Proxon provides an AI management platform that creates a single operating record for all AI systems deployed across an organization. It enables leaders to inventory AI activity, assign ownership and policies, and link spend to business outcomes, helping to surface hidden usage and ensure accountability. The solution also streamlines workflows, allowing more teams to adopt AI safely and effectively.
Funding
Funding not disclosed
Founders
Product
Problem
AI systems are increasingly embedded in daily operations, yet organizations lack visibility into where these models are deployed, who owns them, and how they align with business goals. This opacity leads to fragmented accountability, unmanaged policy compliance, and difficulty demonstrating the value of AI investments.
Solution
Proxon offers an AI management layer that aggregates all active AI models across an enterprise into a single operating record. The platform automatically discovers existing AI deployments, creating an up-to-date inventory without requiring manual reporting. Governance tools let leaders assign owners, attach policy controls, and schedule reviews to maintain compliance as models move from experiment to production. By linking AI usage and associated costs to specific business outcomes, Proxon enables quantifiable attribution of spend to results. Integrated workflow capabilities allow teams to adopt AI responsibly while staying within organizational guidelines, turning hidden AI activity into a transparent, accountable asset.
Target Audience
Proxon is aimed at enterprise AI leaders, such as Chief AI Officers, MLOps managers, and compliance teams, who need organization‑wide visibility and control over deployed AI systems.
Features
- Automated discovery engine that scans cloud, on‑prem, and SaaS environments to build a real‑time inventory of AI models and services
- Centralized governance console for assigning ownership, defining policy rules, and scheduling model reviews or decommissioning
- Cost attribution module that maps compute and licensing spend to individual AI projects and downstream business metrics
- Role‑based access controls and audit trails to ensure compliance with internal and regulatory standards
- API and integration hooks for embedding governance checks into CI/CD pipelines and existing MLOps tooling