Implementing Augmented Engineering with Hybrid AI: A Collaboration between Capgemini and Wolfram
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.
