Welcome to Episode 4 of the Autonomous Maintenance Chronicles — a series where we dissect what it takes to transition from conventional to data-led, autonomous maintenance systems.
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In the last episode, we unpacked the idea of a predictable works pipeline — why workflows driven by alerts (rather than outcomes) tend to collapse, and what it takes to bring structure to that chaos.
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This episode was supposed to coincide with the launch of Omi — the first of six intelligent agents that will eventually power our multi-agent ecosystem for Autonomous Maintenance.
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But as it often happens in product journeys, the last mile deserves a little more time. We’re about a week away from rolling Omi out, and it’ll be worth the wait. You'll soon be able to use simulated data or upload your own to see how Omi filters noise, extracts meaningful signals, and pushes decisions through the DIIV framework.
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In the meantime, as promised in Episode 1, this series is also about sharing our learnings as we build our product. So in today’s post, we’re taking you behind the scenes to show where Omi is already adding real value, what we’re still working to improve, and what it all means for the journey to Autonomous Maintenance.

Internal Testing & Learnings: 1000+ DIIV Cycles Later

Over the past few weeks, we’ve put Omi through 1,000+ DIIV cycles to understand how it performs in real-world scenarios — triaging noise, surfacing signals, and supporting human judgment. Here’s what we’ve learned:
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✅ Signal Clarity Over Volume

Omi filters out low-priority cases that don’t require attention, helping teams focus on what truly matters. This filtering alone has been a massive productivity boost for environments flooded with FDD alerts.
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🧠 Contextual Recall of Constraints

Omi surfaces key events and constraints from site history that typically get lost in notes or memory. This ability to bring back relevant context improves decisions and reduces the need to re-investigate known issues.
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🚀 Real-World Readiness

The agent shows strong promise in triaging noise, prioritizing cases, augmenting human judgment, and developing critical capabilities as we move toward a more autonomous O&M future.

Where We’re Improving

While Omi’s early performance is promising, there are still areas we’re actively refining:
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🧩 First Principles Thinking

Omi demonstrates thoughtful reasoning in most scenarios, but not consistently enough just yet. Right now, we’re seeing ~80% confidence levels — strong, but not production-grade. Our next goal is to push beyond 92%, using reinforcement learning and data from 1000+ DIIV cycles.
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🤖 Human-Like Tone & Personality

Omi’s responses are clear and accurate, but they lack personality. If agents are to be true co-workers, they need to feel more collaborative and less machine-like. We're experimenting with ways to bring more human tone and nuance into how Omi communicates.

User Expectations vs Reality

Our internal tests and early user feedback have surfaced an important insight:
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💡 High Expectations Set by Popular AI Interfaces

With the rise of Claude, ChatGPT, and Gemini, users now expect decision-support agents to offer a similarly polished experience — fast, intuitive, and conversational. That’s the benchmark.
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⚖️ A Double-Edged Sword

While this pushes us to raise the UX bar, it can also distract from what matters most: domain depth and accuracy. Our focus remains on building agents that solve real O&M problems, not just mimic chatbots.

What’s Coming in Episode 5

Episode 5 will mark the official launch of Omi, giving you the chance to test its capabilities firsthand. You’ll experience how it applies the DIIV framework to triage alerts, filter out noise, and support decision-making in real time.
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We’ll also share valuable user feedback, showcasing both successes and areas for improvement. Plus, we’ll provide detailed product walkthroughs to ensure you fully understand Omi’s features.

Conclusion: A New Era of Maintenance is Here

Autonomous Maintenance isn’t about building better dashboards — it’s about creating intelligent systems that think, learn, and enhance decision-making. Omi is just the beginning, and the true potential lies in systems that evolve and adapt over time.
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As we refine our agents, we invite you to collaborate. Whether you're building, evaluating, or experimenting, let’s share insights and learn together. The path to full autonomy is just beginning, and together, we can shape its future. Let’s keep pushing the boundaries.