If you’ve been following the Autonomous Maintenance Chronicles, you know we’ve spent the past few months building — first the workflows, then the Agent behind them.
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In Episode 4, we gave you a glimpse into that journey: how over 1,000 cycles from fault discovery to investigation, implementation, and verification shaped what the Agent sees, filters, and decides. Today, we move from concept to capability.
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Omi — our Reliability Engineering Agent — is now live and available for use.
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You can head to https://xempla.ai/ai-for-reliability-engineers and sign up today.
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This is more than a launch. It’s a practical leap forward in making autonomous maintenance real and accessible. No long onboarding. No complex integrations. Just plug in simulated data or actual site data of your own, and see what happens when AI does the heavy lifting — triaging faults, surfacing signals, and guiding engineers with clarity and context.

Let’s dive in.

Meet Omi: The Reliability Engineering Agent

Omi is the first of many agents in Xempla’s multi-agent ecosystem, and it is purpose-built for reliability engineers on the frontlines of maintenance operations.
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At its core, Omi connects, contextualizes, and activates your data, enabling it to cut through the clutter and focus on what truly matters. It’s not just another layer of automation — it’s an intelligent assistant that helps engineers shift from reactive firefighting to proactive, insight-led decision-making.
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Here’s what Omi does:

• Fault Triage: Prioritizes what needs attention and what doesn’t.
• Signal Extraction: Identifies the real issues hidden in a sea of alerts.
• Insight Delivery: Surfaces what’s happening, why, and what to do next.
• Decision Support: Provides contextual recommendations backed by past patterns, site constraints, and operational context.
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Every output comes with justifications and confidence scores, so users know exactly what the agent sees, why it recommends what it does, and how sure it is — building the transparency and trust that adoption depends on.

Experience Omi in Action

Whether you're dealing with HVAC systems, solar PV assets, or other equipment, you can now explore how Omi works using simulated data across a variety of asset classes. It’s the fastest way to test the waters — and experience how AI Agents like Omi can support your team’s decision-making in the real world.
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More importantly, it removes the traditional blockers to innovation: complexity, cost, and time. Now, anyone can validate impact quickly and take the first step toward Autonomous Maintenance, with zero risk.
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Watch this quick guided tour and get started with OMI

Why This Matters

This launch is about a shift in how reliability engineering gets done to drive outcomes instead of just managing tasks. By reducing noise, highlighting what matters, and offering contextual recommendations, Omi will raise the bar for what reliability engineering should look like going forward.
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What excites us most? Empowering engineers to thrive in an AI-first world — with more clarity, more leverage, and a new sense of possibility in their work. And we’re already seeing that happen. Early adopters are transforming how they operate with less firefighting, more foresight. Now, you have the chance to do the same.

What’s Next

Omi is the first of six agents in our multi-agent ecosystem, each designed to handle specific tasks and decisions across the maintenance lifecycle. Together, they’ll drive a new standard of autonomous operations.
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In the coming weeks, we’ll share:

• Deeper insights into how Omi is being used in the field
• Customer stories and measurable outcomes
• A look at the next agent in line and how it complements Omi

This journey is about building momentum, one decision at a time. Autonomous maintenance isn’t a vision for the distant future anymore. It’s real, it’s practical, and it starts here.

From Dashboards to Outcomes: Getting Started with OMI

OMI presents a 10x boost for reliability engineers — not just in productivity, but in the kind of value and results they can now unlock.
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At this stage, you can start with simulated data existing within the Xempla database. See how it triages faults, delivers insights, and helps you make better decisions with confidence.

Give it a spin here: https://xempla.ai/ai-for-reliability-engineers

Share your feedback, your use cases, or anything you're curious about.

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Loving it so far? Stay tuned for the next episode of Autonomous Maintenance Chronicles!