Custom AI and data engineering solutions for enterprise-level processing of IoT sensor data

Managing MQTT, CoAP, Modbus, and proprietary protocols across hardware ecosystems slows down integration, data normalization, and pipeline consistency.

Massive, continuous telemetry streams overwhelm poorly designed ingestion layers and lead to bloated or unsustainable storage strategies.

Use cases like predictive maintenance, safety alerts, and control systems require sub-second response times, which many platforms can’t reliably support.

Raw sensor data often arrives noisy, incomplete, or with schema inconsistencies, which can disrupt downstream analytics and model performance.

As more devices come online, poorly partitioned, non-resilient systems fail under throughput pressure or become cost-prohibitive to scale.

Without streaming engines and in-memory compute, teams miss the opportunity for live monitoring, alerting, and contextual decisioning.

IoT systems often have weak points in device authentication, unsecured data transmission, or unmonitored edge/cloud pipelines — creating high risk.

Lack of unified telemetry, traceable pipelines, and anomaly detection makes it hard to debug issues or optimize data flow across the stack.