Automations
This pillar covers industrial workflows that interpret continuous machine telemetry, retrieve SOPs, diagnose likely faults, and route maintenance work before an outage occurs. Pages should show manufacturers how a custom maintenance workflow reduces downtime, improves service coordination, and links edge AI, digital twins, and maintenance systems into one operational loop.
This foundational page details the end-to-end architecture for a custom predictive maintenance workflow, from telemetry ingestion and anomaly detection to automated diagnosis and work order routing. It shows how to orchestrate edge AI, digital twins, and CMMS integrations to reduce unplanned downtime by 30-50% and shift from reactive to condition-based operations.
This page explains how to build a multi-agent system that ingests sensor streams, correlates anomalies, and generates prioritized alerts with recommended actions. The architecture focuses on reducing alert fatigue for operators by implementing severity scoring, contextual enrichment, and integration with notification platforms like PagerDuty or Microsoft Teams.
This page covers the implementation of a RAG-based agent that allows technicians to query fragmented documentation repositories using natural language. It details the integration with vector databases, access controls, and how this workflow cuts mean-time-to-repair by providing instant, context-aware procedural guidance at the point of need.
This page outlines a custom diagnostic agent that matches real-time sensor patterns against a curated library of historical failure signatures. It explains the architecture for feature extraction, similarity search, and confidence scoring, which accelerates root cause identification and reduces misdiagnosis in complex industrial systems.
This page details a workflow where AI agents fuse data from vibration, thermal, and acoustic sensors to perform automated root cause analysis. It covers the graph-based reasoning logic, causal inference models, and how this system generates explainable reports that reduce engineering investigation time from hours to minutes.
This page explains how to automate the entire ticket lifecycle, from auto-populating fields based on diagnostic output to intelligently routing work based on technician skill, location, and parts availability. It shows integration patterns with ServiceNow, Maximo, or Jira Service Management to eliminate manual data entry and dispatch delays.
This page covers an orchestration workflow where a diagnostic agent automatically checks ERP and warehouse systems for required spare parts, reserves them, and triggers procurement if stock is low. It demonstrates how to prevent maintenance delays by ensuring part availability is confirmed before a technician is dispatched.
This page details how to build an agent that consumes Remaining Useful Life (RUL) forecasts, operational calendars, and resource constraints to generate optimized maintenance schedules. It shows integration with advanced planning systems to balance uptime goals and labor costs, moving from fixed intervals to dynamic, condition-based planning.
This page explains a workflow that links equipment health scores from predictive models with real-time production data from MES systems. It demonstrates how to build dashboards and alerts that show the direct cost of degrading equipment on throughput and quality, enabling proactive interventions that protect margin.
This page covers a proactive validation workflow where agents monitor sensor drift, cross-check readings against physical models, and flag instruments for recalibration. It details how to implement this guardrail to maintain the trustworthiness of the entire predictive maintenance system and prevent false alerts.
This page outlines an automated workflow that monitors key performance indicators after a repair is completed, comparing them to baseline health to validate fix effectiveness. It covers automated report generation for reliability engineers, closing the loop on maintenance actions and providing data for continuous improvement.
This page details how to build an orchestration layer that continuously feeds real-world sensor data into a digital twin, retrains prognostic models, and validates simulations against actual outcomes. It shows how this live synchronization improves prediction accuracy and supports more reliable 'what-if' scenario planning.
This page explains a workflow where agents analyze failure data, cross-reference it with OEM warranty terms and serial numbers, and automatically assemble and submit validated claim packages. It demonstrates integration with supplier portals to recover costs and reduce the administrative burden on maintenance teams.
This industry-specific page details a custom workflow for robotic welders, paint booths, and conveyor systems in auto manufacturing. It covers integration with PLC networks, vision systems, and how to prioritize alerts to prevent line-stopping failures that cost tens of thousands per minute in lost production.
This page outlines a high-stakes workflow for lithography scanners and etch tools, where agents diagnose complex faults and trigger autonomous recalibration sequences. It emphasizes ultra-low-latency data pipelines, integration with SECS/GEM, and fault containment logic to protect multi-million-dollar wafers in process.
This page covers a GMP-compliant workflow for reactors, centrifuges, and lyophilizers. It details how agents monitor for deviations that could impact product quality, automatically log events in a validated system (e.g., OSIsoft PI), and route alerts to ensure 21 CFR Part 11 compliance throughout the diagnostic process.
This page explains a workflow tailored for hygienic design environments, focusing on pumps, homogenizers, and packaging machines. It covers corrosion detection from imagery, lubrication monitoring, and integration with sanitation schedules to maximize uptime while adhering to strict food safety protocols.
This page details a rugged, edge-based workflow for haul trucks, excavators, and drills operating in remote locations. It explains how to implement offline-capable agents, satellite data backhaul, and predictive models for critical components like hydraulics and drivetrains to avoid catastrophic failures far from service centers.
This page outlines a precision-focused workflow for CNC machines, CMMs, and composite layup tools. It covers monitoring thermal drift, tool wear, and vibration to predict deviations outside tight tolerances, automatically triggering tool changes or calibrations to protect part quality and reduce scrap in high-value aerospace production.
This page explains a workflow that fuses data from inline inspection tools (smart pigs), cathodic protection systems, and satellite-based ground movement detection. It details how agents correlate these signals to predict corrosion hotspots or mechanical stress, automatically generating prioritized dig-site recommendations for integrity engineers.
This page covers a safety-critical workflow for pressurized vessels, heat exchangers, and seal-less pumps. It emphasizes agents trained on process chemistry data to predict fouling, catalyst degradation, and mechanical seal failure, integrating with DCS and SIS systems to recommend interventions before safety incidents occur.
This page details a workflow for remote renewable assets, using SCADA data, vibration analysis, and drone imagery. It explains how agents synthesize these data streams to predict bearing failures and blade defects, optimizing costly crane-based service visits and maximizing energy-based availability payments.
This page outlines a workflow for critical facility infrastructure, where agents monitor CRAC units, UPS systems, and PDUs. It covers the logic for predicting cooling capacity loss or battery failure, and automatically initiating failover sequences or adjusting setpoints to maintain uptime within strict SLA boundaries.
This deep-dive page explains the architecture for a custom RUL estimation pipeline, from feature engineering on time-series data to deploying and serving prognostic models. It covers uncertainty quantification, model retraining triggers, and how to integrate RUL outputs into spare parts and scheduling workflows for maximum financial impact.
This page details a specialized workflow for the most common failure mode in industry. It explains how to implement agents that perform real-time FFT analysis, envelope detection, and pattern matching against fault libraries to diagnose bearing defects early, preventing secondary damage to shafts and housings.
This page covers a workflow that integrates data from on-site oil sensors and lab reports. Agents track viscosity, particle counts, and additive depletion, predicting optimal oil change intervals and diagnosing underlying wear mechanisms in engines and gearboxes, reducing lubricant costs and preventing wear-based failures.
This page explains a workflow for critical motor-driven assets, using agents to analyze current signature analysis (CSA), partial discharge, and temperature data. It details how to predict insulation breakdown and winding faults, scheduling rewinds or replacements during planned outages to avoid unexpected motor burnout.
This page outlines a workflow for complex hydraulic systems, where agents model expected pressure/flow relationships and detect deviations indicative of pump wear, valve stiction, or leaks. It shows how to localize faults to specific circuits and generate instructions for targeted inspection, reducing fluid loss and environmental incidents.
This page details a self-healing workflow where agents detect performance drift (e.g., in a robot's positional accuracy) and automatically execute safe, validated re-calibration routines. It covers the safety interlocks, approval gates for major changes, and integration with control systems to maintain quality without technician intervention.
This page explains a workflow where agents dynamically derate machine operating speeds or loads when health scores degrade, trading short-term throughput for preventing a catastrophic failure. It details the control logic, human-in-the-loop approvals for significant derates, and integration with production scheduling systems.
This page covers a high-availability workflow for critical systems like pumps, fans, or conveyors. Agents monitoring a primary unit's health can automatically and safely start a standby unit, transfer load, and isolate the failing asset, executing a failover sequence in seconds instead of the minutes required for human response.
This page details a workflow for energy-intensive processes like compressors or crushers. Agents predict impending mechanical overstress based on load and temperature trends and automatically initiate a controlled load-shedding sequence to coast the equipment down safely, avoiding a destructive trip or breakdown.
This page outlines a workflow integrating level sensors and predictive models with automated lubrication systems or mobile robots. Agents schedule and execute precise top-ups based on consumption rates, eliminating manual lubrication rounds and preventing failures due to under- or over-lubrication.
This page explains a workflow where agents synthesize thousands of data points into executive-ready reports on asset fleet health, critical alerts, and projected maintenance spend. It covers natural language generation, data visualization automation, and distribution via email or Power BI to keep leadership informed without manual analysis.
This page details a workflow for global operations, where a diagnostic agent's output (fault, part, procedure) is automatically rendered into localized work instructions in the technician's language. It covers integration with translation APIs, technical glossary management, and publishing to mobile CMMS apps.
This page outlines a workflow that allows agents at one facility to query a federated knowledge base of failures and fixes from sister plants. It details the architecture for secure, anonymized data sharing and collaborative reasoning, enabling rare fault diagnosis by leveraging collective experience across a corporate fleet.
This page covers the orchestration layer for intelligent alert routing. It explains how to implement rules based on time-of-day, severity, and personnel roles to escalate alerts through multiple channels (e.g., SMS for critical off-hours, Teams for daytime), ensuring the right person is notified through the right medium without spam.
This page details a workflow where a diagnostic agent outputs a fault code and part number, triggering the automatic assembly of an AR/VR guide from a 3D parts library and procedure database. It shows integration with headsets like HoloLens to guide technicians through complex disassembly, reducing errors and training time.
This page explains a financial planning workflow where agents forecast future part failures and major overhauls based on RUL models and asset age. It demonstrates integration with ERP systems to generate rolling budget forecasts and CAPEX justifications, moving maintenance finance from a historical cost center to a predictive function.
This page details a workflow that automatically tracks the costs avoided (downtime, parts, labor) by each predictive alert and intervention, comparing it to the estimated cost of a reactive failure. It generates business case reports for continuous funding of the predictive program and identifies the highest-value asset classes.
This page outlines a workflow where agents analyze actual failure rates, part consumption, and labor hours against OEM service contract terms. It provides data-driven insights for renegotiation, identifying whether time-and-materials or a different contract structure is more economical based on predictive health insights.
This page covers a workflow that attaches a real-time financial value to each equipment health alert, based on the asset's production rate and margin. It generates forecasts of potential downtime costs and reports on costs avoided through proactive interventions, directly linking maintenance actions to plant P&L impact.
This page details a workflow where agents correlate equipment efficiency (e.g., pump curve deviation) with energy meter data. It identifies assets that are becoming energy hogs due to degradation and recommends maintenance or setpoint adjustments, saving on utility costs while improving reliability.
This page explains a workflow that connects prognostic alerts directly to procurement systems. When a part's RUL falls below a threshold, the agent automatically creates a purchase requisition or triggers a pre-negotiated order with a supplier, ensuring the part arrives just as it is needed, minimizing inventory carrying costs.
This page outlines a workflow for obsolete or long-lead parts. A diagnostic agent, upon identifying a failing part, checks digital inventory for a CAD file, validates it for 3D printing with suitable materials, and submits the job to an on-site or local service bureau printer, drastically reducing lead time for rare components.
This page details a workflow that extends beyond the machine to monitor the health of the supply chain. Agents track lead times, geopolitical news, and alternative supplier status for critical components, alerting procurement if a supplier risk could impact the ability to execute predicted maintenance plans.
This page covers a workflow for companies with mobile or geographically dispersed assets (e.g., rental equipment, ships, trucks). Agents unify telemetry with GPS data, providing a single dashboard of asset health and location, enabling centralized diagnostics and optimized deployment of service crews across regions.
This page explains a critical safety workflow where agents periodically test and verify the functionality of safety interlocks, light curtains, and emergency stops by analyzing expected sensor responses to simulated triggers. It automates compliance logging and alerts on any degradation of these protective systems.
This page details a compliance-focused workflow for scrubbers, filters, and monitoring equipment. Agents predict failures or efficiency drops in emission control systems that could lead to regulatory breaches, triggering maintenance before exceedances occur and automating the generation of compliance reports.
This page outlines a workflow that uses agents to guide technicians through complex LOTO procedures via a digital interface, verifying each step (valve position, energy isolation) against the work order and asset state. It creates an immutable digital audit trail for safety compliance and prevents procedural errors.
This page covers a workflow that automates the management of calibration schedules for critical instrumentation. Agents track certificate expiries, integrate with calibration databases, and automatically generate work orders for recalibration, ensuring measurement integrity for both process control and quality reporting.
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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