Maintenance AI Implementation and Integration Services

Presidio's Maintenance AI Accelerator professional services help manufacturing organizations design, deploy, and operationalize an agentic, AI-driven predictive maintenance solution on AWS. These services enable plant managers, reliability engineers, and operations teams to move from reactive and siloed maintenance approaches to proactive, business-aligned decision-making that improves uptime, reduces costs, and addresses the growing challenge of technician knowledge loss.

Through this engagement, Presidio architects, implements, and integrates maintenance AI workflows built on AWS technologies including Amazon Bedrock, IoT data services, and event-driven processing pipelines. The solution ingests real-time sensor data alongside CMMS records, maintenance logs, equipment manuals, and operational history to detect anomalies, predict failures, diagnose probable root causes, and recommend or trigger corrective actions. This goes beyond threshold alerting into AI reasoning that supports faster, better-informed maintenance decisions at the plant floor level.

Presidio works with customers to tailor the accelerator to their asset environment and existing systems. The solution is vendor-agnostic, designed to integrate across rotating equipment, CNCs, control systems, and material handling assets regardless of OEM or sensor platform. Where relevant, Presidio's partnership with SUSE and the Losant industrial IoT platform can extend the solution to the edge. Human-in-the-loop controls and governance capabilities are built in to address safety and operational risk concerns in OT environments.

All solution components are deployed and run entirely within the customer's AWS environment. Presidio does not host, manage, or maintain any centralized customer infrastructure outside the customer's account. Customers are responsible for AWS infrastructure costs associated with deployed services within their account.

Key Use Cases:

• Predict asset failures before they cause unplanned downtime using real-time sensor and operational data
• Diagnose maintenance issues and surface probable root cause to reduce mean time to repair
• Prioritize maintenance work orders based on asset criticality, failure risk, and operational goals
• Augment technician decision-making with AI-driven guidance based on equipment history and manuals
• Connect maintenance AI workflows to existing CMMS, ERP, SCADA, and OT systems without wholesale replacement
• Provide plant-level visibility into asset health, maintenance performance, and cost impact