Quantifying Risk at Enterprise Scale with Wolfram Consulting

Sure, it was laughable when X’s AI chatbot Grok accused NBA star Klay Thompson of a vandalism spree after users described him as “shooting bricks” during a recent game, but it was no joke when iTutorGroup paid $365,000 to job applicants rejected by its AI in a first-of-its-kind bias case. On a larger scale, multiple healthcare companies—including UnitedHealth Group, Cigna Healthcare and Humana—face class-action lawsuits based on their AI algorithms that are alleged to have improperly denied hundreds of thousand of patient claims.

So, while AI—driven by large language models (LLMs)—has emerged as a groundbreaking innovation for streamlining workflows, its current limitations are becoming more apparent, including inaccurate responses and weaknesses in logical and mathematical reasoning.

To address these challenges, Wolfram Research has developed a suite of tools and technologies to enhance the capabilities of LLMs. Wolfram’s technology stack, including the Wolfram Enterprise Private Cloud (EPC) and Wolfram|Alpha, increases the productivity of AI applications in multiple enterprise environments. By leveraging Wolfram’s extensive experience in computational intelligence and data curation, organizations can overcome LLM limitations to achieve greater accuracy and efficiency in AI-driven workflows.

At the same time, Wolfram Consulting Group is not confined to one specific LLM. Instead, we can enhance the capabilities of any sophisticated LLM that utilizes tools and writes computer code, including OpenAI’s GPT-4 (where Wolfram GPT is now available), Anthropic’s Claude 3 and Google’s Gemini Pro. We can also incorporate these tools in a privately hosted LLM within your infrastructure or via public LLM services.

Wolfram’s Integrated Technology Stack

Wolfram has a well-developed tech stack available to modern LLMs: data science tools, machine learning algorithms and visualizations. It also allows the LLM to write code to access your various data sources and store intermediate results in cloud memory, without consuming LLM context-window bandwidth. The Wolfram Language evaluation engine provides correct and deterministic results in complex computational areas where an unassisted LLM would tend to hallucinate.

When your organization is equipped with the Wolfram technology stack for tool-assisted AIs, the productivity of your existing experts is enhanced with methods that support exploratory data analysis, machine learning, data science, instant reporting and more:

• The LLM can interpret expert user instructions to generate Wolfram code and tool requests performing a wide variety of computational tasks, with instant feedback and expert verification of the intermediate results.
• Custom tools for accessing corporate/proprietary structured and unstructured data, models and digital twins, and business logic feed problems to the Wolfram Language algorithms implementing your analytic workflows.
• Working sessions create a documented workflow of thought processes, prompts, tool use and code that can be reused on future problems or reviewed for audit purposes.

Designed for system integration flexibility, use the platform as a fully integrated system or as a component in an existing one. In the full-system integration, the Wolfram tech stack seamlessly manages all communications between the LLM and other system components. Alternatively, use it as a set of callable tools integrated into your existing LLM stack as our modular and extensible design readily adapts to your changing needs. Also access the integrated Wolfram tech stack through a variety of user interfaces, including a traditional chat experience, a custom Wolfram Chat Notebook, REST APIs and other web-deployed custom user interfaces.

Wolfram Enterprise Private Cloud (EPC)

Wolfram’s EPC serves as a private, centralized hub for accessing Wolfram’s collection of LLM tools and works in commercial cloud environments such as Microsoft Azure, Amazon Web Services (AWS) and Google Cloud. For organizations preferring in-house solutions, EPC can also operate on dedicated hardware within your data center.

Once deployed, EPC can connect to various structured and unstructured data sources. These include SQL databases, graph databases, vector databases and even expansive data lakes. Applications deployed on EPC are accessible via instant web service APIs or through web-deployed user interfaces, including Chat Notebooks. As Wolfram continues to innovate, the capabilities of EPC also grow.

Wolfram|Alpha Infrastructure

Wolfram|Alpha can also be a valuable asset for your suite of tools. With a vast database of curated data across diverse realms of human knowledge, Wolfram|Alpha can augment your existing resources.

Top-tier intelligent assistants, websites, knowledge-based apps and various partners have trusted Wolfram|Alpha APIs for over a decade. These APIs have answered billions of queries across hundreds of knowledge domains. Designed for use by LLMs, Wolfram|Alpha’s public LLM-specific API endpoint is tailored to enable smooth communication and data consumption.

If your LLM platform requires a customized version of Wolfram|Alpha, our sales and engineering teams will work with you to optimize your access to its extensive capabilities. This ensures that you have the right setup to harness the full potential of Wolfram|Alpha in your specific context.

Preparing Knowledge for Computation

While many platforms give an LLM access to data retrieval tools, what sets Wolfram apart is extensive experience in preparing knowledge for computation. For over a decade, Wolfram has provided knowledge curation services and custom versions of Wolfram|Alpha to diverse industries and government institutions with sophisticated data curation workflows and exposed ontologies and schemas to AI systems. Direct access to vast amounts of data alone is not enough; an LLM requires context for data and an understanding of the user’s intent.

Wolfram consultants can establish workflows and services to equip your team with tools for programmatic data curation through an LLM. This process involves creating a list of questions and identifying the subjects or entities to which these questions apply. The LLM, with the aid of the appropriate retrieval tools, then finds the answers and cites its sources. These workflows alleviate the workload of extensive curation tasks, and the enhanced curation capabilities then operate within the EPC infrastructure.

At the same time, you’ll retain ownership of any intellectual property created for your funded project, including custom plugins or tools Wolfram develops, ensuring you have full control over the solutions created for your organization.

Enterprise AI the Wolfram Way

When you decide you need a custom LLM solution, let Wolfram Consulting Group build one tailored to your specific needs. From developing runtime environments that help your teams integrate Wolfram technology into existing platforms to creating application architecture, preparing data for computation and performing modeling and digital twin implementation, Wolfram has the unique experience across all areas of computation for the right balance of approaches to achieve optimal results.

By working with Wolfram, you get the best people and the best tools to keep up with developments in the rapidly changing AI landscape. The result? You will capture the full potential of the new generation of LLMs.

Contact Wolfram Consulting Group to learn more about using Wolfram’s tech stack and LLM tools to generate actionable business intelligence.

OpenStax has established a large-scale foundation for open educational content, delivering structured course materials used by instructors and students across a wide range of subjects and institutions. With the introduction of its teaching and learning platform, OpenStax Assignable, the focus expanded from content delivery to supporting large-scale engagement and assessment within the same environment. In mathematics in particular, this requires generating and evaluating large volumes of problems while accommodating equivalent expressions, multiple solution paths and varying instructional approaches.

Wolfram Consulting worked with OpenStax to extend these capabilities by transforming textbook exercises into algorithmically generated questions using its existing Quezzio assessment system. The team adapted Quezzio’s generation and symbolic evaluation capabilities to produce new problem instances while preserving mathematical structure. This established a pipeline for extending fixed question sets into scalable, dynamic assessments within the Assignable platform.

Building Algorithmic Question Generators

The starting point for each question was the existing OpenStax course material, which was converted into programmatic templates within the Quezzio assessment system. Beyond defining a single static exercise, each template encodes the mathematical structure of a problem along with parameter ranges and constraints.

These templates were then refined through a rigorous verification process to ensure instructional quality and mathematical correctness. This included:

• Defining grading rules that reflect the intended form of an answer
• Validating parameter ranges to avoid degenerate or misleading cases
• Ensuring all generated instances are solvable and pedagogically appropriate
• Typesetting mathematical expressions for clarity
• Generating graphics that match problem parameters, with correct proportions and labeling
• Aligning with educational accessibility best practices

This process established a reliable pipeline for generating assessment content at scale. The result is a system that produces mathematically valid, well-formed problems with consistent grading behavior, ready for integration into an instructional platform.

Integrating the Generators into Assignable

Quezzio’s question generators and symbolic evaluation capabilities are integrated directly into the Assignable platform through a combination of API connections and LTI integration. This allows Quezzio to operate within existing learning management system (LMS) workflows, receiving student input, evaluating responses and returning results without requiring a separate interface or external tool.

As a result, the generators are not theoretical—they run inside the actual learning environment. Students encounter dynamically generated problems as part of their coursework, while Quezzio handles generation, evaluation and feedback behind the scenes within the platform.

What Students Experience

Within Elementary Algebra 2e in OpenStax Assignable, students encounter problems generated by the Quezzio system as part of their regular coursework. Each student receives a unique version of a problem based on the same underlying template, providing individualized practice while preserving the intended mathematical structure. Beyond multiple-choice formats or fixed answers, students enter expressions directly, and those responses are evaluated symbolically.

Quezzio evaluates whether a student’s input is mathematically equivalent and structurally valid, allowing multiple correct forms of an answer while enforcing the intended solution method. When a response is incorrect, step-by-step solutions are generated from the symbolic evaluation of that specific problem instance, showing where reasoning diverged. The system can also interpret answers with units in multiple valid forms—for example, recognizing inputs such as “$5,” “5 dollars,” “5 USD” or “500 cents” as equivalent when appropriate.

This video shows Quezzio in practice within the course environment, from problem generation through evaluation and feedback.

Data-Driven Question Generation

The same Quezzio infrastructure also supports data-driven question generation using Wolfram’s knowledgebase. In some problems, parameters are derived from real-world data, such as geographic locations or measured quantities.

For example, a question may select cities based on population data, compute distances between them and generate a corresponding to-scale map, ensuring that each instance is both mathematically valid and grounded in accurate data.

This approach extends beyond individual problems to broader assessment design. Question generators can be adapted to reflect different teaching styles, align with local or regional curricula and scale across courses and institutions. Rather than rely on fixed question banks, institutions can generate assessments that match their own instructional goals while maintaining consistency, accuracy and flexibility.

Extending This Approach to Other Learning Systems

The same process used to build computable assessments for OpenStax can be applied to other course materials and instructional environments. Many institutions rely on static question banks that are difficult to scale, adapt or align with specific teaching approaches. Converting those materials into algorithmic generators makes it possible to produce large volumes of consistent, flexible assessment content while preserving the structure and intent of the original curriculum.

This approach allows institutions to generate assessments tailored to their own instructional goals rather than rely on generic content. Question sets can be adapted to reflect different teaching styles, align with regional or institutional curricula and scale across courses and programs. Existing content can be converted into dynamic formats that support randomization, symbolic evaluation and automated grading without sacrificing accuracy or control.

Wolfram works with organizations like OpenStax to transform existing content into computable assessment systems, integrating them into existing platforms and workflows to support scalable, curriculum-aligned instruction.

Wolfram can help you convert your existing content into scalable, computable assessment.

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.