AI Showcase · AIoT innovation lab for connected products
When connected devices start to understand their data.
Discover how artificial intelligence explains device data, detects anomalies, supports users, and turns connected hardware into a smarter product experience.
From sensor signal to understandable action: the chain every demonstrator on this page follows.
Framing
Intelligence does not live in the model alone.
A product is not intelligent just because a chat window was bolted on. Intelligence emerges from how the whole system works together, and that is exactly what we build.
We do not treat AI in isolation, but as part of a complete connected-product system: device, connection, app, backend, data, and people.
Module A
interactive prototypeExplain device data clearly.
Devices produce readings, error codes, and states that are hard for users to understand. The AI feature translates them into natural language and explains them in context. Pick a device state of the fictional M40 monitor:
Inputs
Telemetry, error codes, operating state, connection quality, and product documentation.
Business value
Fewer support tickets, faster problem solving, higher product acceptance.
Architecture
Device → BLE → App → context prep → language model → checked output
Limits and safety
Explanations are recommendations, not diagnoses. Critical states follow fixed rules, not the model.
Module B
interactive prototypeDetect anomalies and faults early.
Devices often drift for weeks without any single reading triggering an alarm. The comparison shows why pattern recognition in time series reacts earlier than a fixed threshold:
Inputs
Time series from vibration, temperature, runtime, and load profiles across the device fleet.
Business value
Reduce downtime, make maintenance plannable, lower warranty and service cost.
Architecture
Device → telemetry → cloud pipeline → anomaly model → prioritization → service dashboard
Limits and safety
No model predicts every fault with certainty. Detections are signals with confidence that a human evaluates.
Module C
concept demoAI-assisted product support with device context.
Classic support systems know the documentation, but not the customer’s device. The difference shows on the same request:
Inputs
Product docs, current device state, user description, known fault patterns, support history.
Business value
Fewer follow-up questions, more consistent answers, real relief for first-level support.
Architecture
Device state + docs → retrieve relevant content → language model → answer with sources and escalation path
Limits and safety
Answers rely on approved sources. When uncertain, the assistant hands off to a human instead of guessing.
Architecture decision
Edge, app, cloud, or hybrid: where should AI compute?
The same AI feature can run on the device, in the app, in the cloud, or distributed. The right answer depends on the product, and it shapes privacy, cost, and user experience.
This trade-off is an architecture decision, not a model question. In the Connected Product Blueprint it is documented and justified.
Responsible AI
AI that users and companies can trust.
Good AI shows not only in what it does, but also in when it does not act. These mechanisms are part of product quality for us, not ethics folklore:
Data minimization
Collect and transmit only the data the feature truly needs. Prefer Edge over Cloud where it works.
Make uncertainty visible
Every statement carries its confidence. Users see how sure the system is, instead of a facade of certainty.
Human approvals
Critical actions need confirmation. Human in the loop is an architecture rule, not a slogan.
Safe fallbacks
If the model fails or is uncertain, defined rule behavior takes over. The product stays usable.
Monitoring in production
Model quality is measured after launch. Wrong decisions are captured and fed back into improvement.
EU AI Act in view
Regulatory requirements are framed early so compliance is not a retrofit project.
From experiment to product
A convincing AI prototype is not yet a reliable product.
The path from demo to a durable product feature follows ten steps for us:
Let’s find which AI feature is truly useful for your product.
The Connected Product Blueprint evaluates your architecture, your data, and your AI opportunities in 7 to 10 working days, with a prioritized roadmap instead of buzzwords.