Automations
This pillar addresses geospatial workflows that process satellite and sensor data to detect change, classify assets, and generate monitoring outputs automatically. The content should show how custom earth observation pipelines support defense, logistics, environmental monitoring, and economic intelligence use cases at large scale.
This foundational page outlines a custom, end-to-end agentic workflow for ingesting multi-source satellite, drone, and sensor data, orchestrating change detection and classification models, and routing actionable intelligence to enterprise systems. It covers the architecture for scalable data pipelines, multi-agent orchestration with LangGraph, and integration with defense, logistics, and environmental monitoring platforms to replace manual analyst workflows with automated, auditable intelligence production.
This workflow automates the detection, classification, and movement analysis of ground and naval assets from satellite and aerial imagery, fusing data with signals intelligence. It reduces analyst burden by 80% and provides near-real-time situational awareness, detailing an architecture where specialized agents handle sensor tasking, object detection, track correlation, and alert generation integrated into C4ISR systems like Palantir or custom command centers.
This page details a custom AI workflow that automatically processes pre- and post-strike satellite imagery to assess structural damage, crater analysis, and equipment destruction. It accelerates mission assessment from hours to minutes, explaining the computer vision models, change detection algorithms, and report-generation agents that feed into battle management systems, ensuring faster decision cycles for defense and intelligence units.
This workflow continuously monitors designated areas of interest for construction, earthworks, vehicle accumulation, and other indicative activities, triggering alerts for analyst review. It eliminates the need for manual daily imagery review, detailing a serverless architecture with scheduled satellite data ingestion, diffing agents, and integration with GEOINT platforms for persistent surveillance and early warning.
This page explains a custom workflow that fuses radar, electro-optical, and acoustic sensor data with geospatial context to autonomously detect, classify, and track unauthorized drones near critical infrastructure. It reduces response time and operator fatigue, covering the multi-sensor fusion logic, threat-prioritization agents, and integration with counter-UAS systems for automated alerting and interdiction coordination.
This workflow automates the monitoring of port congestion, berth occupancy, and container stack locations using satellite and aerial imagery. It provides logistics managers with real-time throughput analytics, detailing the computer vision models for container counting, the orchestration logic that integrates with Terminal Operating Systems (TOS), and the alerting agents for bottlenecks, improving asset utilization and reducing demurrage costs.
This page details a custom workflow where agents ingest AIS data, satellite-derived weather and sea-state forecasts, piracy risk maps, and port congestion data to dynamically recommend or execute optimal shipping routes. It reduces fuel consumption and voyage delays, explaining the reinforcement learning models, multi-objective optimization agents, and integration with voyage management and ERP systems for continuous route adjustment.
This workflow automates the monitoring of cargo containers across transit corridors, using satellite and terrestrial IoT data to detect unauthorized stops, door breaches, or route deviations. It minimizes loss and insurance claims, covering the geofencing logic, anomaly detection agents, and integration with security and logistics platforms to trigger immediate alerts and recovery actions.
This page explains a custom workflow that correlates geospatial weather patterns, road closure data, and port satellite imagery to forecast and alert on potential supply chain delays days in advance. It enables proactive rerouting and inventory buffering, detailing the data fusion pipeline, predictive modeling agents, and integration with Transportation Management Systems (TMS) and ERP platforms like SAP.
This workflow automates the analysis of potential warehouse sites by evaluating proximity to highways, population centers, flood zones, and existing infrastructure via satellite and GIS data. It accelerates site due diligence from weeks to days, covering the multi-criteria decision analysis agents, 3D terrain modeling, and integration with corporate real estate and network planning tools.
This page details a custom monitoring workflow that processes high-frequency satellite imagery (e.g., Sentinel, Planet) to identify new forest clearings, classify logging activity, and automatically generate violation reports for enforcement agencies. It replaces manual patrols and batch analysis, explaining the change detection models, alert-routing agents, and integration with government compliance and NGO reporting systems.
This workflow automates the calculation of daily wildfire risk by analyzing satellite-derived vegetation moisture, historical burn scars, weather forecasts, and topography. It provides fire departments and utilities with prioritized risk maps, detailing the data ingestion pipeline, risk-scoring agents, and integration with emergency management platforms for pre-positioning resources and issuing public alerts.
This page explains a custom workflow that processes decades of satellite and lidar data to model shoreline change, predict future erosion hotspots, and assess infrastructure risk. It provides coastal engineers and planners with automated, audit-ready reports, covering the time-series analysis agents, simulation models, and integration with municipal GIS and asset management systems for long-term resilience planning.
This workflow automates the detection of algal blooms, sediment plumes, and chemical discharges in water bodies using multispectral satellite imagery. It identifies likely pollution sources by analyzing upstream land use, detailing the spectral analysis models, source-attribution agents, and integration with environmental agency compliance systems for rapid investigation and enforcement.
This page details a custom workflow for carbon project developers and verifiers, using satellite data to automatically monitor afforestation, reforestation, and avoided deforestation over contract areas. It generates verification-ready evidence packs, explaining the time-series analysis, biomass estimation models, and integration with carbon registry APIs to reduce manual measurement costs and audit friction.
This workflow automates the analysis of satellite and drone NDVI/NDRE indices to map crop vigor, detect stress patterns indicative of disease or pests, and predict outbreak zones. It enables precision intervention, detailing the spectral analysis pipeline, predictive modeling agents, and integration with farm management software (FMS) to trigger scouting or treatment orders.
This page explains a custom workflow where agents analyze evapotranspiration rates, soil moisture from satellites, and local weather forecasts to generate variable-rate irrigation prescriptions for center pivots or drip systems. It optimizes water use and yield, covering the data fusion logic, prescription-generation agents, and direct integration with IoT-based irrigation control systems like Lindsay's FieldNET.
This workflow automates in-season yield forecasting by fusing satellite-derived vegetation indices, soil maps, and weather data with historical yield patterns. It provides agronomists and grain buyers with field-level forecasts, detailing the machine learning models, forecast-aggregation agents, and integration with commodity trading and harvest logistics platforms for optimized planning and pricing.
This page details a custom workflow that processes radar and optical satellite data to create high-resolution soil moisture maps and correlates them with crop health to infer nutrient deficiencies. It replaces manual soil sampling for broad-acre assessment, explaining the remote sensing models, zone-creation logic, and integration with variable-rate application equipment and input supply systems.
This workflow automates the validation of crop insurance claims by comparing self-reported damage against satellite imagery analyzed for hail, flood, or drought impact. It drastically reduces adjuster field visits and fraud risk, detailing the event detection models, claim-matching logic, and integration with insurance core platforms like Guidewire for accelerated, evidence-based payout processing.
This page explains a custom workflow for city planners that automatically detects new construction, quantifies impervious surface growth, and checks against zoning maps and master plans. It ensures proactive compliance monitoring, detailing the building footprint extraction models, change detection agents, and integration with municipal GIS and permitting systems like Accela.
This workflow automates the analysis of traffic patterns using satellite-derived vehicle detection and ground sensor data to model congestion, identify bottlenecks, and simulate the impact of signal timing changes. It supports dynamic traffic management, detailing the simulation agents, optimization logic, and integration with adaptive traffic control systems (ATCS) for cities.
This page details a custom workflow that uses satellite and cellular data to map population density, job centers, and existing transit ridership to model and optimize bus routes and rail extensions. It replaces manual surveys and static models, explaining the spatial analytics agents, simulation environments, and integration with transit agency planning software for data-driven network redesign.
This workflow automates the creation and updating of digital twin city models by extracting building footprints, heights, and roof forms from stereo satellite imagery and lidar. It accelerates GIS database maintenance, detailing the computer vision pipeline, 3D reconstruction agents, and integration with BIM and smart city platforms like Esri's ArcGIS Urban for planning and simulation.
This page explains a custom workflow for general contractors and developers that uses weekly satellite or drone imagery to automatically measure earthwork volumes, track structure completion against schedule, and flag unauthorized work. It provides real-time project dashboards, detailing the progress quantification models, schedule-integration agents, and alerts routed to project management software like Procore or Autodesk Build.
This workflow automates the surveillance of thousands of miles of pipeline right-of-way using satellite-based synthetic aperture radar (InSAR) for ground subsidence and multispectral imagery for vegetation stress indicative of leaks. It enables preventive maintenance, detailing the anomaly detection models, prioritization agents, and integration with pipeline integrity management systems (PIMS) for work order generation.
This page details a custom workflow where agents analyze solar irradiance maps, land slope, proximity to grid infrastructure, and environmental constraints to rank potential sites. Post-construction, it monitors panel soiling and vegetation encroachment. It accelerates development and optimizes ROI, explaining the multi-criteria analysis, performance benchmarking agents, and integration with asset management platforms.
This workflow automates the micro-siting of wind turbines within a project area by analyzing high-resolution terrain data, historical wind patterns, and simulating wake losses to maximize energy yield. It reduces manual engineering effort, detailing the computational fluid dynamics (CFD) models, optimization loops, and integration with wind resource assessment and layout design software.
This page explains a custom workflow that processes satellite and lidar data to map vegetation height and growth rates near transmission lines, predicting contact risk and optimizing trimming schedules. It prevents wildfires and outages, detailing the risk-scoring models, work-order generation agents, and integration with utility GIS and maintenance management systems like SAP EAM.
This workflow automates the monitoring of oil and gas fields using InSAR satellite data to detect millimeter-scale ground subsidence or uplift caused by extraction or injection activities. It ensures regulatory compliance and operational safety, detailing the time-series InSAR processing pipeline, threshold alerting agents, and integration with reservoir engineering and regulatory reporting systems.
This page details a custom workflow for insurers that automatically ingests geospatial data on flood plains, wildfire perimeters, and seismic hazards to score property-level risk and simulate portfolio exposure. It replaces manual underwriting inspections, explaining the hazard fusion models, exposure aggregation agents, and integration with policy administration systems for dynamic risk-based pricing and accumulation control.
This workflow automates the creation of hyper-local flood risk maps by analyzing LiDAR-derived elevation, rainfall data, and river gauge readings, updating them in near-real-time during events. It enables accurate, dynamic premium calculation, detailing the hydraulic modeling agents, map-generation logic, and integration with insurance rating engines and customer-facing quoting platforms.
This page explains a custom workflow that uses high-resolution satellite and aerial imagery to automatically assess roof age, material, and condition (missing shingles, debris) for residential and commercial properties. It eliminates manual inspections for low-risk policies, detailing the computer vision models, condition-scoring agents, and integration with underwriting workstations to accelerate quote generation.
This workflow automates first-notice-of-loss validation by cross-referencing claimant addresses with satellite-confirmed hail swaths, wind damage signatures, or flood inundation maps. It triages claims instantly, detailing the event verification models, claims-routing logic, and integration with claims management systems like Duck Creek to fast-track legitimate claims and flag suspicious ones.
This page details a custom workflow for commodity traders and analysts that monitors global oil storage tank levels via satellite radar and crop health in breadbasket regions to forecast supply changes weeks ahead of official reports. It provides a trading edge, explaining the tank farm analysis and yield prediction models, data-aggregation agents, and integration with quantitative trading platforms.
This workflow automates the estimation of retail foot traffic and parking lot occupancy using satellite and aerial imagery over time, correlating it with sales data and demographic trends for site valuation. It replaces manual surveys, detailing the vehicle and pedestrian detection models, trend analysis agents, and integration with real estate investment and site selection software.
This page explains a custom workflow that monitors regional construction starts, housing development phases, and commercial building activity from satellite imagery to predict housing inventory and commercial space supply 6-18 months out. It informs investment strategy, detailing the activity detection pipeline, forecasting models, and integration with real estate analytics and portfolio management platforms.
This workflow automates the monitoring of infrastructure projects (ports, mines, factories) in emerging markets for private equity and infrastructure funds, tracking progress against milestones using satellite imagery. It reduces on-site due diligence costs, detailing the progress verification models, milestone-alerting agents, and integration with investment committee reporting and portfolio monitoring systems.
This page details a custom workflow for telecom operators that uses high-resolution 3D city models derived from satellite/lidar to simulate radio frequency propagation, identify coverage gaps, and optimize new tower or small-cell locations. It accelerates network rollout, explaining the RF simulation engines, multi-objective optimization agents, and integration with network planning tools like Atoll.
This workflow automates the planning of terrestrial and subsea fiber routes by analyzing terrain, existing infrastructure, geological hazards, and permitting constraints from geospatial data. It minimizes construction risk and cost, detailing the least-cost-path algorithms, risk-assessment agents, and integration with CAD and project management software for engineering teams.
This page explains a custom workflow that uses satellite imagery and drone data to automatically inspect cell towers for structural issues, vegetation encroachment, and security breaches across vast, remote networks. It prioritizes maintenance dispatch, detailing the anomaly detection models, work-order generation agents, and integration with tower company asset management and field service platforms.
This workflow automates the wide-area monitoring of maritime domains using satellite-based Automatic Identification System (AIS) and radar/SAR imagery to detect, classify, and track vessels, identifying those with disabled transponders. It enhances maritime domain awareness, detailing the sensor fusion logic, behavior anomaly detection agents, and integration with coastal surveillance systems for navies and coast guards.
This page details a custom workflow that continuously monitors offshore platforms for safety violations (e.g., unauthorized vessel approaches, oil sheens, structural anomalies) using satellite and drone data. It ensures regulatory compliance and prevents incidents, explaining the multi-source monitoring pipeline, compliance-checking agents, and integration with offshore operations centers and safety management systems.
This workflow automates the identification of suspected IUU fishing vessels by correlating satellite radar/SAR imagery with Vessel Monitoring System (VMS) and AIS data to find dark vessels operating in protected zones or during closed seasons. It supports enforcement, detailing the gap detection algorithms, risk-scoring agents, and integration with fisheries management and coast guard patrol planning systems.
This page explains a custom workflow for port authorities that uses satellite imagery to automatically count waiting vessels, measure yard occupancy, and model turnaround times. It provides real-time congestion analytics and recommends optimal berth assignments, detailing the computer vision models, simulation agents, and integration with Port Community Systems (PCS) for dynamic resource allocation.
This workflow automates the planning of harvest blocks by analyzing forest inventory, slope, and riparian buffers from satellite/lidar data, and then monitors logging activity to ensure it stays within permitted boundaries. It supports certified forestry operations, detailing the planning algorithms, change detection agents, and integration with forestry GIS and compliance reporting platforms.
This page details a custom workflow for mining companies where agents process multispectral and hyperspectral satellite imagery, geological maps, and geophysical data to identify mineral alteration patterns and rank exploration targets. It reduces early-stage field survey costs, explaining the spectral analysis models, prospect-ranking agents, and integration with exploration information management systems (EIMS).
This workflow automates the monitoring of vegetation regrowth, water quality, and landform stability at closed mine sites using time-series satellite imagery to ensure compliance with reclamation bonds. It replaces manual audits, detailing the vegetation index analysis, change detection for erosion, and integration with environmental management and regulatory reporting systems.
This page explains a custom workflow for emergency response agencies that automatically compares pre- and post-disaster satellite and aerial imagery to map building collapses, road blockages, and flood extent within hours of an event. It accelerates resource deployment, detailing the rapid imagery tasking, damage segmentation models, and integration with common operational picture (COP) platforms like Cesium or Esri.
This workflow automates the site selection and layout planning for refugee camps by analyzing terrain, water sources, and security buffers, and later estimates population density using satellite-derived shelter counts. It supports humanitarian logistics, detailing the suitability analysis agents, population estimation models, and integration with relief organization supply chain and coordination platforms.
This page details a custom workflow that ingests real-time rainfall and river gauge data, combines it with high-resolution elevation models, and automatically generates dynamic flood inundation maps. It identifies at-risk communities and simulates optimal evacuation routes, explaining the hydraulic modeling agents, route optimization logic, and integration with emergency alert and traffic management systems.
This workflow automates the monitoring of crop conditions across entire regions or countries using satellite vegetation indices, correlating them with rainfall and market data to forecast production shortfalls and food insecurity risks. It informs aid allocation, detailing the anomaly detection models, risk-scoring agents, and integration with early warning systems for organizations like the UN World Food Programme.
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.
Read moreThe first call is a practical review of your use case and the right next step.
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