Meet Proxon: Building the system of record for the enterprise AI workforce
Hey Product Hunt! 👋
We are the team behind Proxon, and we’re incredibly excited to share a sneak peek of what we’ve been building ahead of our official launch on July 13th.
AI is spreading across every organization, but right now, managing it is chaotic. Most company leaders are stuck relying on scattered tools, policy documents, random invoices, and anecdotal feedback.
We built Proxon to serve as the definitive management layer and system of record for your AI workforce. It seamlessly connects to the AI tools, custom agents, and automated workflows your teams are already running to give you full visibility into:
Adoption & Flow: Who is utilizing AI and exactly which workflows they are running.
Financial Clarity: Real-time visibility into what your AI workforce actually costs.
Data & Governance: Exactly what data your AI touches and where security or compliance gaps exist.
True ROI: Tracking the actual outcomes and value these autonomous agents create.
As we gear up for the launch, we’d love to open up the floor early!
What are your biggest pain points when it comes to managing, auditing, or scaling AI agents in your current workflows?
Drop any questions, feature requests, or thoughts below. We'll be hanging out in the thread to chat!👇
Replies
For us-
it’s figuring out the best /goal prompts to get consistent and repeatable results.
This way, after then work finishes and our agentic playwright walkthrough finishes, we hand it to a human for a proper code review and QA. How do we systematize our process besides copy pasting prompts from a group discord thread or an internal skill library?
How do I enforce engineering compliance to prevent agents from reading sensitive info? Right now, we have a settings.json that blocks env var reads, but I need to script something for endpoint management to make sure we’re all using the same config.