Control enterprise AI: unified model, data and compute governance platform

Roughly 125,000 enterprises are actively embedding models into core operations and their security, compliance and MLOps teams are facing fragmented governance across models, data and compute—leading to inconsistent access controls, incomplete audit trails, and runaway inference costs that increase regulatory and operational risk. New laws like the EU AI Act demand explainability, auditable risk assessments and documented provenance, which current point solutions and homegrown stacks cannot reliably provide at scale. You could build a unified control plane that ties model artifacts, data lineage and compute orchestration into a single governance fabric: policy-as-code enforcement, immutable audit logs, automated risk scoring and explainability, plus a cost-aware routing layer that optimizes inference across cloud and on-prem resources. Expose open APIs and prebuilt connectors to MLOps, identity and cloud providers, and package enterprise offerings around a $500K ACV model (the $62.5B TAM from 125,000 enterprises) with professional services and SLAs to land proof-of-value deployments. The timing is favorable—AI-as-infrastructure, regulatory pressure and rising inference bills create buyer urgency; internal scoring gives this opportunity a Market Score of 92/100 and Revenue Potential of 90/100. To win in a medium-competition landscape you must demonstrate provable outcomes (meaningful inference cost reduction and measurable audit-time compression), pursue vendor-neutral integrations and compliance certifications, and be candid about the toughest hurdles: long sales cycles, complex legacy integrations and the engineering effort required to deliver ironclad provenance across heterogeneous stacks.