New platform improves alert accuracy through advanced signal intelligence, machine context, historical knowledge, and AI reasoning to empower reliability teams.

EDMONTON, AB, CANADA, June 25, 2026 /EINPresswire.com/ -- Nanoprecise, 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. It analyzes sensor patterns, diagnoses likely causes, reasons over asset history and site-specific knowledge, and produces a complete, human-reviewable analysis with supporting evidence — so reliability teams can make faster decisions as they oversee growing volumes of machine data.

“ReliabilityOS represents the next step in how industrial reliability teams will work with AI,” said Manpreet Singh, CEO of Nanoprecise. “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 combines advanced signal intelligence and contextual AI reasoning to extract relevant patterns from machine data, detect abnormal behavior, diagnose likely causes, and highlight areas of concern — then reasons over machine history, maintenance context, and site-specific information to prioritize alerts and generate evidence-backed analysis. Even when complete machine history is unavailable, ReliabilityOS can still identify abnormal patterns and provide analysts with the supporting data needed for review.

Purpose-Built AI Where It Matters Most
ReliabilityOS builds on Nanoprecise’s Condition Intelligence product, which marked a major step in using AI to surface critical alerts for industrial assets. ReliabilityOS 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 capabilities, additional AI agents gather supporting data, summarize findings, and prepare what analysts need to review issues quickly. The result is a structured, evidence-backed analysis that helps human reviewers and customer-side AI agents understand what changed, why it matters, and what action to consider.

Designed to Make Reliability Teams More Efficient
ReliabilityOS is built to augment reliability engineers, vibration analysts, and maintenance teams as sensor deployments expand and programs scale. Rather than starting from a raw alert, analysts get an investigation that has already been prepared — likely causes surfaced, supporting data organized, and the path from detection to action accelerated. Analysts remain in control of the final decision; ReliabilityOS makes their expertise dramatically more efficient.

Open by Design for the Enterprise AI Stack
ReliabilityOS works inside the customer’s existing reliability and enterprise AI ecosystem. Through an open architecture — including support for Model Context Protocol–based connections — the platform shares reliability insights, asset health, diagnostic reasoning, and recommended actions with customer copilots, agentic workflows, CMMS platforms, APM systems, and other enterprise tools.
The result is a connected reliability workflow where Nanoprecise agents, customer-owned agents, and human reviewers can all participate — querying asset health, triggering investigations, enriching analysis with maintenance or operational context, escalating issues, or connecting recommendations directly to work management systems.

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. “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.”

About Nanoprecise Sci Corp
Nanoprecise Sci Corp is an AI-driven prescriptive maintenance company that helps industrial operators detect faults early, prevent unplanned downtime, and extend asset life. Its ReliabilityOS platform combines purpose-built sensors, edge computing, advanced signal intelligence, and agentic AI to deliver higher-confidence alerts, diagnostic insight, and prescriptive action across rotating equipment in manufacturing, mining, oil and gas, power, water, and pulp and paper. Learn more at nanoprecise.io.

Christian Keon
Nanoprecise Sci Corp
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