Real-time sensor intelligence, cloud-based analytics, and machine learning–driven predictive maintenance for farms, industrial assets, and critical infrastructure.
Our end-to-end stack handles collection, transport, storage, analytics, and automated response — all vendor-agnostic and fully customizable.
Sensors capture any electrically measurable variable — temperature, pressure, humidity, current, level, CO₂, pH, and more — in real time.
Data travels via LoRa WAN, 3G/4G LTE/5G, NB-IoT, or Wi-Fi to secure cloud ingestion endpoints, using MQTT over encrypted channels.
All time-series data lands in the cloud broker with AI-ready storage. Policies, alarms, and historical analytics are available from day one.
ML models learn normal operating behavior and flag deviations before failures occur — turning reactive maintenance into proactive intelligence.
Actuator commands are sent automatically or on demand — shutting down circuits, starting pumps, triggering alerts via SMS, email, Telegram, or Slack.
Customizable dashboards give operations teams a live, historical, and predictive view of all assets from any device, anywhere.
We deliver vendor-agnostic, cloud-native IoT solutions across agriculture, energy, industrial, and critical infrastructure sectors.
Manage climate, irrigation, nutrients, and crop steering with data-driven insights that maximize yield and minimize waste across every growth stage.
24/7 bird health tracking with real-time air pressure, humidity, temperature, CO₂, weight gain, and water intake — interpreted by specialist consultants.
24/7 current monitoring with hall-effect sensors — no circuit interruption. Prevent overcurrent, estimate costs, and automate load shedding.
Real-time parameter monitoring for medium/high voltage transformers. Predictive maintenance reduces downtime, OPEX, and distribution penalties drastically.
Servers, cold storage, generators, pharma refrigeration, tanks, safety systems — vendor-agnostic visibility with instant alerts and remote control.
Measure real-time cellular signal strength and quality with live mapping, historical data, and customized dashboards for network operations teams.
Our platform goes beyond monitoring. By continuously learning the normal operating fingerprint of each asset, cloud-based ML models can detect anomalies days or weeks before a failure occurs — transforming costly reactive repairs into planned, low-impact interventions.
The platform ingests historical time-series data to build a statistical model of each asset's typical operating envelope — accounting for load cycles, seasonality, and environmental conditions.
Real-time sensor streams are continuously scored against the learned baseline. Statistical drift, vibration anomalies, thermal runaway signatures, and efficiency degradation are flagged the moment they emerge.
Regression models project the expected remaining service life of components, enabling maintenance teams to schedule interventions at the optimal time — not too early, never too late.
ML-prioritized alerts suppress noise and surface only actionable anomalies, delivered via SMS, email, Telegram, or Slack — with contextual severity scoring and recommended actions.
All model training, inference, and storage run on our aaS cloud broker. No local ML infrastructure to manage. New models deploy automatically as more asset data accumulates.
As operating conditions evolve, models retrain on fresh data. Every model version is tracked, auditable, and reversible — providing a complete ML lineage for regulated industries.
Sensors connect over the network that best fits their environment — from sub-GHz LPWAN in remote fields to 5G for high-bandwidth industrial assets.
From remote water tanks to high-voltage transformer networks, our solutions operate in the harshest and most demanding environments.
Every design decision prioritizes uptime, accuracy, and usability for the people who depend on the data every day.
Uninterrupted view of every asset and its evolution over time — from commissioning to end of life.
Dashboards work on tablets, smartphones, and laptops. Information follows your team, not the other way around.
Increase observation accuracy by upgrading sensor quality or sampling frequency — no platform changes required.
Automated data pipelines eliminate transcription and observation errors that plague manual monitoring programs.
Predictive ML models shift spending from reactive repair to planned maintenance — typically cutting costs by 30–40%.
Customizable dashboards surface cross-variable relationships in real time — no data scientist required for day-to-day ops.
Works with any sensor brand, any connectivity protocol, and any cloud region — protecting your investment as technology evolves.
If it can be measured electrically, we can monitor it. Our platform adapts to your specific operational requirements.
Request a demo and our team will walk you through a live deployment scenario tailored to your industry and use case.
No commitment required. Response within 24 hours.
Or reach us directly at sales@theiotcompany.net