- Posted on: 2026-09-28
Bringing AI for Test and Analytics to NI CompactRIO with Symplify Now!
For years, NI CompactRIO has been a trusted platform for deploying reliable, real-time test and industrial monitoring applications. As engineering teams begin adopting AI and advanced analytics, a new challenge has emerged: how do you leverage modern data science tools and the benefits of modern scripting language to cut down validation time with NI CompactRIO without significant development efforts?
The answer is our precompiled application Symplify Now! that is written in LabVIEW for maximum performance and extremely open for any customization you may require in the future. One of the most common ways to expand the out of the box solutions is with the provided APIs that make it easy for Python applications to access live and historical data running on CompactRIO systems. This allows engineers to combine the reliability of CompactRIO with the rapidly growing ecosystem of AI, machine learning, and data analytics tools available in Python.
Accelerating Test Development with AI
One particularly exciting application is AI-assisted test plan generation.
By providing an AI model with system requirements, interface specifications, or existing test procedures, engineers can quickly generate draft test plans, identify missing edge cases, and improve test coverage.
When combined with data accessible through Symplify Now!, AI can help teams move faster from requirements to validation while maintaining engineering oversight and decision-making.
From Data Acquisition to Intelligent Insights
Traditional test systems excel at collecting and storing data. The next step is extracting meaningful information from that data automatically.
Using Symplify Now! APIs, Python applications can connect directly to test and monitoring systems and perform tasks such as:
- Automated test result analysis
- Anomaly detection
- Statistical processing
- Trend identification
- AI-generated summaries and reports
Instead of manually reviewing thousands of measurements, engineers can focus on the insights that matter most.
Integrate with Existing Systems Without Rewriting Everything
One of the biggest challenges when introducing new analytics and in existing processes is integration. Most organizations already have significant investments in automation systems, databases, reporting tools, and custom software that cannot simply be replaced.
Symplify Now! was designed with this reality in mind. The same open APIs allowing for Python connectivity, Symplify Now! can easily exchange data with existing infrastructure, including:
PLC-based control systems
SQL and historian databases
ERP platforms
Custom test equipment and instruments
Internal web applications and dashboards
Third-party analytics and reporting tools
Rather than forcing organizations into a new ecosystem, Symplify Now! acts as a bridge between proven operational systems and modern analytics technologies. This allows engineering teams to add AI-assisted workflows, reporting, and edge analytics to existing deployments with minimal disruption and significantly less development effort.
Analytics at the Edge
Many CompactRIO deployments operate in environments where sending large volumes of data to the cloud is impractical or undesirable. With Python applications connected through Symplify Now!, analytics can run directly at the edge, close to where the data is generated.
This approach offers several advantages:
- Faster response times
- Reduced network traffic
- Improved reliability
- Better control over sensitive data
Whether you’re monitoring a production line, validating a new product design, or running a long-duration test, edge analytics can provide real-time insight without requiring cloud infrastructure.
Modern Tools, Proven Platforms
Engineering teams shouldn’t have to choose between the stability of CompactRIO and the innovation happening in AI and data science.
By connecting Python to deterministic controls in LabVIEW on the CompactRIO through Symplify Now! organizations can leverage modern analytics, AI-assisted workflows, and automated reporting while continuing to build on the proven foundation of NI hardware and LabVIEW-based systems.
The result is a smarter, more connected test architecture that helps engineers spend less time processing data and more time solving difficult problems.
