Mistral AI & Capgemini

Global integrator's private software engineering assistant delivers systematic productivity improvements and quality enhancements while meeting strict sovereignty requirements for regulated industries. 

From 30% to 100% developer adoption, the internal coding assistant delivers systematic productivity gains while maintaining data sovereignty for regulated industries. 

1. 100% developer adoption rate across all client projects
2. 90% code completion accuracy vs. 50% with previous solutions 
3. 50+ client projects reporting productivity improvements in less than 6 months 

Capgemini, a global system integrator with 350,000 employees across 40+ countries, has revolutionized its software engineering capabilities through “RAISE for Private Software Engineering” (aka SovBox), an internal software engineering assistant powered by Mistral AI. Serving regulated clients in aerospace, defense, and public sectors who require self-hosted AI solutions, Capgemini achieved a dramatic transformation from 30% to 100% developer adoption, enabling the company to deliver enhanced productivity, quality, and security across all client engagements while dedicating more resources to business value creation. 

“Leveraging Mistral’s Codestral has been a game changer in the adoption of private software engineering assistants for our client projects in regulated sectors. We have evolved from providing basic support for some development activities to delivering systematic value for our development teams.“

Alban Alev, VP, Head of Solutioning at Capgemini France.

Modernizing software engineering while meeting regulatory requirements 

As a global system integrator, Capgemini recognized early that generative AI would fundamentally transform software engineering. However, their extensive client base includes many organizations with strict regulatory requirements that prevent them from using 

cloud-based AI solutions, creating a significant gap in their ability to leverage AI-powered development tools to better serve their clients. 

"While generative AI's power is typically delivered through cloud-based solutions, these would not comply with all contexts in terms of sovereignty requirements. For our regulated clients, including public sectors, aerospace and defense clients, strict data privacy and control were fundamental obstacles to adopting traditional cloud offerings," explains Alban Alev, who leads portfolio pre-sales and solutioning for Capgemini France's application services. 

The challenge extended beyond data privacy concerns. Capgemini's initial attempts with open-source coding assistants proved unsuccessful despite promising laboratory results. Adoption rates remained at only 30%, with developers finding the ~50% accuracy insufficient for daily use. The company also faced the complexity of supporting legacy applications dating back to the 1980s, requiring AI assistance across diverse programming languages including ADA, C or C++ and industry-specific languages critical for their regulated clients managing decades-old application portfolios. 

"The challenge wasn't just about finding the right technology—it was about achieving adoption. We had successfully passed the first two milestones of finding and deploying tools for our private coding assistant back in 2023, but the third milestone, getting developers to actually use them, was the most challenging," explains Alban Alev, Portfolio Pre-sales and Solutions Lead at Capgemini France. 

Codestral delivers the breakthrough needed for developer adoption 

Capgemini's transformation came when they transitioned from their previous open-source LLM to Mistral AI's Codestral in their self-hosted coding assistant solution. The switch required no changes to their existing infrastructure—they simply replaced the underlying model while maintaining other IDE and CI/CD integrations. 

Accuracy jumped from 50% to 90%, which proved to be transformational for the developers experience. "It's the difference you experience, as a developer, when the first answer is good at 50% or 90%. It completely changes your first impressions on the helpfulness of the coding assistant," explains Alban Alev. This improvement was consistent across multiple programming languages, including support for legacy languages critical to regulated clients with application portfolios conceived in the 1980s. 

Codestral's superior performance addressed Capgemini's unique requirements for supporting applications across various programming languages and contexts. The model's ability to provide accurate code completion and chatbot assistance within developers' IDEs meant that engineers could work more efficiently across their entire portfolio of client technologies. 

Beyond the technical capabilities, Capgemini invested also in deployment methodology and developer coaching to ensure successful adoption after the first impressions were positive. 

Why Mistral AI? 

Capgemini selected Mistral AI models for their: 

• Self-hosted deployment capabilities that satisfy strict data-privacy and compliance mandates for regulated clients. 
• Superior accuracy across both modern and legacy programming languages, driving immediate developer trust and adoption.
• Seamless integration with existing IDE workflows, avoiding costly re-platforming or workflow disruption.
• Cost-efficient performance that supports enterprise-wide scaling across active projects without budget overruns.

Universal adoption drives client value and business transformation 

The results were immediate and impressive. Developer adoption jumped from 30% to 100%, with all approximately 50 client projects reporting productivity savings. "From one day to another all the projects went green," describes the transformation Capgemini experienced across their active client engagements. 

This universal adoption enables Capgemini to deliver on their core value proposition of "doing more and better" for clients. The improved efficiency in software production allows Capgemini teams to dedicate more time to business value and client adoption rather than basic coding tasks. For regulated clients, this means faster delivery without compromising on sovereignty requirements. 

"Today, using Generative AI has switched from a nice opportunity to a requirement from the new joiners in the company," Alban explains, highlighting how AI tools have become essential for attracting and retaining software engineering talents. This positions Capgemini's Mistral AI-powered coding assistant not just as a productivity tool, but as a competitive advantage in talent acquisition and employee satisfaction while delivering enhanced value to clients through improved project outcomes. 

The success has prompted Capgemini to expand their AI strategy beyond basic coding assistance. They're now deploying the full Mistral Code solution, including Codestral Embed and Devstral for enhanced RAG capabilities and CI/CD integration. Future applications include business requirements capture, automated test plan generation, operations automation, and software transformation projects.