# Proxon

> Proxon is the management layer for your AI workforce. It discovers every AI tool, agent, and workflow running across a company, assigns each one an owner and a policy, attributes AI spend to teams and outcomes, and propagates the workflows that work best.

Proxon turns scattered AI adoption into one managed operating system: discovery, ownership, cost attribution, policy enforcement, and the propagation of high-performing patterns. Built for Heads of AI, CIOs/CTOs, CISOs, legal, finance, and operations leaders.

## Product

- [Proxon — The management layer for your AI workforce](https://proxon.ai/): Turn AI from scattered experiments into a managed system for discovery, ownership, spend, policy, and growth.
- [Product demo](https://proxon.ai/demo): Walk the full operating surface: adoption, org overview, agents and skills, inventory, outcomes, cost intelligence, and compliance.
- [Pricing](https://proxon.ai/pricing): Plans and pricing for the Proxon AI management platform.
- [Cost Intelligence](https://proxon.ai/cost): Attribute AI spend to vendors, models, teams, workflows, and business outcomes.
- [Compliance & Control](https://proxon.ai/compliance): Map AI data flows, enforce policies at execution time, and keep full audit trails.
- [Knowledge Transfer](https://proxon.ai/knowledge): Extract high-performing AI workflows and propagate them across similar teams.
- [AI Skill Levels](https://proxon.ai/levels): Explore the six levels of AI work, from safe first use to recursive improvement systems.
- [Ownership & Accountability](https://proxon.ai/own): Give every AI agent, workflow, and output an owner, a purpose, and a lineage trail.
- [AI Readiness](https://proxon.ai/compete): Benchmark AI readiness and build a deployment roadmap across every function.
- [Whitepaper](https://proxon.ai/whitepaper): The full Proxon thesis: AI workforce management, cost, governance, and propagation.
- [Blog](https://proxon.ai/blog): Research and analysis on AI governance, cost, ownership, and adoption.

## Industries

- [AI Management for Financial Services](https://proxon.ai/ft): Manage observed and registered AI systems, attributable adoption where identity is available, registered workflows, and available spend context across your financial institution.
- [AI Management for Software, Cloud, and Developer Tools](https://proxon.ai/sw): Manage observed and registered AI systems, attributable adoption, registered workflows, and captured or provider-reported spend across software delivery.
- [AI Management for Professional Services and BPO](https://proxon.ai/ps): Manage observed and registered AI systems, attributable adoption, registered workflows, and available spend context across client delivery and business-process operations.
- [AI Management for Healthcare and Life Sciences](https://proxon.ai/hls): Manage observed and registered AI systems, attributable adoption, registered workflows, and available spend context across healthcare administration and life-sciences work.
- [AI Management for Retail and Ecommerce](https://proxon.ai/ret): Manage observed and registered AI systems, attributable adoption, registered workflows, and available spend context across retail and ecommerce teams.
- [AI Management for Real Estate and Construction](https://proxon.ai/re): Manage observed and registered AI systems, attributable adoption, registered workflows, and available spend context across real-estate and construction organizations.
- [AI Management for Media and Telecommunications](https://proxon.ai/mt): Manage observed and registered AI systems, attributable adoption, registered workflows, and available spend context across media and telecommunications organizations.
- [AI Management for Energy and Natural Resources](https://proxon.ai/enr): Manage observed and registered AI systems, attributable adoption, registered workflows, and available spend context across energy and natural-resources organizations.
- [AI Management for HR and Workforce Teams](https://proxon.ai/hr): Manage observed and registered AI systems, attributable adoption, registered workflows, and available spend context across HR and workforce operations.
- [AI Management for Manufacturing and Industrial Companies](https://proxon.ai/mfg): Manage observed and registered AI systems, attributable adoption, registered workflows, and available spend context across manufacturing and industrial organizations.

## Guides

- [AI Usage Visibility Guide](https://proxon.ai/guide/ai-usage-visibility): How to see every AI tool, agent, and workflow running across an organization.
- [Shadow AI Discovery Guide](https://proxon.ai/guide/shadow-ai-discovery): How to find unapproved AI tools, agents, and data flows before they become incidents.
- [AI Governance Guide](https://proxon.ai/guide/ai-governance): How to apply ownership, policy, and audit controls to the AI systems a company runs.
- [AI Cost Intelligence Guide](https://proxon.ai/guide/ai-cost-intelligence): How to attribute AI spend to teams, workflows, and outcomes instead of invoices alone.
- [AI Adoption Measurement Guide](https://proxon.ai/guide/ai-adoption-measurement): How to measure who is getting better at AI across people, teams, and departments.
- [AI Policy Automation Guide](https://proxon.ai/guide/ai-policy-automation): How to turn AI policy documents into execution-time rules that actually enforce.
- [AI Vendor Inventory Guide](https://proxon.ai/guide/ai-vendor-inventory): How to maintain one inventory of AI models, tools, MCP servers, and data sources.
- [AI ROI Reporting Guide](https://proxon.ai/guide/ai-roi-reporting): How to connect AI usage to business metrics and report attributable gains.

## Resources

- [AI data collection: how Proxon sees your AI usage — Proxon](https://proxon.ai/data-collection): Every data collection method Proxon offers, what each one sees, and how to choose between them.
- [Proxon on your computer: what it can and can't see](https://proxon.ai/observers): What each observer running on an employee machine can and cannot see, in plain language.

## Documentation

- [Proxon Docs](https://proxon.ai/docs): Product documentation: collection methods, capture levels, observers, and the OpenTelemetry and Agent Observer integrations.

## Company

- [About](https://proxon.ai/about): What Proxon is and why AI needs a management layer.
- [Security](https://proxon.ai/security): How Proxon secures customer data and the AI systems it governs.
- [Careers](https://proxon.ai/careers): Open roles at Proxon.
- [Contact](https://proxon.ai/contact): Get in touch with the Proxon team or book a demo.

## Legal

- [Terms of Service](https://proxon.ai/terms): Proxon terms of service.
- [Privacy Policy](https://proxon.ai/privacy): Proxon privacy policy.

## Blog

- [Everyone You Work With Just Got Promoted](https://proxon.ai/blog/everyone-you-work-with-just-got-promoted): Introducing Proxon, the management layer for the AI workforce. (2026-07-11)
- [The AI Act Transparency Countdown: Why AI Disclosure Needs an Operating Record](https://proxon.ai/blog/ai-act-transparency-countdown): The EU AI Act is turning AI transparency from a policy statement into an evidence problem. Companies need a live record of systems, owners, data, decisions, and controls. (2026-06-20)
- [Shadow AI Is Now a Talent Problem, Not Just a Security Problem](https://proxon.ai/blog/shadow-ai-talent-problem): Unauthorized AI use is not only a control failure. It is often a signal that employees are moving faster than the company's enablement model. (2026-06-19)
- [Every AI Agent Needs an Owner](https://proxon.ai/blog/every-ai-agent-needs-an-owner): As agentic AI moves from prototypes into workflows, ownership becomes the core control. Without an owner, an agent is not managed work. It is organizational drift. (2026-06-17)
- [MCP Servers Are the New Shadow IT Inventory](https://proxon.ai/blog/mcp-servers-shadow-it-inventory): The Model Context Protocol is making tool access easier for AI systems. That also means enterprises need to know which MCP servers exist, what they connect to, and who owns them. (2026-06-16)
- [Why Agentic AI Projects Get Canceled Before They Scale](https://proxon.ai/blog/agentic-ai-projects-get-canceled): Agentic AI demos are easy to fund. Durable operating value is harder. Projects fail when cost, risk, ownership, and outcomes are not managed from the start. (2026-06-14)
- [The Agent ROI Gap: Why Early Adopters Pull Ahead](https://proxon.ai/blog/agent-roi-gap): AI agents are moving into production, but ROI depends on whether companies can connect agent activity to real workflow and business outcomes. (2026-06-13)
- [Ungoverned AI Is Becoming a Breach Multiplier](https://proxon.ai/blog/ungoverned-ai-breach-multiplier): AI expands who can touch data, how fast data moves, and where sensitive context can appear. Security teams need AI-specific access, inventory, and evidence trails. (2026-06-11)
- [Colorado's AI Law Shows the U.S. Will Stay Patchwork](https://proxon.ai/blog/colorado-ai-law-patchwork): State-level AI rules are moving faster than a single federal operating model. Enterprises need flexible AI records that can support different jurisdictions, notices, and review duties. (2026-06-10)
- [AI Usage Is Up. Workflow Redesign Is Still Missing.](https://proxon.ai/blog/ai-usage-workflow-redesign): AI adoption has crossed into the mainstream, but usage alone does not transform work. The next management challenge is redesigning workflows around what AI actually improves. (2026-06-08)
- [From Copilot Chats to Board Metrics: Measuring AI's Real Work](https://proxon.ai/blog/copilot-chat-board-metrics): AI interaction data is becoming a management asset. The companies that win will translate chat, agent, and workflow activity into board-level signals. (2026-06-06)

_Last updated: 2026-09-08. Sitemap: https://proxon.ai/sitemap.xml_