/negotiation logic layer when interacting with shared commercial ground networks.
This page details the workflow for intelligently managing the downlink queue at each ground station, prioritizing data based on age, customer SLA, and value. The automation ensures high-priority data (e.g., disaster imagery) is transmitted first, improving service quality, and involves integrating with onboard storage telemetry and data management systems.
This page covers the automation of ingesting CDMs from space traffic management services (like Space-Track), applying risk-scoring models, and flagging high-probability conjunction events. The workflow reduces the manual burden on flight dynamics teams, standardizes risk assessment, and creates an audit trail for regulatory compliance, requiring integration with orbital propagation tools.
This page explains the high-stakes workflow where, upon a high-risk conjunction alert, an AI agent generates multiple CAM options, simulates their outcomes, and recommends the most fuel-efficient plan that maintains mission safety. Automating this complex astrodynamics task preserves precious propellant, extends satellite life, and accelerates response time in critical situations.
This page details the automation of routine orbit maintenance tasks—calculating drift, planning station-keeping maneuvers, and maintaining the desired constellation geometry. The workflow replaces periodic manual analysis with a continuous control loop, ensuring optimal fleet positioning for coverage and inter-satellite links, and integrates with flight dynamics and propulsion systems.
This page covers the mission control center workflow where an AI agent synthesizes telemetry status, scheduled payload tasks, ground contacts, and planned maneuvers into a validated, conflict-free daily plan for operator review. This automation eliminates hours of manual shift planning, reduces human error, and serves as the master schedule for all downstream automated systems.
This page explains the workflow that automatically aggregates the past shift's telemetry, events, resolved tickets, and open issues into a concise natural language summary for the incoming team. This automation ensures consistent, comprehensive handovers, improves situational awareness, and frees engineers from administrative report writing.
This page details the implementation of a conversational AI interface that allows operators to ask questions (e.g., 'What's the battery health of SAT-102?' or 'Show me passes over Ukraine in the next 6 hours') and get synthesized answers from disparate databases. This workflow speeds up information retrieval and requires a RAG architecture over telemetry, tasking, and orbital databases.
This page covers the automation of assembling technical data, orbital parameters, and corporate information into draft filings for regulatory bodies. The workflow reduces legal and engineering overhead during licensing and renewal cycles, minimizes errors, and requires templates, rule-based validation, and integration with document management systems.
This page explains the edge AI workflow where raw sensor data (e.g., imagery, RF signals) is processed on the satellite to extract features (e.g., ship detections, cloud masks) before downlink. This automation reduces ground station bandwidth requirements, lowers latency for actionable insights, and involves deploying and managing ML models on constrained space-grade hardware.
This page details the ground-based workflow that automates the post-processing of raw satellite imagery through a sequence of AI and geometric models for calibration, orthorectification, and mosaicking. This automation accelerates product delivery, improves consistency, and creates a scalable pipeline that integrates with cloud storage and processing platforms like AWS or GCP.
This page covers the post-downlink workflow where data products are automatically tagged, routed to different processing pipelines or cloud buckets, and notifications are sent based on predefined customer SLAs and priorities. This automation ensures premium customers get fastest service, optimizes cloud compute costs, and requires integration with CRM and billing systems.
This page explains the critical workflow for autonomously executing the post-launch sequence of tests (LEOP, commissioning) to bring a new satellite into the operational fleet. The automation reduces the intense, around-the-clock manual effort required, standardizes the process, and integrates with test scripts, telemetry checkers, and anomaly response systems to ensure a safe and efficient onboarding.
This page details the automation for updating all operational systems—tasking schedulers, ground station planners, health dashboards—to incorporate a newly commissioned satellite. This workflow prevents configuration errors and service disruption during fleet scaling, involving agents that propagate new satellite parameters across multiple databases and control systems.
This page covers the intelligent workflow that automatically begins assigning imaging tasks or communication traffic to new satellites based on their capabilities and orbital position, effectively integrating them into the revenue-generating fleet. This automation accelerates the ROI on new launches and requires dynamic capacity modeling within the tasking system.
This page explains the cybersecurity workflow that monitors telecommand uplinks for anomalous patterns, potential intrusions, or spoofing attempts using behavioral analytics. Automating this detection and alerting strengthens the security posture of the constellation, a critical concern for commercial and government operators, and integrates with security incident and event management (SIEM) systems.
This page details the high-reliability workflow where, upon detection of a critical subsystem anomaly, AI agents diagnose the likely fault, isolate it, and execute a pre-approved sequence to transition the satellite to a safe, power-positive state. This automation minimizes the 'panic' window, prevents fault propagation, and preserves the asset while engineers are looped in.
This page covers the automation of testing new flight software patches in a sandbox environment, validating them against operational constraints, and orchestrating a staged, safe rollout across the fleet. This workflow reduces the risk and operational burden of software updates, which are frequent in agile space operations, and requires robust version control and rollback mechanisms.
This page explains the commercial workflow where AI agents analyze historical demand, competitor pricing, and real-time capacity utilization to adjust pricing for imaging or communication services. This automation maximizes revenue yield, similar to airline pricing, and requires integration with the sales CRM and tasking system to enforce price-based access controls.
This page details the workflow that automates the process from signed contract to active service: creating accounts, setting up API keys, configuring data delivery endpoints, and initiating billing. This automation scales customer operations without adding headcount, reduces time-to-first-image, and connects CRM, identity management, and billing platforms.
This page covers the workflow that continuously tracks service delivery (e.g., image delivery time, data latency) against contractual SLAs, automatically detects violations, and calculates or issues service credits. This automation ensures contractual compliance, improves customer trust, and reduces manual accounting overhead by integrating with the billing system.
This page explains an industry-specific workflow that fuses satellite-based Automatic Identification System (AIS) data with optical/SAR imagery to automatically detect dark ships, monitor port activity, and generate alerts for maritime security clients. This automation creates a high-value intelligence product from raw data streams, requiring fusion algorithms and geospatial analytics pipelines.
This page details a public service workflow where AI agents analyze thermal and optical satellite imagery in near-real-time to detect new wildfires, track their growth, and automatically generate alerts and perimeter maps for emergency services. This automation provides life-saving early warning and requires integration with geospatial alerting platforms and government agency systems.
This page covers an agri-tech workflow that processes multispectral satellite imagery to calculate vegetation indices (like NDVI), detect crop stress or disease, and trigger automated irrigation or scouting alerts for farm management systems. This automation enables precision agriculture at scale, turning imagery into actionable field-level prescriptions.
This page explains a workflow for the energy sector where InSAR (Interferometric Synthetic Aperture Radar) and optical satellite data are automatically analyzed to detect ground subsidence, leaks, or encroachment along pipeline routes. This automation replaces costly manual patrols and helicopter surveys, providing continuous monitoring and early warning of integrity threats.
This page details a workflow for insurers where satellite-derived data (flood extent, building footprints, vegetation density) is automatically ingested and scored to assess property risk, validate claims, or model portfolio exposure. This automation improves underwriting accuracy and claims efficiency, requiring integration with geospatial analytics and core insurance systems.
This page expands on collision avoidance by detailing the workflow for ingesting and fusing multiple debris tracking catalogs, propagating their orbits, and assigning dynamic risk scores to each piece relative to the constellation. This automation provides a more comprehensive threat picture than public CDMs alone, enabling proactive risk management.
This page covers the critical workflow that continuously models propellant consumption based on executed maneuvers and forecasts remaining lifetime. The automation generates alerts when satellites approach end-of-life thresholds, enabling proactive deorbit planning and preventing them from becoming unresponsive debris, a key regulatory and sustainability concern.
This page explains the workflow for automatically identifying compliant deorbit windows years in advance, simulating the maneuver sequence, and reserving ground station support. This automation ensures regulatory adherence (e.g., the 25-year rule), optimizes the use of remaining fuel, and turns a complex, one-off planning task into a managed, scheduled process.
This page details a diagnostic workflow where an AI agent correlates anomalies across multiple satellites in a constellation to identify common root causes, such as a space weather event or a systemic software bug. This automation accelerates engineering investigations, turning fleet-wide data into a diagnostic asset rather than a siloed burden.
This page covers the safety-critical workflow where every command sequence bound for uplink is automatically checked against a digital twin of the satellite state and a library of constraints before being released to the ground station. This automation provides a final, automated guardrail against human error in command generation, preventing potentially mission-ending mistakes.
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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