We offer a comprehensive suite of machine learning, data analytics, and AI offerings in the context of engineering and R&D, extracting value and insights from data where the data or problem has a strong engineering or scientific focus. We provide consulting services and analytics-as-a-service; supported by methodologies for Trusted AI in industrial environments and AI deployment at scale (AIDevOps).
As digitalization progresses, firms competing using data science and AI are already outperforming those that continue to operate traditionally. There is a race to invest in the use of AI to add speed, scale, creativity, and cost control to their products and services – without sacrificing end-user or market confidence.
However, most companies face challenges such as:
- Understanding how to charter, design, execute, and deliver an AI data science project from start to finish
- Lacking the ability to progress from model PoCs to the real world
- Mistrusting automated decisions that are neither transparent nor explainable (Trust in AI systems)
WHAT WE DELIVER
Capgemini Engineering Global Service Line specialized in Data Analytics and Artificial Intelligence combines AI, machine learning, and enterprise-level data engineering to help clients improve profitability and performance.
We design, co-create, validate, and support the deployment of trusted AI solutions at scale in very different domains like drug discovery, understanding production quality variations, telco network performance, or train door reliability.
- Analytics Services
- Data Science Partnership (analytics as a managed service)
- Human Centric AI
- Trusted AI
- AIDevOps (for drug discovery)
HIGHLIGHTS OF OUR NEXT CORE PORTFOLIO
OUR SUCCESS STORIES
Rare diseases are very hard to diagnose because most doctors may never have seen such a case before.
Our algorithm uses cognitive computing to exploit the global body of knowledge on diseases and their symptoms and achieves world-leading accuracy for over 7000 diseases, providing doctors with a shortlist of probable conditions.
A major pharmaceutical company decided to unify, connect, and integrate all internal data and research silos to increase efficiency and speed of ML model development and deployment. We led technology assessment, design, and development of AI Ops platform, which connects research and development outcomes cohesively in the fields of drug discovery, diagnostics and clinical trials.
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