Automations
This pillar covers equipment workflows that analyze vibration, temperature, and usage patterns across cranes, excavators, loaders, and other heavy assets to predict service needs before failures occur. The content should explain how custom maintenance automation reduces downtime, improves asset utilization, and supports remote or edge-connected construction fleets.
This foundational page details the custom orchestration of sensor telemetry, diagnostic agents, and CMMS integration to predict and schedule maintenance for excavators, cranes, and loaders. It explains how the architecture reduces unplanned downtime by 20-40% and connects edge data processing, anomaly detection models, and work order generation into a single, auditable operational loop for construction fleets.
This page covers the custom implementation of an AI workflow that continuously analyzes vibration signatures from critical excavator components to forecast bearing, gearbox, and hydraulic pump failures. It details the edge-to-cloud data pipeline, time-series anomaly models, and the integration with service dispatch systems to trigger maintenance before catastrophic downtime, directly protecting project schedules and repair budgets.
This page explains the architecture for a custom multi-agent workflow that monitors hydraulic pressure, temperature, and flow data to diagnose leaks, pump degradation, and valve failures in real time. It shows how the system generates prioritized alerts, retrieves relevant SOPs, and creates pre-populated work orders in systems like SAP or Maximo, reducing diagnostic time by over 50% for field technicians.
This page details a custom automation workflow that fuses thermal camera data with operational telemetry to detect overheating in crane motors, brakes, and electrical systems. It covers the edge AI models for anomaly detection, the alert routing logic based on criticality, and the integration with inspection logs to create a proactive maintenance strategy that prevents costly failures and enhances jobsite safety.
This page outlines the custom orchestration workflow that automatically reschedules maintenance tasks based on real-time asset health, utilization, and project criticality data. It explains the agentic logic that balances CMMS calendars, parts availability, and crew logistics to optimize resource allocation, reducing scheduled downtime waste and improving fleet availability by over 15%.
This page describes the custom AI workflow that forecasts part failure rates across a fleet and triggers just-in-time procurement orders integrated with supplier systems. It details the data fusion from telemetry and service history, the inventory optimization logic, and the ERP integration to minimize capital tied up in stock while ensuring critical components are available, cutting inventory carrying costs by 25-35%.
This page covers the custom orchestration of field service operations, where agents analyze fault severity, technician skill sets, location, and parts availability to dynamically dispatch and route crews. It explains the integration with GPS, workforce management, and communication platforms to minimize travel time and improve first-time fix rates, directly lowering service operational expenses.
This page details the technical architecture for a custom workflow that ingests IoT sensor alerts, validates them against thresholds, and automatically creates and populates work orders in systems like IBM Maximo or Oracle SCM. It focuses on the data mapping, validation rules, and human-in-the-loop approval gates required to eliminate manual data entry and accelerate the maintenance trigger-to-action cycle.
This page explains the custom AI workflow that analyzes engine load, operator behavior, and site conditions to provide real-time coaching and automatic adjustments for fuel efficiency. It covers the edge processing of CAN bus data, the recommendation agents, and the integration with operator dashboards to reduce fuel costs by 8-12% across a mixed fleet of loaders, dozers, and haul trucks.
This page describes the implementation of a custom workflow that uses telemetry to detect non-productive machine idling, classify its cause, and trigger contextual alerts or training modules for operators. It details the rule-based and ML-driven analysis, the notification system, and the integration with performance management platforms to lower fuel waste and emissions while improving operator accountability.
This page covers the custom architecture for monitoring remote or off-grid heavy machinery using satellite IoT connectivity. It explains the edge compression of critical telemetry, the fault detection agents that operate with high latency, and the alerting workflows that ensure service teams are notified of issues despite lack of cellular coverage, protecting high-value assets in isolated project locations.
This page details the custom workflow for securely managing and deploying firmware and software updates to a dispersed fleet of connected machinery. It covers the version control, rollback mechanisms, compliance checking, and staged rollout logic integrated with asset management systems, reducing the manual effort and downtime associated with field updates by over 70%.
This page explains the custom AI workflow that analyzes load moment, outrigger pressure, wind speed, and ground condition data to predict stability risks for mobile cranes. It details the real-time sensor fusion, the physics-informed ML models, and the automated alert system that can warn operators and lock functions to prevent tip-overs, materially reducing insurance claims and project delays.
This page describes the implementation of a custom site-safety workflow that fuses data from RFID, radar, and camera systems on machinery to detect personnel and asset proximity hazards. It covers the edge processing for low-latency alerts, the integration with machine control systems for automatic slowdown, and the incident logging for safety compliance reporting.
This page details the custom automation of hard hat, vest, and other PPE detection using jobsite cameras and edge AI. It explains the video processing pipeline, the real-time violation alerting to supervisors, and the integration with safety management systems to automate audit trails and reduce manual monitoring labor, fostering a stronger safety culture.
This page covers the custom workflow for automating tower crane operations by calculating optimal hook paths, predicting load sway from weather data, and providing real-time guidance to operators. It details the integration of lift plans, spatial models, and control algorithms to reduce cycle times, improve placement accuracy, and minimize the risk of collisions on dense urban sites.
This page explains the custom AI workflow that analyzes sensor data from bulldozers to monitor blade wear, cutting edge condition, and ground penetration efficiency. It details how the system correlates usage patterns with wear rates, predicts optimal sharpening or replacement intervals, and automatically orders parts, extending blade life and maintaining productivity.
This page describes the architecture for a custom workflow that uses strain gauges and load sensors to monitor stress cycles on concrete pump booms. It explains the digital twin integration for fatigue life calculation, the predictive maintenance scheduling to prevent structural failures, and the automated reporting for regulatory compliance and asset valuation.
This page details the custom implementation of a workflow that continuously monitors tire pressure and temperature across a fleet of wheel loaders. It covers the IoT sensor network, the analytics that link pressure to wear patterns and fuel efficiency, and the automated alerting system that schedules inflation checks, reducing premature tire replacement costs by up to 20%.
This page expands on inventory automation by detailing the custom multi-agent workflow that identifies specific components at risk (e.g., final drives, hydraulic cylinders), validates warranty status, checks supplier catalogs, and generates purchase orders. It focuses on the orchestration between telemetry analytics, OEM parts portals, and procurement systems to achieve true just-in-time spare parts management.
This page explains the custom workflow that automates the end-to-end warranty claim process for heavy machinery. It details how agents compile fault evidence from telemetry and service records, populate OEM-specific claim forms, submit them via API, and track resolution—reducing administrative overhead, accelerating reimbursement, and ensuring claim compliance.
This page describes the custom analytics workflow that ingests fuel, maintenance, repair, and depreciation data to dynamically forecast the total cost of ownership for each machine. It explains the data aggregation from disparate systems, the predictive modeling, and the dashboard integration that enables finance and operations teams to make informed fleet renewal and disposal decisions.
This page covers the custom AI workflow that analyzes machine usage, maintenance history, and market data to predict residual value and recommend the optimal time to sell or trade-in equipment. It details the integration of internal asset data with external market feeds and the agentic logic that provides actionable insights to maximize return on capital assets.
This page details the custom orchestration workflow that links predictive maintenance alerts directly to project scheduling tools like Primavera P6 or Microsoft Project. It explains how the system models the impact of a potential machine failure on the project critical path, recommends mitigation actions (like reassigning assets), and alerts project managers proactively to protect milestone dates.
This page describes the implementation of a custom, agentic workflow that automatically assigns available and healthy machinery to daily project tasks based on capability, location, and priority. It covers the integration with project management and telemetry systems, the optimization algorithms, and the dispatch notifications that reduce manual planning time and improve fleet utilization.
This page explains the advanced custom workflow for multi-modal sensor fusion, where data from vibration, thermal, and acoustic sensors is combined using AI to provide a comprehensive health diagnosis for complex components like engines or transmissions. It details the data synchronization challenges, the ensemble ML models, and how this approach reduces false positives and pinpoints root causes more accurately.
This page covers the custom architecture for maintaining a live digital twin of an excavator that syncs with real-time telemetry and maintenance actions. It explains how the twin is used to simulate stress, predict failure points, and test maintenance interventions virtually, enabling a closed-loop system that optimizes service strategies and extends asset life.
This page details the custom workflow where AI agents analyze failure and maintenance data across an entire fleet to identify and report on common failure modes and their root causes. It describes the data mining across CMMS records, the clustering algorithms, and the automated report generation that helps engineering teams implement design or procedural changes to prevent recurring issues.
This page describes the custom in-cab workflow that provides operators with contextual guidance based on machine telemetry and site conditions. It covers the AI that identifies suboptimal digging or loading patterns, the retrieval of relevant best practices from knowledge bases, and the delivery of timely suggestions via displays or audio to improve efficiency and reduce wear.
This page explains the implementation of a custom hands-free workflow where technicians interact with an AI assistant via voice to diagnose faults. It details the integration with service manuals and historical repair data, the conversational agent built on retrieval-augmented generation (RAG), and how it reduces diagnostic time and improves repair accuracy in the field.
This page covers the custom workflow that connects field technicians wearing AR glasses with remote experts. It details the orchestration of live video feed, annotation tools, and parts lookup, allowing experts to guide repairs in real-time. This system reduces the need for specialist travel, cuts mean-time-to-repair, and captures repair knowledge for future use.
This page describes the custom workflow for monitoring diesel particulate filters (DPF), selective catalytic reduction (SCR) systems, and exhaust sensors to predict failures and schedule cleaning or regeneration. It explains the integration with engine control units (ECUs) and environmental reporting systems to avoid non-compliance penalties and maintain fuel efficiency.
This page details the custom implementation of a rule-based and ML-driven workflow that automatically manages machine idling. It goes beyond detection to enact controls, such as sending shutdown warnings to operators and, if authorized, automatically turning off engines after a safe period, directly cutting fuel costs and emissions.
This page explains the custom orchestration required for managing a mixed fleet of electric and hybrid construction machinery. It covers the workflow that monitors battery state-of-charge, schedules charging based on electricity rates and project demands, and optimizes the deployment of electric vs. diesel assets to minimize total energy cost and carbon footprint.
This page describes the custom workflow that uses fluid level sensors, pressure drops, and even vision systems to detect hydraulic oil, coolant, or fuel leaks. It details the immediate alerting system, the automated shutdown procedures for critical leaks, and the integration with spill response protocols to minimize environmental impact and cleanup costs.
This page covers the custom security workflow that monitors the CAN bus and telematics gateways of connected heavy equipment for anomalous signals indicative of cyber threats. It explains the network traffic analysis, the threat intelligence integration, and the automated response playbooks to isolate affected systems, protecting critical assets from ransomware or sabotage.
This page details the custom workflow that automatically validates the success of a repair or maintenance action. After a work order is closed, the system monitors key performance indicators (KPIs) like fuel efficiency, vibration levels, or hydraulic pressure to confirm the issue is resolved, creating a feedback loop that improves repair quality and holds vendors accountable.
This page explains the custom orchestration platform for managing service events involving multiple OEM or third-party technicians. It details the workflow that coordinates schedules, shares fault data securely, tracks each vendor's performance against SLAs, and automates reporting and invoicing, streamlining complex multi-source maintenance operations.
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.
01
We understand the task, the users, and where AI can actually help.
Read more02
We define what needs search, automation, or product integration.
Read more03
We implement the part that proves the value first.
Read more04
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.
Talk to Us
