DehazeLabs builds industrial IoT platforms, predictive maintenance systems, computer vision for quality inspection, and agentic process automation for manufacturing and industrial operations. Production AI that runs in physical environments — not demos on clean data.
Manufacturing is where AI meets the hardest version of the physical world: legacy equipment, heterogeneous sensors, harsh environments, and zero tolerance for false positives on safety-critical systems. The data engineering problem alone — normalizing sensor data from PLCs, SCADA systems, and IoT sensors across equipment generations and vendors — stops most AI initiatives before they start.
DehazeLabs applies the same telemetry normalization and reasoning layer architecture we use for data center operators to the factory floor. Heterogeneous sensor fragmentation is a solved problem for us. The production AI systems that result from that foundation — predictive maintenance, vision inspection, agentic process control — operate on data that's actually trustworthy.
Sensor ingestion across PLCs, OPC-UA, MQTT, Modbus, and proprietary protocols. Unified operational schema normalization. Sensor health validation and data quality monitoring. The foundation that all other manufacturing AI systems need to actually work.
Failure prediction models trained on equipment telemetry and maintenance history. Anomaly detection for precursor signals before visible failure modes. Maintenance scheduling optimization that balances predicted failure probability against production schedule constraints.
Defect detection and classification at production line speed. Models trained on annotated examples from your specific product and defect types. Integration with line control systems for real-time pass/fail signaling. Drift monitoring as materials and product specs change. For manual inspection lines, we start with a 4-week lighthouse pilot — image capture, labeled dataset, baseline model, and operator review dashboard — before committing to production camera infrastructure. See also our production CV deployment breakdown.
Claude-powered reasoning layers that interpret operational telemetry, correlate signals across equipment, and execute autonomous responses — parameter adjustments, maintenance alerts, dispatch signals — with human escalation for decisions that require judgment. The agentic operations model applied to industrial control.
Tell us about your sensor environment, the quality or maintenance problem you're trying to solve, and what your data looks like today. We'll tell you what's actually feasible.