We’ve helped global enterprises unify fragmented AI initiatives under scalable Centers of Excellence—where shared infrastructure, evaluation standards, and rollout governance deliver real results, not overhead.
Our teams include MLOps architects, solution engineers, and compliance specialists who’ve shipped AI into pricing engines, clinical systems, and regulated production stacks—where downtime or audit gaps aren’t acceptable.
We’ve standardized AI operations across 10+ business units—eliminating duplication, aligning reuse policies, and accelerating compliant deployment across healthcare, pharma, and financial ecosystems.
From registries and prompt libraries to policy engines and fine-tuning workflows, we implement components that teams actively use—with adoption plans, documentation, and traceability baked in.
It’s a structured function that centralizes AI strategy, tooling, and governance — enabling reuse, enforcing compliance, and accelerating delivery across business units.
An AI CoE helps reduce duplicate AI spend, improves model reuse, enforces risk controls, and aligns AI efforts with business strategy — especially at scale.
Teams typically include data scientists, MLOps engineers, solution architects, product managers, and risk/compliance leads, overseen by an executive steering committee.
Centralized CoEs manage all AI delivery; federated CoEs define standards and tooling, while letting business units execute locally. Most large enterprises adopt a hybrid model.
Faster time-to-production, reduced rework, stronger governance, better compliance readiness, and broader cross-BU adoption of AI capabilities.
Yes — our CoEs are built to support both classical ML models and GenAI stacks, including LLM orchestration, prompt libraries, and RAG pipelines.
Begin with a maturity and delivery model assessment — including AI use cases, tooling, org structure, and governance gaps — to define a tailored CoE roadmap.
