Computable Assessment in Practice: OpenStax Assignable + Quezzio

Heavy-duty electric trucks don’t have the luxury of wasted heat. Every watt must either be stored or redirected, which makes thermal control one of the hardest parts of vehicle design. For a global manufacturer of commercial freight vehicles developing its next electric platform, simulation speed became the barrier. Each test cycle ran for hours, slowing every decision about how to warm the cabin and protect the batteries.

Wolfram Consulting replaced that process with a unified computational model and a neural network able to predict and correct thermal behavior in real time. What once ran overnight now runs in seconds.

The Challenge

Designing the thermal system of a heavy-duty electric truck means balancing efficiency with durability and driver comfort. The job demands precise control of temperature across batteries, motors and cabin spaces, while keeping energy use as low as possible.

At the start of development, separate engineering groups produced their own heat data for a four-hour reference route between company facilities. The battery team modeled pack heating, the driveline group estimated motor losses, and the cabin group focused on air-handling loads. The thermal group then merged these datasets by hand in spreadsheets to approximate whole-system behavior.

Detailed GT-Suite runs that took hours were reproduced in tens of seconds in System Modeler. For closed-loop control experiments that adjust valves/pumps on the fly, the team identified a real-time simulation constraint that required running at 1×, which is why those validation runs were executed overnight.

The Solution

Wolfram Consulting delivered a two-step solution: first, a high-fidelity System Modeler framework that replaced manual thermal analysis, and second, a neural network trained on those simulations to predict and optimize real-time performance.

The team replaced static spreadsheets with a computable model of the truck’s thermal circuit, similar to the one shown here. Engineers built a two-tier System Modeler library for early exploration and for detailed dynamics. The first tier used simplified components with basic mass-flow inputs, so engineers could visualize and test circuit concepts before detailed data existed. The second tier added hydraulic flow and pressure behavior, including pipe diameters, pump curves and coolant properties that vary with temperature and pressure.

These models produced results consistent with GT-Suite in a fraction of the time. A full run of the same route that once took three to four hours could be completed in about 35 seconds. The library enabled rapid iteration and reuse across projects, giving the thermal group a practical foundation that matched operating conditions.

Once validated, the team generated steady-state simulations spanning valve positions and pump speeds under varied operating conditions. The resulting data trained a neural network that learned how each configuration affected the battery systems and the cabin. It could predict future temperatures and, more importantly, compute the control settings needed to hit a target.

This invertible approach made the network an adaptive control layer. During a full cycle, the system adjusted parameters in real time to avoid under- or over-temperature events. When battery temperature fell below range, the controller identified the precise valve and pump changes needed to recover. It could also ease compressor load when limits were stable.

Together, the simulation framework and control layer created a self-correcting thermal system that improved speed and accuracy.

The Results

The framework delivered immediate gains. Engineers moved from multi-hour runs to seconds-scale iteration, testing new circuit layouts and control strategies that had been impossible to study before.

The adaptive control layer turned static calibration into a data-driven process. Instead of tuning modes by hand, the system maintained target ranges automatically during validation, adjusting pump speeds and valve positions to keep all components within their limits.

The libraries are now used beyond the initial program. The battery group is modeling internal heat generation, and the controls team has integrated the thermal model through FMI for co-simulation. Working from the same computable foundation, teams report faster decisions and more consistent results.

Building a Scalable Modeling Standard

By replacing a slow, siloed workflow with a unified computational framework, Wolfram Consulting turned thermal modeling from a bottleneck into a continuous design process. What began as a single EV program has become a shared modeling standard across engineering groups and now extends to electrical and control work.

When you need to speed up simulations or optimize system performance, Wolfram Consulting can help you build models that deliver faster, more reliable results.

Labs generate more data than they can easily use. The result? Manual workflows and fragmented tools slow down decisions. LabV, a lab data management company, decided to design a system that makes it faster to identify issues and communicate them clearly—both internally and externally.

LabV worked with Wolfram Consulting to build a digital assistant for real-time lab data analysis. A single prompt now surfaces trends that once took hours to uncover. In one case, it revealed a correlation between density and heat resistance—guiding product decisions without manual analysis or coding. This made LabV one of the first AI-powered solutions for material testing labs, combining AI’s fluency with a computational foundation so every output is both fast and verifiable.

The Prerequisites for Smarter Analysis

Meeting regulatory standards requires collecting and analyzing hundreds of data points across batches and suppliers. But LabV’s instruments weren’t fully connected to its laboratory information management system (LIMS), and much of the data still lived in spreadsheets. Even routine retrieval was slow. Deeper comparisons—like tracking material behavior by supplier—were often skipped entirely.

While generative AI tools were readily available, they couldn’t access LabV’s proprietary datasets or guarantee accurate answers. Without a way to ground responses in the lab’s own data, AI risked producing results that looked plausible but couldn’t be trusted. Research shows that disorganized, siloed data delays batch approvals, extends development cycles and drives up costs by consuming engineering hours in redundant work.

More fundamentally, the fragmented system made automation impossible. Traditional LIMS platforms weren’t designed for complex data integration or analysis at scale. To move forward, LabV had to build a centralized, structured dataset—one capable of supporting real-time decisions and enabling smarter tools. Without that foundation, AI was just out of reach.

Making Data Usable at Scale

LabV couldn’t move forward until it fixed its data layer. Traditional LIMS systems aren’t built to connect every instrument or consolidate outputs. Wolfram helped create a unified framework where test results could be collected, structured and searched—laying the groundwork for automation and eventual AI-powered analysis.

Once the data was structured, LabV and Wolfram Consulting built a digital assistant to work on top of it. The interface relies on a large language model to process natural language queries, while Wolfram’s back end handles the actual analysis—ensuring accuracy and preventing AI hallucinations. The assistant’s chat interface connects through Wolfram Enterprise Private Cloud, which coordinates LabV’s structured datasets, Wolfram’s computation results and the language model output. This architecture ensures every response comes from verified data and is computed with the same algorithms used in scientific and engineering applications.

The assistant isn’t built for show—it’s built to be used. LabV’s approach reflects best-practice machine learning workflows: data generation, preparation, model training, deployment and maintenance—keeping predictive insights accurate, explainable and current. Powered by Wolfram’s tech stack, a single prompt can return correlation tables, batch-level comparisons or supplier performance charts in seconds, using both historical and current data.

Real Results, Not Just Output

LabV replaced fragmented workflows with prompt-driven analysis powered by Wolfram’s back end. Batch issues that once went unnoticed were flagged in seconds. One manufacturer reported a 10% reduction in engineering hours by running fewer tests without sacrificing quality—time they could redirect toward faster development.

In coatings R&D, the assistant has identified optimal formulations for extreme environmental conditions by combining historical data with defined requirements, cutting development cycles and improving resource efficiency. Visual outputs improved supplier communication, and customized data handling reduced errors—helping the lab meet standards without extra head count or production delays.

Plus, the project’s six-week delivery window showed the team could move quickly without cutting corners. That speed, paired with Wolfram’s technical foundation, helped validate LabV’s approach and reinforced its credibility as a scalable platform for lab data oversight.

From Complexity to a Competitive Edge

LabV’s results were made possible by a system built to handle complexity from the start. Wolfram’s tech stack supports legacy data, real-time queries and evolving compliance needs. For leaders, that kind of scalability isn’t theoretical—it’s what makes automation sustainable under real-world demands.

Wolfram’s hybrid approach—combining a language model front end with a computational back end—delivers usable results without guesswork. Prompts return verifiable outputs, not vague summaries. Teams can surface patterns, outliers or points of interest in moments, interpret their significance and rapidly iterate through potential solutions—giving LabV clients an agility advantage in R&D and quality control.

By embedding a computational layer between the language model and the underlying data, Wolfram Consulting ensures AI output isn’t just plausible—it’s correct, explainable and backed by traceable sources. Since machine learning models return probabilities rather than certainties, having verifiable computation in the loop means every result can be trusted and acted upon. That’s why it’s not just about adding AI—it’s about making your data work for you.

When you’re ready to move beyond fragmented data, Wolfram Consulting can help you build the system that makes better decisions possible.

Radio Access Networks (RANs) form the essential bridge between mobile devices and a network’s core infrastructure. For 3G and 4G networks, mobile operators have traditionally relied on proprietary RAN systems—typically sourced from a single vendor—which can limit interoperability, raise long-term costs and slow adaptation to new technical demands.

In the shift to 5G, Wolfram has shown how AI-driven network optimisation and a deployable full-stack environment extend what Open RAN can deliver, giving developers direct access to built-in machine learning and advanced visualisation inside the network.

That vision of a more open and adaptable network underpins Wolfram’s work in the Cambridgeshire Open RAN Ecosystem (CORE) project. The initiative—one of 19 regional trials funded through the UK government’s Open Networks Ecosystem program—served as a live, multi-vendor testbed.

Wolfram’s role centred on building and deploying a predictive optimisation rApp inside this multi-vendor network, demonstrating how machine learning can guide real-time decisions and proving that the full Wolfram stack can operate natively in an Open RAN environment.

An Open Way Forward

As 5G networks evolve to support increasingly complex demands—from real-time media streaming to dense device connectivity—open, interoperable infrastructure offers a path toward more adaptive and cost-effective deployments. Countries like the United States and Japan have already begun scaling Open RAN technologies in live environments, while adoption in Europe is still in the early stages.

Wolfram collaborated with eight partners ranging from infrastructure providers to academic researchers to deploy a testbed Open RAN 5G network in Cambridgeshire. Unlike prior lab demonstrations, the CORE network operated across public venues and supported hardware and software from multiple vendors, demonstrating the viability of a modular, interoperable network at scale. In field testing, Wolfram’s predictive optimisation rApp helped the network sustain download speeds of 700 Mbps with latency under 10 milliseconds—performance levels proven during a live augmented-reality concert demo.

Wolfram’s role in this project centred on building an rApp within the RAN Intelligent Controller, showing how AI-driven insight can support real-time policy decisions without sacrificing explainability or control. Just as significant, the project proved that the full Wolfram technology stack can run natively inside an Open RAN environment, giving developers immediate access to advanced computation previously available only outside the network. As UK operators explore new architectures and supply-chain diversification, the CORE project stands as a concrete example of what’s possible—and what’s ready to scale.

Wolfram’s Network Optimisation rApp

In the Open RAN architecture, rApps are standardised software applications that run in the non-real-time RAN Intelligent Controller (Non-RT RIC). Their role is to analyse historical and near-real-time network data to support intelligent traffic management, anomaly detection and policy optimisation—helping operators adapt to changing network conditions without manual intervention.

rApps are a core component of the O-RAN specification, and Wolfram developed a native Wolfram Language implementation for the CORE project. The result was a customised rApp that provided predictive analytics and explainable decision support within a multi-vendor environment. It also established a foundational Open RAN SDK to drive future data and AI-powered app development.

Wolfram’s Network Optimisation rApp is a full-stack implementation designed to support intelligent decision-making in Open RAN environments. Built using Wolfram Language, the system combines a back-end machine learning engine that analyses network data and predicts performance patterns with a front-end dashboard that visualises key metrics and delivers actionable recommendations to engineers.

Developed as part of the CORE project, the rApp was successfully deployed alongside partner hardware in a multi-vendor testbed. This deployment also confirmed that the entire Wolfram technology stack could operate natively within the RAN environment, giving developers direct access to advanced computation and machine learning without external integration.

The back end of Wolfram’s rApp processes live network performance data to support intelligent optimisation. It ingests key performance indicators (KPIs) such as throughput, latency, signal strength and packet loss—core metrics used to assess the health and efficiency of a 5G network. Using these inputs, the machine learning model identifies patterns in traffic behavior, detects anomalies and generates recommendations aimed at improving network performance. These outputs are designed to help operators proactively manage traffic flow and resource allocation across the network. While retraining frequency depends on deployment parameters, the model is intended to operate continuously in tandem with incoming telemetry data.

The front-end dashboard of Wolfram’s rApp provides a visual interface for monitoring network performance in real time. Engineers can view KPIs such as throughput and signal strength using standard visualisations, with options to compare multiple data streams or focus on specific time windows. Users have control over which metrics are displayed and can adjust the timeframe to analyse short-term fluctuations or longer-term trends.

One view, for example, displays throughput across two adjacent cells, offering a side-by-side comparison that helps operators quickly identify load imbalances or performance deviations. This interface supports decision-making by making underlying patterns immediately visible and aligning visualisations with actionable insights generated by the back end.

The rApp’s recommendation system translates back-end analysis into actionable suggestions for network engineers. When the model identifies a configuration that could improve performance—such as activating additional cells to handle rising traffic—it surfaces a recommendation through the dashboard interface. These suggestions are presented alongside relevant context and supporting metrics but are not applied automatically; engineers remain responsible for reviewing and approving any changes. Here is an example recommendation detailing a traffic-steering policy for a specific cell based on predicted demand.

The Promise of Open 5G: Higher Speeds, Exciting Applications

Tests conducted during the CORE project trial in Cambridgeshire demonstrated that the open 5G network consistently achieved download speeds exceeding 700 Mbps with sub-10-millisecond latency and 100% uptime during live operation. These results were obtained using commercially available devices under typical deployment conditions, not lab-simulated scenarios.

Performance was well above the ITU’s recommended 5G benchmark of 100 Mbps for user-experienced throughput and outperformed trial speeds reported in similar UK Open RAN pilots, such as Three UK’s 520 Mbps deployment in Glasgow—confirming that Open RAN configurations can deliver production-grade connectivity in high-demand settings.

To evaluate network performance under immersive, high-bandwidth conditions, the CORE consortium organised a public augmented-reality concert trial. A live performance at the Cambridge Corn Exchange was streamed in real time to a second location, where participants wearing Meta Quest and Apple Vision Pro headsets viewed the show as spatial 3D video with synchronised audio.

The feed—captured using custom 8K 3D cameras and streamed over the open 5G network—was delivered with less than 1.5 seconds of latency and maintained flawless audio-visual sync across multiple headsets. This setup provided a realistic stress test of the network’s capacity to handle sustained, low-latency, multi-device loads in a public setting.

The successful execution of the augmented-reality concert trial underscores the potential of Open RAN to support next-generation applications that demand both high throughput and ultra-low latency. It also validated the ability of a multi-vendor, standards-compliant architecture to deliver consistent performance under real-world load conditions.

While this trial was limited in geographic scope, the results provide a working proof of concept for scalable, interoperable network design. By adding software-defined intelligence through Wolfram’s rApp, the project showed that intelligent optimisation and real-time responsiveness can coexist—a model for broader adoption of adaptive 5G deployments across the UK.

Ready to Build Smarter Networks?

The CORE Project proved two things: that Wolfram’s full technology stack can run natively inside an Open RAN environment—powering rApps, dashboards and machine learning–driven optimisation—and that Wolfram can deliver those results while working closely with industry partners and government-led initiatives. Together, these capabilities position Wolfram Consulting as a partner ready to help operators and vendors turn network data into real-time intelligence and adaptive 5G solutions.

Contact Wolfram Consulting to put the full Wolfram tech stack to work inside Open RAN—delivering predictive optimisation and actionable insights out of the box.