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RAISE helps organizations move from exploration to results
2024 is the year for scaling AI

Weiwei Feng
4th July 2024

Capgemini’s RAISE framework signifies a new era of advancement and evolution for generative AI. It embraces the fast-evolving and highly experimental development of the technology and adapts to the rapid pace of innovation. RAISE delivers accelerators and learnings to harness the power of AI and generative AI while focusing on sustainability, scalability, and trustworthiness.

Generative AI has emerged as a groundbreaking force, democratizing innovation across industries. Open source and commercial models alike have become widely available, leveling the playing field for those eager to harness their potential.

However, as approachable as generative AI may seem, navigating its complexities is no small feat. Within just a year, the field has seen seismic shifts in paradigms and underlying technologies. Organizations are exploring generative AI, recognizing its value as a catalyst for innovation and revenue growth. The Capgemini Research Institute underscores this trend, revealing that nearly 90% of organizations plan to prioritize AI, including generative AI, in the next 12 to 18 months. The question is: are organizations ready to transition from mere exploration to achieving tangible results?

Generative AI generates new content, ideas, or solutions by learning from vast datasets. This extends from creating realistic images and text to generating code and innovative solutions across various fields.

As generative AI solutions are being constructed, decoupled development has led to redundancy and inefficiency. This disconnected approach gives rise to multiple issues: identical open-source models running on separate GPUs, increasing costs and complexity; commercial APIs used in disparate applications, preventing better vendor deals due to split volumes; and the repeated development of similar applications without performance comparison or monitoring.

The year 2023 was a time for experimentation; 2024 is the year for scaling. To address these challenges and herald a new era of development, we crafted the RAISE framework to enforce sustainability, reusability, and trustworthiness throughout the code, establishing a robust AI partnership with our clients. The RAISE framework streamlines the development process, ensuring that generative AI solutions are built on a solid, efficient, and cohesive infrastructure.

Exploring the RAISE framework

RAISE is a gateway to a future of trustworthy, scalable, and sustainable AI. While many companies concentrate on solutions, RAISE shifts the focus to infrastructure – the foundation of both the service and solution layers.

RAISE’s is built on modularization. It promotes the development and deployment of reusable components as independent services, covering an array of AI and generative AI models, tools, and data services. This includes both commercial APIs and open-source models.

At the core of the RAISE framework is a uniform pipeline structure, ensuring cohesion and efficiency throughout development and deployment. It emphasizes efficient deployment of open-source models, leveraging shared GPUs for optimal resource utilization, minimizing environmental impact, and maximizing performance. Its unified management system facilitates easy comparison, efficient deployment, and thorough monitoring of both open-source models and commercial APIs, improving scalability and enabling cost savings as new models emerge.

Adaptability and experimentation are critical, as is embracing a diverse mix of technologies and staying open to future changes. As H. James Harrington noted, “Measurement is the first step that leads to control and eventually to improvement,” highlighting the importance of enforced evaluation. Such processes not only enable rapid comparison of changes in the experiment stage but also ensure smooth transitions to new ideas and models.

RAISE continually identifies and builds reusable components to enhance trust, efficiency, and performance in AI and generative AI applications. Its current offerings include model cascading for cost savings, prompt optimization for performance improvement, and RAG services for scaling enterprise text retrieving service. The framework is committed to evolving and refining its services, empowering its users to embrace ultramodern technology.

RAISE provides a path towards:

  • Trustworthy AI, through rigorous evaluation, testing, and monitoring
  • Scalable AI, by embracing modularization, DataOps, MLOps, DevOps, and governance
  • Sustainable AI, focusing on cost optimization, reusability, and efficiency.

Pioneering the future

The RAISE framework is setting the pace towards a future where AI’s potential is fully unleashed. This next phase in RAISE’s evolution is pioneering tomorrow’s innovations.

Innovative deployment and customization. The future of RAISE is marked by an even greater emphasis on customization and efficiency. Leveraging the latest advancements in large language models (LLMs), RAISE is poised to offer an even more refined infrastructure setup. This includes specialized pipelines for deployment, fine-tuning, and data management, designed to streamline the AI development lifecycle from conception to deployment.

Tailored data and model training. A standout feature for RAISE is its enhanced capability for organizations to craft their own high-quality datasets for training or evaluation purposes. This ensures the data meets the specific needs of each project and also elevates the quality of model training. Coupled with the RAISE training framework, organizations will have the flexibility to develop custom models, pushing the boundaries of what’s possible with generative AI.

Cost-effective model selection. A novel aspect of the RAISE framework is its intelligent model selection service, designed to optimize resource allocation by matching the most suitable model to each task. This reduces costs and amplifies the effectiveness of AI initiatives.

Leading the way into tomorrow

RAISE is pioneering innovative solutions that address today’s challenges and anticipate tomorrow’s opportunities. As large models advance in capability, emerging agents face the challenge of dynamically decomposing tasks and choosing the right tools for completion. RAISE provides these agents with an ever-growing toolbox, incorporating a comprehensive catalog of information and standardized endpoints.

We invite you to reach out. Together, let’s shape the future of AI, leading the charge into uncharted territories of possibility and success.


MODULARIZATION FOR EFFICIENCY: Break down AI development into reusable components with RAISE, optimizing resources and streamlining processes for enhanced efficiency.

TRUST AND SCALABILITY: Ensure trustworthiness and scalability in AI solutions with RAISE’s evaluation, testing, and monitoring mechanisms.

FUTURE-PROOF INNOVATION: Stay ahead of the curve with RAISE’s commitment to adaptability and customization, empowering organizations to pioneer tomorrow’s AI solutions.

Interesting read?

Capgemini’s Innovation publication, Data-powered Innovation Review | Wave 8 features contribution from leading experts from Capgemini and esteemed partners like Dassault SystèmesNeo4j, and The Open Group. Delve into a myriad of topics on the concept of virtual twins, climate tech, and a compelling update from our ‘Gen Garage’ Labs, highlighting how data fosters sustainability, diversity, and inclusivity. Embark on a voyage of innovation today. Find all previous Waves here.


Weiwei Feng

Global Generative AI Portfolio Tech Lead, Insight and Data, Capgemini
Weiwei is a deep learner and generative AI enthusiast with a knack for turning complex algorithms into real-world magic. She loves hunting down fresh ideas and transforming them into scalable solutions that industries can’t resist. Think of her as the bridge-builder between futuristic research and practical.