Built on your data. Built with your team.
A robot hesitates. Orders take an unexpected turn. Your team notices, but needs to know why. We build AI models on your data, put them to work on your systems, and refine them alongside the people who use them.
Helm Part of the Paniax Intelligence Platform. Follow a manufacturing plant's activity in real time.
Customer story / Manufacturing
Inside one of Sweden's largest manufacturing companies, the machines thump, whistle, and hiss. You start to follow their rhythm. Then a robot hesitates for a few milliseconds, and the rhythm breaks.
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A factory is easier to understand when you can see how it fits together. Explore the floor in 3D, select a machine, and bring its activity into focus.
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The overview tells you where to look. A closer timeline puts movements, cycles, and warnings alongside each other so the next question can be more precise.
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Production carries on while you investigate. Follow output, uptime, and sequence balance alongside the activity of the machines doing the work.
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Every order joins a longer story. See recorded recipe runs across machines, with a separate workspace for exploring what could come next.
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Sometimes you need the rows behind the picture. Connect tables visually, choose the fields that matter, and inspect the results in the same workspace.
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One hour rarely tells the whole story. Put production measures on a shared timeline and compare the pattern across the period you want to understand.
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Follow a part through its process steps and inspection results. The record brings measurements, missing checks, and limits into one place for review.
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Ask Zora about a production period, then explore the returned data as a table or chart. Keep the timeline nearby as you decide what to investigate next.
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Fine-tuning the platform where the work happens, with the people who know its rhythm.
Product views use recorded and example data from our showcase environment.
No data could leave the factory. We brought our AI platform onto the customer's own systems, connecting the people beside the machines to the detail coming from the lines.
The robot finished its movement. The unusual cycle appeared on screen. We could go down to the millisecond and ask: could we save a little time here? What might that mean over a year?
Our notebooks filled with ideas from the team. Two customer-specific adaptations went into place in the first two days on site. Commissioning was one milestone; the conversations and improvements continue.
What could this look like in your business?Capabilities
In that factory, the data had to stay on site and the detail had to reach the people beside the machines. Your constraints may be different. They shape what we build, where it runs, and how your team uses it.
Orders pick up. Customer behavior shifts. A familiar pattern breaks. A model following your live data can bring that change into view while your team still has time to investigate and act.
They know the rhythms, exceptions, and questions that come up every day. We build around that knowledge, training a model on your data and shaping it to fit the way your people work.
Your team needs answers, and your data has boundaries. We work with those requirements, whether the model belongs on your own servers, an edge device, or in your cloud.
People see the model working and ask what else it could do. We stay involved, from the first look at your data through deployment and the refinements that follow.
How it works
Tell us where the work gets difficult. We look at the data with you, choose a focused use case, and work through what it would take to put a model into daily use.
Walk us through the work. Where do people lose time, miss a signal, or have to make a decision without enough information? We choose a question worth answering together.
We explore the data behind that question and develop a model around it. Your team's experience helps us judge whether the results make sense in the real world.
The model moves onto the systems where it will be used. We work through the practical details with you, so the answer reaches the people who need it.
Conditions change and new ideas arrive. We keep refining the model with your team, using what you learn from working with it.
Example models
A line slows down. A price falls behind the market. An energy reading drifts. Here are four examples of the work a custom model could help your team tackle.
A line is running, but something is slowing it down. Follow the live process to look for the bottleneck and give the team a place to start investigating.
New orders arrive as the market shifts. Use those incoming signals to adjust pricing while the conditions behind the decision are still current.
A customer chooses one product, then another. Learn from that sequence to suggest a next step that reflects how people actually buy.
An energy reading starts to drift from its usual pattern. Compare it with expected consumption and give your team a reason to take a closer look.
Your question may be different. That is where we start.
Tell us about itPricing
Start with the problem your team keeps coming back to. We agree on the scope and usage, then price the work of building, running, and refining your model.
All-inclusive. No setup fees. No infrastructure overhead.
See it work with your data
Start with the work
The pause you hear on the line. The spreadsheet someone checks every morning. The decision that still comes down to guesswork. Send us a few lines about the problem and the data you have. Together, we'll see whether a custom model is a useful next step. Suitable projects can start with a free proof of concept.
Tell us what you're working on
