CORE PLATFORM
Machine Learning & Anomaly Detection
Time-series AI for predictive operations.
From predictive maintenance on rotating machinery to demand forecasting in supply chains, Machine Learning and Anomaly Detection let you spot problems before they cost you money - and opportunities before competitors do. We build, train, and deploy production-grade models tuned to your industrial context.
Our ML practice covers the full lifecycle: data acquisition and labelling, feature engineering, model training and validation, deployment to cloud or edge, and continuous monitoring for model drift. Every model we ship comes with explainability tooling and performance baselines.
Anomaly detection is not just about flagging outliers - it is about understanding the normal operating envelope of your process and surfacing deviations that matter, filtering out the noise that does not.
Capabilities
Predictive Maintenance
Vibration analysis, acoustic monitoring, motor current signature analysis - catching failures before they happen.
Demand Forecasting
Time-series models for supply chain, energy, and inventory - turning historical patterns into forward-looking intelligence.
Anomaly Detection
Real-time outlier identification across sensor streams - surfacing deviations that matter while filtering noise.
Pattern Recognition
Cyclical event detection, process drift identification - understanding what normal looks like so you can spot what is not.
Model Training Pipelines
Custom models with MLflow / Kubeflow orchestration - reproducible, versioned, and auditable from experiment to production.
Edge Inference
Optimised deployment to factory-floor hardware - running models where the data lives, not in a distant data centre.
Technical Stack
Common questions
What does Tavlo's machine learning service cover?
The full lifecycle: data acquisition and labelling, feature engineering, model training and validation, deployment to cloud or edge, and continuous monitoring for model drift. Every model we ship comes with explainability tooling and performance baselines.
How does predictive maintenance actually detect failures early?
Through vibration analysis, acoustic monitoring and motor current signature analysis on rotating machinery - time-series models learn the normal operating envelope of your equipment and flag developing faults before they cause downtime.
Is anomaly detection just outlier flagging?
No. It is about understanding the normal operating envelope of your process and surfacing the deviations that matter while filtering out the noise that does not - across live sensor streams, in real time.
Do you have a ready-made product for predictive maintenance?
Yes - Tavlo Industrial AI: predictive quality and maintenance for production lines, running fully on-premises on edge hardware, with named fault types and guarded automatic responses. Working systems are running; pilot partners are welcome.
How do you keep models reproducible and auditable?
With model training pipelines orchestrated on MLflow and Kubeflow - reproducible, versioned and auditable from experiment to production, deployable to Azure ML, AWS SageMaker or factory-floor edge hardware via ONNX.
How do we start a project with Tavlo?
Write to contact@tavlo.tech and describe the process or line you want to improve. We respond within two business days and typically start with a 30-minute call. There is no public price list - engagements begin with a conversation, and implementation details are shared under NDA.
Your next intelligent system starts here.
Whether you're in DACH or beyond, we're ready to deliver.