IoT with your existing sensors
Senseioty collects the streams from the IoT sensors, PLCs and machine data you already produce. Where new measurements are needed, targeted sensors are added — you don't start from scratch.
Use case · Operational efficiency & predictive maintenance
Predict failures before they happen.
Machine learning on data streams, IoT included, to predict failures, detect fraud and automatically generate work reports. A mobile app guides field operators, step by step, with human-in-the-loop validation.
In short
What FlairBit's operational efficiency is.
FlairBit's operational efficiency solution uses machine learning algorithms on data streams, IoT included, to predict failures before they occur, detect misuse and fraud, and automatically generate work reports. A mobile app guides field operators step by step, with human-in-the-loop validation of the reports.
The problem
Unplanned downtime costs money. Paper slows everything down.
Sudden failures stop production and cost time and money. Planning installations and maintenance, activating warranties and producing work reports by hand is slow and scattered — and without continuous data it's hard to enable advanced service models.
- Unplanned downtime. Sudden failures stop production and drive costs up.
- Manual processes. Planning interventions, activating warranties and writing reports by hand is slow and scatters data.
- Service held back. Without continuous data it's hard to enable models like Appliance as a Service.
Line 3 · sudden failure⚠ stopped
Work report · on paper⚠ to type up
Warranty · to activate⚠ late
Downtime cost · estimate€€€ / hour
The FlairBit solution
From data to prediction, with Senseioty and Stratum AI.
The Senseioty IoT platform collects the data streams and Stratum AI agents turn them into predictions, alerts and reports. Three capabilities working together, from the field to the archive.
01 · Predictive maintenance
It spots the anomaly and predicts the failure.
Machine learning algorithms analyze IoT streams continuously, learn each machine's normal behavior and identify anomalies. When risk rises, they suggest a proactive intervention — before the stop, not after.
- ML on IoT streams. Continuous analysis of vibration, temperature, consumption and machine cycles.
- Anomalies early. Deviations from normal behavior become a signal before the failure.
- Proactive interventions. The system suggests what to do and when, reducing downtime.
Status · bearing line 3
ML · anomaly threshold exceeded
Failure likely within 6 days — replace bearing.proactive intervention · scheduled
02 · Mobile app for operators
It guides the field operator, step by step.
A mobile app accompanies the technician during the intervention: guided checklist, photo documentation and automatic report generation. Every report goes through human-in-the-loop validation, so the operator confirms before it's archived.
- Step-by-step guidance. Checklists and procedures right on the device, in the field.
- Photo documentation. Photos and notes attached to the intervention, paper-free.
- Human-in-the-loop validation. The report is auto-generated, but the operator confirms or corrects it.
1 · Intervention checklist
2 · Photos + notes in the field
3 · Report generated · to validateBearing replacement line 3 — 42 min. The operator confirms before archiving.
03 · Fraud detection and automatic reports
It uncovers misuse, archives everything on its own.
Usage-pattern monitoring detects tampering and misuse, while automatic report generation combines machine data and the technician's observations. The result is a complete digital archive, ready for warranties and compliance.
- Fraud detection. Anomalous usage patterns flag tampering and misuse.
- Automatic reports. Machine data and the technician's observations in a single document.
- Digital archive. Contracts and work reports always tracked and searchable.
Usage · machine #A12within norm
Usage · machine #B07⚠ anomalous pattern
Report #1043 · auto-generatedarchived
Warranty · activationautomatic
Benefits
Less downtime, more control, lower costs.
The effects of predictive maintenance on operations and on the service model.
Operating costs
−18%
Less downtime, fraud detection and leaner processes: savings of up to 18% on operations.
Machine downtime
Reduced
Predictive maintenance and proactive interventions: you act before the failure, not after.
Control & compliance
End-to-end
Traceability and compliance along the entire value chain, from field to digital archive.
Paper
Zeroed
Contracts and work reports archived digitally: instant search, goodbye paper.
Typical effects of FlairBit implementations on Senseioty + Stratum AI; results vary by machine fleet and processes.
How it integrates
It starts from the data you already have. Legacy included.
The solution draws on your existing IoT sensors, PLCs and machine data through Senseioty, and plugs into custom management software, dated ERPs and legacy systems. Multi-model GenAI, available in private hosting, GDPR and AI Act compliant.
Legacy integration
Custom connectors and adapters for dated ERPs and bespoke management software. It draws on your existing information system for warranties, records and orders, without rebuilding it.
Multi-model GenAI, private too
Automatic selection of the private or public model for each request, based on security, confidentiality and performance. Available in private hosting too, with no lock-in.
GDPR & AI Act
Governance, traceability and differentiated access designed into the platform. Compliance by design, not bolted on afterwards.
From selling the product to selling the service. Appliance as a Service, powered by data.
Frequently asked questions
The questions we get most often.
Do I need to install new sensors?
Not necessarily. The solution draws on the data streams you already produce — existing IoT sensors, PLCs, machine data and management software — through the Senseioty platform. Where new measurements are needed, targeted sensors are added, but the starting point is the data you already have.
How does it predict failures?
Machine learning algorithms continuously analyze data streams, IoT included, and learn each machine's normal behavior. When parameters start to drift toward an anomalous pattern, the system flags the anomaly and estimates the failure risk, suggesting a proactive intervention before the stop.
What is servitization or Appliance as a Service?
It's the shift from selling the product to selling the service: the customer pays for the machine's use or performance, not the machine itself. Predictive maintenance and continuous data monitoring make this model sustainable, because they let you guarantee uptime and service levels.
How do field operators use the solution?
Through a mobile app that guides them step by step through the intervention: checklist, photo documentation and automatic report generation. Reports go through human-in-the-loop validation, so the operator confirms or corrects before archiving.
Let's see it on your machine fleet