Quix Cloud

Quix Cloud is a Python-first industrial data platform that enables R&D teams to run MATLAB® and Simulink models as nodes in real-time and batch data pipelines. Engineers can plug a MATLAB model into a pipeline just like a Python transformation (sensor data flows in, model outputs flow out). The Quix Streams library handles micro-batching optimization, and Quix AI provides coding assistance to engineers unfamiliar with Python. The platform includes a centralized data lake, AI-powered notebooks for analysis using natural language, and an AI-assisted pipeline builder. Founded by four McLaren Formula 1 engineers, Quix brings production-grade data infrastructure to engineering teams without requiring big data expertise.

Quix Cloud serves engineering R&D teams in automotive, aerospace, motorsport, energy, and manufacturing industries. Primary users are R&D directors, test engineers, mechanical engineers, simulation engineers, and data engineers who work with test rigs, simulations, and configuration data. Key use cases include: deploying MATLAB and Simulink models in real-time data pipelines (F1 teams processing telemetry every 100ms); running batch simulations with automated parameter sweeps using ML-driven DOE; stopping expensive wind tunnel or test rig runs early based on real-time threshold analysis; cataloging simulation inputs, outputs, and configurations for full traceability; and enabling mechanical engineers to build and share data analysis apps via Marimo notebooks without deep programming skills.

Quix Cloud integrates with MATLAB and Simulink at these distinct levels:

MATLAB Model Hosting: Engineers can plug a MATLAB model into a data pipeline just like a Python transformation. The Quix Streams library supports micro-batching optimization. In F1, this means processing data every 100ms. MATLAB can run either as a full headless runtime (requiring one license per container on Kubernetes) or as a compiled Python SDK artifact (requires a compiler license but enables unlimited runs for 12 months). This is actively used by F1 customers running MATLAB functions alongside Python functions.
Simulink Integration via MQTT: Quix has developed custom Simulink blocks for MQTT-based I/O. This requires no direct Simulink integration. Simulink can run anywhere (even on Windows machines) while all input/output is cataloged in Quix. A live demo shows a phone used as a throttle: accelerometer data flows through Quix for normalization, then via MQTT to a Simulink combustion engine model (running on a Windows laptop), with RPM output streamed back to Quix in real time.
Simulink Docker Deployment: For tighter integration, Quix supports running Simulink via the official Simulink Docker image on Kubernetes, or compiling the model as a Python wheel for license-free horizontal scaling across multiple containers.

Quix also supports Functional Mockup Units (FMUs) via the FMI open standard, enabling integration with 280+ R&D modeling tools. Engineers can use the Quix FMU runner (based on FMPy) to simulate an FMU on live input data, then output the computed results to a downstream destination. A dynamic configuration service injects parameters into running models in real time; when car configuration changes on track, the MATLAB and Simulink models are automatically updated.