MLOps Consulting Services for Enterprise AI

MLOps manages the lifecycle of traditional machine learning models. LLMOps extends these practices to large language models and RAG systems, adding prompt versioning, evaluation, and cost tracking. GenAIOps covers the broader operation of GenAI applications and agents, including orchestration, observability, governance, and quality control.

Before delivery begins, we agree on the permitted data access, repository boundaries, tool use, logging requirements, human-review checkpoints, and release controls for the engagement. Client code or data is used with AI tools only when the engagement’s approved tool, access, and data-handling rules permit it.

Yes. Capital Numbers provides ongoing MLOps managed services covering drift monitoring, retraining, infrastructure updates, and governance reporting once your systems reach production.

MLOps implementation services may cover pipelines, deployment, monitoring, LLMOps, GenAIOps, cloud infrastructure, governance, integrations, and contract-defined ownership of deliverables.

Timelines and cost vary by model count, environments, integrations, security controls, platforms, pipeline maturity, and governance scope. We provide a clear estimate after assessment.

Yes. We integrate with existing tools and support cloud, hybrid, or on-premises environments when architecture, security, and platform constraints allow.

Yes. Our MLOps Readiness Assessment provides a structured MLOps maturity assessment of your lifecycle, platforms, pipelines, monitoring, controls, and operating model. It concludes with prioritized recommendations and a phased implementation roadmap.