New platform improves alert accuracy by combining advanced signal intelligence, machine context, historical knowledge, and AI reasoning to help reliability teams focus on the issues that need immediate attention
EDMONTON, AB — June 18, 2026 — Nanoprecise Sci Corp, a pioneer in AI-powered predictive maintenance for industrial assets, today announced the launch of ReliabilityOS, a new agentic AI platform, designed to help industrial teams move from alert overload to faster, more confident reliability decisions.
ReliabilityOS combines advanced signal processing, machine knowledge, historical maintenance context, and AI reasoning to identify the alerts that matter most. The platform analyzes complex sensor patterns, diagnoses likely causes, reasons over asset history and site-specific knowledge, and produces a complete, human-reviewable analysis with supporting evidence.
For maintenance and reliability teams overseeing more connected assets and increasing volumes of machine data, ReliabilityOS is designed to make expert analysis dramatically more efficient. Instead of asking analysts to review every alert from scratch, the platform surfaces the most relevant issues and equips teams with the context, reasoning, and data needed to make decisions faster.
“ReliabilityOS represents the next step in how industrial reliability teams will work with AI,” said Manpreet Singh, CEO of Nanoprecise Sci Corp. “It brings together high-confidence alerts, machine context, human expertise, and customers’ own AI agents into one connected workflow. By integrating seamlessly with systems like CMMS, APM platforms, and enterprise copilots, ReliabilityOS allows every step of the reliability process to be enriched with the right context, so teams can move from detection to decision faster and with greater confidence.”
Built to Find the Alerts That Matter
Industrial environments generate massive volumes of sensor data, alerts, maintenance notes, operating logs, and expert observations. Much of the most valuable knowledge about a machine is difficult to structure: technician notes, historical patterns, recurring operating conditions, colloquial descriptions of past failures, and tribal knowledge held by experienced personnel.
ReliabilityOS is designed to make that knowledge usable.
The platform brings together advanced signal intelligence and contextual AI reasoning. It extracts relevant patterns from machine data, detects abnormal behavior, diagnoses likely causes, and highlights areas of concern. It then reasons over machine history, maintenance context, operating knowledge, and available site-specific information to prioritize alerts and generate evidence-backed analysis.
Even when complete machine history is unavailable, ReliabilityOS can still identify abnormal patterns, explain areas of concern, and provide analysts with the supporting data needed for review.
Purpose-Built AI Where It Matters Most
ReliabilityOS builds on Nanoprecise’s previously released Condition Intelligence product, which had been a big leap forward in using AI algorithms to surface critical alerts and triaging for industrial assets. With ReliabilityOS, Nanoprecise extends that foundation into a broader agentic platform that adds contextual reasoning, workflow intelligence, and seamless integration with enterprise reliability systems.
At the core of the platform are a few critical capabilities:
- Condition Intelligence detects abnormal machine behavior, extracts meaningful patterns from sensor data, identifies early indicators of degradation, and prioritizes alerts based on severity and relevance.
- Contextual Investigation brings together asset history, maintenance notes, operating conditions, tribal knowledge, and available machine context to explain why an alert matters.
- Diagnostic Reasoning connects signal patterns with likely fault modes, contributing factors, and recommended next steps.
- Workflow Enrichment allows reliability insights to be enriched through existing tools such as CMMS, APM platforms, asset hierarchies, and customer AI agents.
Behind these core capabilities, additional AI agents help gather supporting data, summarize findings, enrich reports, and prepare the information analysts need to review an issue quickly and confidently.
The result is not just another alert. ReliabilityOS delivers a structured, evidence-backed analysis that helps human reviewers and customer-side AI agents participate in the reliability workflow, understand what changed, why it matters, what data supports the finding, and what action should be considered.
Designed to Make Reliability Teams More Efficient
ReliabilityOS is built to augment reliability engineers, vibration analysts, and maintenance teams by reducing the time spent reviewing low-priority alerts and manually gathering context.
As sensor deployments expand and reliability programs scale, teams are being asked to monitor more assets, interpret more data, and respond faster. ReliabilityOS helps them do that by embedding machine knowledge, historical context, and AI-assisted reasoning directly into the reliability workflow.
Analysts remain in control of the final decision, but they are no longer starting from a raw alert. ReliabilityOS prepares the investigation, surfaces the likely causes, organizes the supporting data, and accelerates the path from detection to action.
Open by Design for the Enterprise AI Stack
ReliabilityOS is designed to work inside the customer’s existing reliability and enterprise AI ecosystem. Through an open architecture, including support for Model Context Protocol-based connections, the platform allows reliability insights, asset health information, diagnostic reasoning, and recommended actions to be shared with customer copilots, agentic workflows, CMMS platforms, APM systems, and other enterprise tools.
This creates a connected reliability workflow where Nanoprecise agents, customer-owned agents, and human reviewers can all participate. Customer workflows can query asset health, trigger investigations, enrich analysis with maintenance or operational context, escalate issues, or connect recommendations directly to work management systems.
Rather than operating as a closed application, ReliabilityOS is designed to become an intelligent reliability layer across the customer’s broader digital and AI ecosystem.
AI-First Strategy and New Leadership
ReliabilityOS is the customer-facing expression of Nanoprecise’s broader evolution as an AI-first industrial technology company. AI has been central to Nanoprecise since its founding; ReliabilityOS represents the next stage of that journey, applying agentic systems, contextual reasoning, and automation to improve how reliability work is performed at scale.
To lead this next chapter, Nanoprecise has named Matthias Winkeler as Head of AI. Winkeler has been with Nanoprecise for more than three and a half years and played a central role in shaping ReliabilityOS from concept to launch. He brings more than 13 years of experience in predictive maintenance and industrial analytics, along with Category III certification in vibration analysis.
“ReliabilityOS was built around a simple but important idea: industrial AI must understand more than sensor signals alone,” said Matthias Winkeler, Head of AI at Nanoprecise Sci Corp. “To improve decision-making, AI needs to connect signal patterns with machine behavior, maintenance history, operating context, and the practical knowledge that reliability teams use every day. Our goal is to give analysts a complete, evidence-backed view of the issue so they can focus their expertise where it creates the most value.”