Most IIoT deployments follow the same arc. Sensors go in. Dashboards go up. Everyone's excited for a month. Then the production team stares at the screens and thinks: "So what do I do with this?"
Nobody told them. Nobody built that part. The data is there, but the path from numbers on a screen to a decision that saves money is still missing.
That's the gap Owl's latest features are built to close.
Owl now has a built in AI agent with direct access to your live process data. Streams, alarm thresholds, calculated values, historical trends. You can ask it questions the way you'd ask a colleague.
"Why did our output drop after the shift change on Tuesday?"
"Which line has been consuming the most energy per kilogram this month?"
"Something seemed off around 2 PM yesterday. Can you check?"
The agent reads your data, finds anomalies, checks alarm history, and gives you a straight answer. No query language. No dashboard configuration step first.
What makes this useful is context. The agent understands it's looking at an extrusion line, not a random spreadsheet. It recognizes what a temperature spike in zone 3 could mean. It knows the difference between normal process variation and something worth investigating. In early testing, we pointed it at a production environment and it correctly identified the type of equipment and process without being told.
Setting up derived metrics used to mean writing configuration files or waiting for a developer. Now you describe what you want: "Track energy consumption per kilogram of output on Line 2, updated every five minutes." Owl builds the calculated stream.
This matters most in the first months after deployment, when the production team is still figuring out which metrics actually drive decisions. Instead of locking in a dashboard spec upfront, you can try a metric for a week. Refine it. Replace it. The cost of experimenting drops to almost nothing.
Every machine connected to Owl adds to a growing your knowledge base. Not only your proprietary production data, but structural patterns. How a 90 kW extruder motor's power consumption profile typically looks. What normal bearing vibration ranges are for a given class of gearbox. What energy per kilogram should look like for a specific polymer.
When a new site goes live, Owl doesn't start from zero. It can suggest what to monitor, what thresholds make sense, and what the early signs of common problems look like. An extruder screw wearing down looks the same whether it's in Vaasa or Stuttgart.
Owl's Device Health capability runs machine learning models against your equipment data continuously in the background. Instead of waiting for something to break or following a calendar that doesn't account for actual condition, the platform flags degradation trends early.
It's not a black box that gives you a single health score. It shows what's changing, how fast, and what similar patterns have led to before. Your maintenance team gets to make informed decisions, not just react to alarms.
Owl's plugin architecture means customer specific features can be built and deployed without touching the core platform. A custom visual component, a report tailored to your process, an integration with your MES or ERP. Combined with AI assisted development, what used to take weeks can often be done in days.
For production teams, this means a system that adapts to how you work. Not the other way around.
The industry has spent years solving the data collection problem. Sensors are cheap. Connectivity works. That part is done.
The bottleneck moved to interpretation. Most deployments stall because the people who know the process don't have time to become data analysts, and the data analysts don't understand the process.
Owl's approach is to make the platform do that translation. When the system can tell you what's worth paying attention to, suggest what to measure next, and learn from every machine it touches, you stop asking "did we get the dashboards we specced" and start asking "what did we catch that we would have missed."
If your production data has been sitting in dashboards that nobody opens anymore, let's talk.
