Artificial Intelligence

Xebia provides end-to-end AI services including AI strategy and consulting, maturity assessments, use-case discovery, AI leadership consulting, design and implementation of scalable AI/GenAI platforms (MLOps, LLMOps, GenAI OS), solution development (e.g., demand forecasting, fraud detection, conversational agents), AI managed services (pipeline monitoring, platform support, FinOps), agentic AI development, and upskilling/training through Xebia Academy.

Applied AI focuses on traditional, production-ready models solving concrete business problems—like forecasting, document processing, and fraud detection. Generative AI centers on LLM-based capabilities and platforms: building GenAI OS, implementing LLMOps, fine-tuning models, building conversational agents and automation workflows. Both are delivered with production-grade practices and scalable infrastructure.

Xebia uses a Scaled GenAI Framework covering solution design, development and experimentation, secure deployment with MLOps/LLMOps and governance, operations and monitoring, and continuous improvement. They emphasize aligning use cases with business objectives, fine-tuning models with domain data, CI/CD pipelines, observability, guardrails, and ongoing retraining and metrics to sustain performance.

Yes. Xebia designs cloud-native and hybrid deployments tailored to your technical landscape. They partner with AWS, Google Cloud, Microsoft Azure and leverage best-of-breed open-source components to integrate model serving, data pipelines, APIs, observability and governance into existing environments with secure, scalable architecture.

Xebia implements secure governance, intelligent guardrails, monitoring, and model governance practices as part of deployment. They apply LLMOps frameworks, in-depth monitoring, human-in-the-loop validation, domain fine-tuning, and CI/CD controls to reduce hallucinations, enforce data protection, and maintain compliance across production systems.

Xebia focuses on measurable outcomes tied to specific business objectives—examples include improved demand forecast accuracy, reduced email triage effort, faster customer query resolution, and fraud reduction. They define actionable metrics during design, monitor model and process performance in production, and iteratively refine solutions to drive sustained ROI.

Timelines vary by scope: quick pilots or proof-of-value for single use cases can take weeks to a few months, while designing and implementing a scalable GenAI platform or enterprise-wide program typically takes several months. Xebia emphasizes rapid experimentation paired with a roadmap for production readiness and scaling.