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Capgemini Engineering and Wolfram Research are developing a co-scientist framework: a tool designed to support engineers working on complex physical systems. Drawing on shared strengths in symbolic computation, generative AI and systems engineering, the co-scientist helps translate engineering intent into executable, verifiable computations.

This project is part of Capgemini’s broader Augmented Engineering strategy, an initiative that applies hybrid AI techniques to real-world engineering challenges. By combining Wolfram’s expertise in symbolic computation with generative AI, the co-scientist helps teams engage with complex problems earlier in the design process—making it easier to refine assumptions and address critical risks before they compound.

Natural Language In, Symbolic Logic Out

The co-scientist is designed to bridge natural language input and computational output. By combining large language models with Wolfram’s symbolic computation and curated knowledgebase, it allows engineers to ask domain-specific questions in plain language and receive results in the form of executable code, equations or dynamic models that can be verified and reused.

Using the co-scientist feels less like querying a search engine and more like working with a technically fluent collaborator. Engineers can describe the problem in their own words—“simulate thermal behavior under load” or “optimize actuator response time”—and receive results they can validate immediately. Behind the scenes, the co-scientist generates Wolfram Language code and combines existing models, Wolfram Language code and computable data to return outputs that are not just plausible but logically structured and derived from verifiable computation.

Building Trust with Hybrid AI

This collaboration puts hybrid AI to practical use by combining the flexibility of language models with the structure of symbolic reasoning and computational modeling. Instead of generating surface-level responses, the system can explain its logic and return results that hold up in real-world settings like aerospace or industrial automation.

In regulated or high-risk environments, outputs must be traceable and reproducible. Hybrid AI systems built on symbolic foundations allow teams to audit every step: how an equation was derived, what assumptions were embedded and how the result fits within engineering constraints. That’s not just helpful—it’s required in science and engineering.

From Acceleration to Transformation

The co-scientist doesn’t just accelerate existing workflows—it shifts how engineers and researchers approach complex challenges. Instead of translating a question into technical requirements and then into code, users can work at the level of intent, refining the problem itself as they explore possible solutions. That creates space for earlier insights, faster course corrections and a more iterative, computationally grounded design process.

As this collaboration continues, Wolfram’s computational framework keeps the co-scientist anchored in formal logic and verifiable output—qualities that matter in domains where failure isn’t an option. From engineering design to sustainability analysis, the co-scientist is already showing how generative AI can shift from surface-level response to real computational utility.

Cancer researchers aren’t short on data, but they are often overwhelmed by it. The Cancer Genome Atlas (TCGA) contains more than 2.5 petabytes of genomic, clinical and imaging data across 33 cancer types. For those in the medical and research communities—especially without formal computational training—making practical use of that data remains a significant challenge.

As a result, a valuable resource remains underutilized.

Dr. Jane Shen-Gunther, a gynecologic oncologist and researcher with expertise in computational genomics, encountered these limitations firsthand. Drawing on her deep expertise, she recognized the pressing need for a more accessible, unified method to access and analyze genomic and imaging data from multiple sources.

“The TCGA, The Cancer Imaging Archive (TCIA) and the Genomic Data Commons (GDC) are essentially three goldmines of cancer data,” she said. “However, the data have been underutilized by researchers due to data access barriers. I wanted to break down this barrier.”

Shen-Gunther partnered with Wolfram Consulting Group to design the TCGADataTool, a Wolfram Language–based interface that simplifies access to TCGA cancer datasets.

Designing a Research-Ready Tool with Wolfram Consulting

Drawing on prior experience with the consulting team, Shen-Gunther worked with Wolfram developers to design a custom paclet to streamline data access and clinical research workflows while remaining usable for individuals without extensive programming experience.

Shen-Gunther explained, “Mathematica can handle almost all file types, so this was vital for the success of the project. It also has advanced machine learning functions (predictive modeling), statistical functions that can analyze, visualize, animate and model the data.”

Interface and Data Retrieval — The guided interface (TCGADataToolUserInterface) allows users to select datasets, review available properties and launch key functions without writing code. Built-in routines support batch retrieval of genomic data from the GDC and imaging data from TCIA, including scans and histological slides associated with TCGA studies.

Data Preparation and Modeling — Processing functions like cleanRawData and pullDataSlice prepare structured inputs for analysis by standardizing formats and isolating relevant variables. Modeling tools enable users to identify potential predictors, visualize candidate features, generate design matrices and build models entirely within the Wolfram environment.

Visualization Tools — The paclet includes support for swimmer plots, overall survival plots and progression-free survival plots, helping researchers visualize clinical outcomes and stratify cases by disease progression or treatment response.

By combining domain-specific requirements with Wolfram Language’s built-in support for data processing and analysis, the paclet makes it possible for researchers without programming experience to work with genomic and imaging datasets that would otherwise require advanced technical skills. This shift enables researchers without programming experience to work directly with TCGA data using computational methods previously out of reach.

Moving from Complexity to Capability

The TCGADataTool was developed in response to specific challenges Shen-Gunther encountered while working with genomic and imaging data. She emphasized the importance of designing a tool that could support clinical research workflows without requiring a background in programming—and saw the result as both accessible and technically robust.

By designing a tool that supports clinical research without requiring fluency in code, Wolfram Consulting Group delivered a solution tailored to the needs of domain experts—extending the reach of high-throughput data into contexts where traditional software workflows often fall short.

“The [Wolfram team] carefully and thoughtfully developed technical solutions at every step and created a beautiful, easily accessible product,” said Shen-Gunther. “Their expertise in data science and user-interface development was essential to the success of the paclet.”

Rather than offering a generalized platform, Wolfram Consulting delivered a focused, researcher-specific application that addresses both technical complexity and day-to-day usability. That model—identifying key obstacles, understanding the research workflow and delivering a targeted solution—can be extended to other biomedical contexts where access to large datasets is essential but often limited by tool complexity.

If your team is facing similar data access or analysis challenges, contact Wolfram Consulting Group for a solution tailored to your research environment.

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