AI & Analytics

Drive operational efficiency, great customer experiences and sustainable innovation through business and people-centric AI and machine learning stategies. Build or revisit your AI adoption strategy to take advantage of recent advances in natural language processing (NLP) advances, choose the right opportunities for AI use, address talent gaps, create meaningful AI governance and ensure quality training data.

Integrate generative AI, reinforcement learning and classic AI techniques into all you create and do across the enterprise, from personalized customer experiences and dynamic pricing to augmented product design and rich strategic planning and scenario simulation. Identify and implement AI opportunities using human-centric AI-based approaches that outperform machine-centric approaches with less computing resources and data.

Deploy machine learning models to production faster through MLOps excellence. Build and train generative AI models fast and efficiently. Adapt the principles, practices and tools from Continuous Delivery to implement MLOps and LLMOps end-to-end processes, enabling increased automation, built-in quality, smaller, more frequent deployments, and repeatable, auditable processes.

Tap sophisticated techniques and tools to analyze and interpret complex data sets to uncover meaningful insights, patterns, and trends. Go beyond basic data analysis and include statistical analysis, data mining, predictive modeling, machine learning, and other advanced techniques to extract valuable information from data.