Two products for everyday property management – usable independently of each other. Central equipment monitoring with AI maintenance forecasting and digital protocols.
All technical systems of all buildings in one dashboard. AI-powered maintenance forecasting detects problems before they occur.
Learn more ↓Capture handover protocols, property inspections and inspection reports digitally. From walkthrough to finished PDF in 7 steps.
Learn more ↓Heating, PV, elevator, fire alarm, ventilation – regardless of manufacturer. Real-time sensor data, automatic alerts and AI-powered predictive maintenance.
SolarEdge, Viessmann, KONE, Siemens, ÖkoFEN, Vaillant, Geze, Fronius, Helios and many more. 6 protocols (REST, Modbus, MQTT, BACnet, ONVIF, KNX).
Email, push to mobile, phone call for critical conditions. Configurable per building and severity level. With cooldown to prevent alert fatigue.
Weekly or monthly AI reports by email. Only systems requiring action – no information overload.
PV, heating, elevator, fire alarm, ventilation, access control, cameras, water, electrical, HVAC, cost management, other.
Temperature, output, vibration, pressure, humidity, fill levels – everything live on the dashboard with history charts.
Residential, commercial, industrial, private. Manage unlimited buildings and sub-buildings in one account.
Predictive maintenance combines IoT sensors with artificial intelligence. Instead of servicing systems on a rigid schedule, the system analyses the actual condition – and warns weeks before a failure occurs.
Modern heating systems, PV inverters, elevators and ventilation units have digital interfaces (REST API, Modbus, BACnet, MQTT). Tentacl connects to these interfaces and reads operating data in real time – temperatures, output values, vibration patterns, fill levels, operating hours. This data flows hourly into the analytics platform and forms the basis for AI-powered condition assessment.
Temperature
Vibration
Output
Fill levels
Each sensor value is compared against the rolling average of the last 14–30 days. The Z-score measures how many standard deviations a value is from the mean. From Z > 2.5 a value is classified as an anomaly. The method detects both sudden spikes (burner failure, short circuit) and gradual drifts (declining heating output over weeks).
Remaining Useful Life per ISO 13381-1:2015. Linear regression on the degradation trend of sensor data calculates when a system will reach the critical threshold. Result: a concrete timeframe in days or operating hours until recommended replacement. The ISO standard defines the entire forecasting process – from data collection through feature extraction to confidence calculation.
A weighted score from four factors: anomaly frequency (30%), degradation trend (30%), system age relative to manufacturer specification (20%) and maintenance history (20%). Result: a failure probability in percent for the next 30 days. From 60% an alert is automatically sent to the property manager.
The statistical analysis provides numbers – the AI provides meaning. Claude AI interprets raw data in the context of the respective system type: What does 820°C combustion temperature mean for a pellet boiler? Is 4.2 mm/s vibration normal for a fan motor? Result: plain-language analysis, prioritised action recommendations and cost estimates for each repair.
Pellet Boiler
15–20 yrs.
Maintenance: annual. Burner, heat exchanger, flue gas values. Pellet silo every 3–6 months.
PV Inverter
10–15 yrs.
Degradation 0.3–0.5%/year. String monitoring detects faulty optimisers early.
Elevator
25–30 yrs.
BetrSichV §16 inspection annually. Door motor, cables, brakes. Vibration analysis of guide rails.
Heat Pump
15–20 yrs.
COP monitoring detects efficiency loss. Refrigerant pressure and compressor temperature monitored.
Fire Alarm System
10–15 yrs.
DIN 14675 inspection quarterly. Individual detector age tracked, replacement planned in time.
Ventilation Unit
15–20 yrs.
Vibration sensors on the motor detect bearing wear. CO2 monitoring shows performance decline.
Scientific background
The methods used are based on established standards in condition monitoring: ISO 13381-1:2015 (Condition Monitoring – Prognostics and RUL calculation), ISO 13379-1:2012 (Data interpretation and anomaly detection) and VDI 2888 (Condition-based maintenance). The Z-score method for anomaly detection in IoT time series is peer-reviewed and documented (MDPI Sensors 2024, Nature Scientific Reports 2023).
According to McKinsey & Company (Prediction at Scale, 2024), predictive maintenance reduces maintenance costs by 20–40% and unplanned downtime by up to 50%. Deloitte quantifies the elimination of unexpected failures at 70–75% and the improvement of equipment reliability at 30–50%. Accenture confirms a reduction in maintenance costs of 30%.
Fraunhofer ISE (Freiburg) develops methods for proactive condition assessment of building systems and has analysed over 70,000 photovoltaic modules since 2012 in the TestLab PV Modules. Fraunhofer ITWM (Kaiserslautern) researches mathematical models for condition monitoring and applies machine learning to predictive maintenance in practice. Fraunhofer IPT (Aachen) has developed the vBox – a retrofitting system for vibration monitoring that can also be added to existing systems. Studies by the US Department of Energy demonstrate a tenfold return on investment and 35–45% shorter downtime through prognostic maintenance.
Sources: ISO 13381-1:2015/2025 · ISO 13379-1:2012 · VDI 2888 · McKinsey – Prediction at Scale (2024) · Deloitte – Predictive Maintenance Report · Accenture · Fraunhofer ISE – Photovoltaics Report 2025 · Fraunhofer ITWM – Predictive Maintenance Machine Learning · Fraunhofer IPT – vBox Retrofitting · U.S. Department of Energy · MDPI Sensors (2024) · Nature Scientific Reports (2023)
Capture handover protocols, property inspections and inspection reports digitally, sign them and export as PDF. Directly on your smartphone.
Master data, rooms, meters, keys, defects, signature, PDF. Ready to use without any training.
Photograph room condition, annotate and embed directly into the protocol. With timestamp.
Professional PDF generated instantly and sent by email to all parties.
Rooms, keys, meter readings, contacts centrally stored. Reusable for future protocols.
Tenant self-disclosure, handover, maintenance report, commercial property. With custom required fields.
On-site capture on smartphone or tablet. Signature directly on the screen.
Monitor systems, document handovers, fulfil operator responsibilities. Fewer emergency calls, more control.
Avoid heating failures, document handovers with legal certainty. No disputes on move-out.
Dashboard and protocol on your phone. No paperwork, no 12 different apps.
2–3 unplanned heating failures per winter. Emergency at the weekend: 800–2,000 EUR. Avoidable with early warning – saving: 5,000–15,000 EUR/year. Plus: avoided rent reductions, less stress, happier owners.
Maintenance automatically documented, due inspections notified in good time.
Operator responsibility in FM. Automatic documentation.
Elevator inspection. Deadlines monitored, warning when overdue.
Fire alarm systems. Inspections documented.
Legionella testing. Automatic reminder.
Electrical testing. Overdue inspections highlighted in red.
Energy Act. Heating efficiency monitored.
Test both products for free and without obligation.