Agentic AI for hardware testing: Resilience and agility for sovereign, industrial engineering
The advent of AI-defined hardware
AI transformed how we build software. Now it's going to do the same to how we build physical systems. Autonomous vehicles, industrial robots and reusable rockets are just the tip of the iceberg. Entire factories, grids and supply chains will eventually run with a level of autonomy that's hard to fully picture today.
For world-class engineers, it's a blank canvas. They know how to design, architect and build this state-of-the-art hardware, developing systems that will shape our interaction with the physical world for years to come. But fragmenting supply chains, shifting regulations and a failure to upgrade underlying infrastructure are constraining local capability.
Meanwhile, the rise of global competition is creating increasing pressure for sovereign technology to be delivered faster and at lower cost.
The proliferation of hardware data
The shift to net zero, the rise of interconnected autonomous systems and the emergence of AI have converged to produce data faster than it can be stored, queried, and analysed. For an engineer, time is now sunk configuring data, building infrastructure and navigating complex software systems before any time can be spent designing, architecting or engineering.
In practical terms that looks like a test run completing and nobody being able to answer questions such as:
- What configuration was running?
- Was that sensor reading real or a rig artefact?
- Has this component been through a similar test before?
The information exists somewhere, but it's in a spreadsheet, a Teams message, or the head of the engineer who ran the test.
Introducing Quix for Hardware Testing
Quix for Hardware Testing captures every test with its full configuration. It's designed for resilience across the most ambitious engineering programmes, including those that are iterative, multi-phase, spread across rigs and simulations and in-service assets. Capable of running in multi-cloud, hybrid and on-premise environments, data security and sovereignty are built in by default.
Quix for Hardware Testing comprises five key pillars.
- QuixPipelines deploys agentic data engineers to build and maintain real-time data pipelines.
- QuixLake consolidates data from every test in one lakehouse, tagged with its configuration, in open formats on your own blob storage.
- QuixLab is a Python environment for analysis and correlation across rig, dyno, and simulation data, with agents handling the scripting, with results that are visualised through dashboards and reports.
- QuixApps lets you build, deploy and host any application, from a single dashboard to a full multi-page tool.
- QuixAI completes research tasks and validates requirements faster using autonomous AI agents that analyse tagged, consolidated data.
Where it applies
The platform is already being used across some of the most data-intensive environments in hardware development, including Formula 1 racing, automotive, aerospace, defence, manufacturing and energy. It's well suited to environments where the volume and complexity of test and operational data has consistently outgrown the tools available to manage it.
Core use cases include:
Real-time telemetry and decision support: Anomalies surface during the test, not hours later when data has been processed and reviewed. When a reading looks wrong, it's possible to check immediately whether it's a real system behaviour or a sensor issue.
Test data consolidation: MIL, SIL, HIL, dyno runs, physical rigs, in-service hardware are consolidated in one place, with consistent configuration context across the full development cycle.
Simulation against live data: Running a simulation model against real test data in real time closes the loop between virtual and physical development. When correlation drops, it's visible immediately, and model calibration cycles that used to take weeks get shorter.
Manufacturing process optimisation: High-frequency data is captured continuously across manufacturing operations, tagged with full process configuration, and fed into an ML model that improves set-points over time. The result is fewer rejected parts, less rework, and measurably better throughput from the same equipment without additional capital investment.
Test file ingestion: Test data arrives in a mix of vendor-specific formats including TDMS, ROS bag files, LabVIEW exports and CSV drops from bench rigs. Quix ingests all of it through a single pipeline, normalises it against a consistent schema, and enriches each record with test configuration metadata at the point of ingestion, with no bespoke per-format connectors or manual conversion step required.
Asset monitoring and optimisation: Sensor data from in-service assets is ingested continuously, enriched with contextual inputs, and fed into ML models that surface operational decisions in real time, replacing manual processes with automated, threshold-driven triggers.
We'll be providing a first look of the release this week at the Automotive Testing Expo, booth 1782. Stop by if you're attending, or, contact us here to book a demo.
You can also download a deep dive into the QuixAI architecture here.