Condition monitoring
Detect abnormal machine behaviour early and build toward diagnosis and predictive maintenance.
Artificial intelligence on the edge
The edge-native industrial intelligence platform.
An integrated intelligence stack for existing industrial assets. Controlable combines smart industrial monitoring, existing machine data and local edge AI to detect anomalies, understand machine health and build towards predictive maintenance — without rebuilding your factory.
Close to the machine
Product renderSmall sensor. Bigger perspective.
Vibration sensing at the source. Part of the Controlable edge intelligence system.
Machine-agnostic. On-premise by design. Built for brownfield and mixed-OEM environments, with an optional dedicated private cloud and no long-term platform lock-in.
From concept to industrial edge deployment
Deployment overview
The core platform is operational and entered structured industrial deployment in heavy industry. In parallel, serious discussions are underway with a broader set of industrial companies regarding rollout in other asset- and process-intensive environments.
A phased rollout path has been aligned, with broader expansion in scope.
What makes this new is not only the software layer, but the addition of smart edge devices and local AI that create intelligence layers and datapoints that most factories do not yet have. The same platform foundation is designed to support machine intelligence, packaging line intelligence, inspection, quality monitoring and wider operational insight.
- Core platform operational
- Structured industrial deployment underway
- Phased rollout path aligned
- Built for machine intelligence, inspection and operational intelligence
Potential benefits
Better insight. More informed decisions.
Continuous condition monitoring can help teams understand machine behaviour across a broader asset base, rather than relying on periodic snapshots. The potential value depends on your assets and application.
- No data science team required.
- No complex IT overhaul.
- No long-term platform lock-in.
Earlier detection
Recognise changes in machine behaviour.
Less downtime
Support timely, informed interventions.
Lower energy use
Identify avoidable energy waste.
Longer asset lifespan
Understand and manage degradation.
More predictable costs
Plan with better operational context.
Explore the potential operational value
Earlier detection of machine anomalies, degradation and process deviations
Fewer unplanned stops, emergency interventions and avoidable quality losses
Lower energy consumption through continuous machine and process insight
Improved quality, yield and process stability across existing assets
Extended asset lifetime without large reinvestments or invasive upgrades
Lower cloud and data-handling costs through local processing at the edge
Fast time-to-value through non-invasive deployment and phased rollout
How It Works
Existing machine data. Smart sensing. Local intelligence.
Edge processing · logical functions
- 01
Sources
Machines, sensors, cameras and events
- 02
Edge nodes
Capture data close to the line
- 03
Edge gateway
Process and buffer
- 04
Edge AI runtime
Analyse and detect
- 05
Intelligence layers
Condition, process, energy and packaging
- 06
Central platform
Visualise and act
Example outputs
- Alerts
- Audit trail
- Trend analysis
- Reporting
- Site overview
System architecture / On-premise by designIllustrative architecture. Gateway and runtime are logical functions, not necessarily separate devices. Hardware, hosting, interfaces and output availability are defined per application.
- 01
Edge sensor nodes capture machine behaviour directly at the source. TinyML models analyse this data locally and detect deviations and anomalies in real time, even in environments with limited connectivity.
- 02
Only relevant insights and health indicators are transmitted, keeping bandwidth usage, storage requirements and cloud dependency to a minimum. Dashboards provide clear, actionable views for operations and maintenance teams.
- 03
As representative data accumulates, model development and application-specific validation can support a path toward diagnosis and predictive maintenance. Progression is not automatic.
Role-based views
One platform. Different perspectives.
Relevant information for the people making the decisions. Explore illustrative views for operations, quality, engineering and management.
One shared Controlable platform
Illustrative view
Keep the production floor in focus.
- Line status
- Live alerts
- Throughput
Illustrative view
Review inspection outcomes in context.
- PASS / REVIEW / FAIL
- Image evidence
- Audit trail
Illustrative view
Understand the system behind the signal.
- Edge health
- Configuration
- Model versions
Illustrative view
Connect operational detail to the bigger picture.
- KPIs
- Trends
- Site overview
Illustrative role-based views, not product screenshots. Roles, screens, alerts, audit trails and model-version functions are defined and confirmed for each application.
What We Are Building
An integrated edge-native intelligence stack for existing industrial assets
Controlable is building a new industrial edge intelligence platform: a fully integrated stack that adds new smart sensing, interpretation and intelligence layers to industrial environments where those layers do not yet exist. It is designed to work with existing machines, existing systems and existing factory realities — not to replace them.
Application domains
Condition intelligence
Machine health
Process intelligence
Production flow
Energy intelligence
Usage behaviour
Packaging intelligence
Visual QA and traceability
Shared platform capabilities
Smart edge sensing
Local data capture and sensing
AI models
Machine learning and computer vision
Decision logic
Rules and event processing
Dashboards
Role-based views
Integrations
OT / IT systems
Physical assets
- Machines
- Production lines
- Packaging lines
- Utility assets
- Material handling
A functional overview, not a fixed deployment topology. Capabilities and application scope vary by deployment; these domains do not imply production readiness in every application.
The result is a non-invasive intelligence layer that can grow from single assets to wider production environments — without forcing a central rebuild of existing systems.
Explore the platform approach
The platform combines:
- Existing machine and OEM data where available
- Added smart sensing where critical datapoints are missing
- Multi-sensor edge devices and nodes deployed close to machines, lines and infrastructure
- AI running locally on those devices for anomaly intelligence, inspection, prediction and decision support
- A secure integration and output layer for interpretable machine, process and operational insight
- A learning and model lifecycle layer that improves intelligence over time
- A scalable foundation for machine intelligence, packaging line intelligence, inspection and broader factory insight
Applications
One intelligence platform, multiple industrial applications
Vision-based inspection
Enable edge-based inspection and quality control in operational environments, including packaging, labeling and product verification use cases.
Energy and process intelligence
Turn machine-level behaviour into operational and efficiency insight.
Operational performance
Improve reliability, output and quality across existing assets and production environments.
Application exampleRepeatable application path: packaging line intelligence
An example of how the edge-native platform could support packaging inspection and product verification. Inspection functions, camera positions, SKU rules and decision outputs must be agreed and validated for each application.
Explore the sensor systemSide view
Inspection point A
Codes · OCR · Labels
Inspection view selected per package
Top view
Inspection point B
Top label · Seal · Closure presence
Inspection scope selected per package
Packaging flow
Edge gateway
AI models · SKU rules
PASS / REVIEW / FAIL
Evidence and history
Packaging intelligence console
Illustrative configuration, not a released product interface. Camera count, positions, inspection functions, SKU rules and decision outcomes depend on application-specific development and validation.
Why This Is Different
A new industrial intelligence architecture for real factories
Most industrial software layers try to connect and structure data that already exists. Controlable adds new intelligence where it does not yet exist — through smart edge devices, local AI and a non-invasive deployment model built for real production environments.
Our approach is fundamentally different:
- Machine-agnostic and manufacturer-agnostic
- Non-invasive and independent from existing systems
- Fully on-premise by design, with optional secure dedicated cloud
- Low data transfer and low latency through local processing
- No long-term platform lock-in
- Scalable across brownfield, newer assets and mixed-OEM environments
- Attractive investment profile through phased deployment and direct operational value
This creates a practical path to industrial intelligence for companies where existing approaches remain too heavy, too expensive or too dependent on central cloud and integration projects. Designed for continuous condition monitoring across a broader asset base from the outset, rather than periodic snapshot-based analysis on only a limited number of machines.
Development path
From visibility to industrial intelligence
- Current foundation
Visibility
Make assets visible, measurable and interpretable.
- Current foundation
Anomaly intelligence
Structure deviations, detect what matters and continuously monitor changes in asset condition.
- Development path
Diagnosis
Develop fault-family and component-level understanding with representative data and application-specific validation.
- Development path
Scaled intelligence
Progress toward prognostics, fleet intelligence and broader rollout as the evidence supports it.
The current foundation is visibility, anomaly intelligence and continuous condition monitoring at the edge. Later stages require suitable data, development and validation for each application; progression and predictive outcomes are not guaranteed.
Who This Is For
Built for organisations responsible for critical assets
Our platform is designed for organisations that want to add new intelligence to existing industrial assets without rebuilding their factory stack.
OEMs
Enhancing machines with intelligent monitoring, inspection and service layers without rebuilding the full machine stack.
Manufacturers
Adding practical intelligence across brownfield lines, mixed-OEM environments and existing production assets.
Asset Owners
Improving uptime, visibility and long-term value across installed assets through a non-invasive intelligence layer.
The platform scales from a single machine or production line toward broader factory deployment.
Our Mission
Practical industrial intelligence — under customer control
Our mission is to make industrial intelligence practical and reachable for production environments where existing approaches remain too cloud-heavy, too invasive, too expensive or too complex to deploy at scale.
Too often, factories are expected to modernise by rebuilding systems around central platforms. We believe there is a better path: add new intelligence locally, close to machines, lines and operations, while keeping control over data, deployment and decisions.
By combining smart edge devices, on-device AI and secure platform integration, Controlable enables organisations to add intelligence where it does not yet exist — with limited data transfer, low latency and the flexibility to stay fully on-premise by design.
Team & Credibility
Industrial experience meets edge engineering and applied AI.
The team brings together experienced industrial advisors with a new generation of engineers specialised in hardware, embedded systems, platform development and AI.
Meet the teamAndré GerverFounder & CEO
Architect behind Controlable’s edge-native industrial intelligence strategy, combining decades of experience in industrial innovation, digital strategy, business intelligence, machine learning and AI with a track record of building and leading multiple ventures. Shapes both the strategic direction of the company and the development of its core intelligence stack for machine intelligence, inspection and industrial asset intelligence.
Hans WaningAdvisor, Industrial Reliability & Embedded Systems
Supports Controlable on industrial reliability, maintenance and embedded systems. Combines decades of industrial and technical experience — including maintenance engineering, control technology, sensor-based systems and applied technical education — and helps bridge industrial practice, technical robustness and real-world deployment.
Koen LammersFounding Edge & Platform Engineer
Focused on sensing, electronics and embedded systems. With a background in Computer Engineering and hands-on experience in PCB design, C/C++ development and IoT-oriented prototyping, helps translate industrial intelligence into robust hardware and edge systems for real-world deployment.
Lars GritFounding Edge & Platform Engineer
Working across embedded software, industrial systems and platform development. With a background in Computer Engineering and experience in embedded development, robotics service engineering and industrial automation tooling, helps connect hardware, edge intelligence and the wider software platform into one integrated system.
Bart KapiteijnAI & Vision Engineer
With a background in Applied Mathematics, data science and machine learning, contributes to computer vision and AI model development for real-world inspection and monitoring applications, while continuing to broaden engineering capabilities within Controlable’s wider intelligence platform.
Controlable
Get Involved
Explore pilots, partnerships and early access
We are currently working with launching customers and industrial partners to bring a new edge-native intelligence layer into real industrial environments.
Get in touch to discuss an industrial pilot, a partnership or integration with your existing systems.
The core platform — smart sensing, edge intelligence and software — is operational and moving into structured industrial deployment in heavy industry. A phased rollout path has been aligned, with broader site expansion in scope.
Our focus is now on:
- Field validation in production environments
- Refinement based on operational data
- Scaling towards broader industrial deployment