Turn connected asset data into business-critical insights, fast

Translate complex analysis into radically simple routines. Our specialized engine reduces complex queries from hundreds of lines of SQL into simple, intuitive routines. It natively handles common patterns like sequence detection, time-windowed aggregations, and cross-sensor correlations that would require complex JOINs and window functions in traditional databases. The engine combines this simplified interface with a distributed execution layer that leverages pre-computed aggregations and a correlation graph, delivering millisecond response times on queries that typically take hours to write and execute in standard databases.

Harness AI to detect hidden patterns. Dozens of AI algorithms identify emerging issues and anomalies across sensor data, diagnostic codes, and maintenance records. Predictive models leverage both historical patterns and real-time telemetry to forecast component health and failure probabilities. These capabilities are enhanced by AI that helps users explore complex relationships in the data, investigate root causes, and understand emerging patterns. All models are specifically optimized for industrial equipment data, ensuring accuracy across diverse asset types and operating conditions.

Ask complex questions, get fast answers. 
Our interface lets users easily perform sophisticated operations – from similarity searches and temporal correlations to predictive modeling. Operations that typically require months of data science work can be executed in minutes: comparing failure patterns across vehicle cohorts, detecting subtle correlations between manufacturing conditions and field performance, or predicting component health based on operating patterns. Whether through our visual interface, query language, or conversational LLM, users can explore relationships and test hypotheses across their entire fleet without writing code or building custom models.