AI is spreading across every company, but most leaders are still managing it through scattered tools, policy docs, invoices, and anecdotes. Proxon is the system of record for your AI workforce: it connects to the AI tools, agents, and workflows your teams already use, then shows who is using AI, what it costs, what data it touches, what outcomes it creates, and where ownership or governance is missing.
Finally gave it a spin and the cost breakdown by team was genuinely eye opening, we had no idea how much some departments were burning on API calls. Mapping data exposure across all our agents in one view feels like the kind of thing every ops lead has been cobbling together in spreadsheets.
@glhaneb63 Thanks so much, Gülhan! "Cobbling together in spreadsheets" is exactly the pain point we were trying to solve with the data exposure mapping. We're thrilled to hear the cost breakdown is already bringing that level of visibility to your team. Let us know if there's anything else you'd love to see added!
the "who is using AI" question is the one I'd actually want answered honestly - a lot of the real exposure isn't the sanctioned agents your team built, it's someone pasting a customer contract into a random ChatGPT tab because it's faster than asking IT for access to the approved tool. does Proxon have any way to see that shadow usage, or is it scoped to the tools/agents that are already connected and reporting in?
@galdayan Hey Gal! You hit the nail on the head, that exact scenario (pasting contracts into unauthorized AI tabs) is what keeps security leaders up at night. To answer your question directly: yes, Proxon absolutely has a way to see that shadow usage. We aren't just scoped to the sanctioned tools; our platform is designed to map the entire shadow AI footprint across your organization so you can actually govern it
@pez_vortex good to hear it's not just scoped to the connected tools. curious how that detection actually works under the hood though - is it network/DLP-style traffic inspection, a browser extension, or something else? asking because that's also the part that determines how invasive this feels to the employees being monitored, not just how complete the picture is for leadership
Struct
Amazing team that is spot on with this problem. How does onboarding and setup work?
@nimeshmc Thanks so much, Nimesh! Really appreciate the kind words from the Struct team.
We designed setup to be highly modular so you don't need everything to begin. Most teams start by rolling out our three Core Observers (Browser, Desktop, and Network), which you can push seamlessly through tools you already use like Jamf, Intune, or Google Admin (or just let folks self-install).
That immediately gives you a baseline of how AI is being used. From there, you can layer on Targeted Sources—like direct vendor API integrations or our drop-in SDK for your custom agents—to get precise billing data. Happy to show you around if you want to see it in action!
Hey Product Hunt! 👋
I’m Santiago from Proxon.
A quick story on why we built this: Over the past year, almost every founder and operations leader we spoke to told us the exact same thing. Their teams were adopting AI tools, custom agents, and automated prompt loops incredibly fast, but leadership was completely blind to it. We kept hearing the same questions: What are these tools actually costing us? What data are they touching? And who owns them if something drifts?
Managing AI through scattered invoices, random policy docs, and manual spreadsheets just doesn't scale.
That’s why we built Proxon, the definitive management layer and system of record for your AI workforce.
Here is what you can do with Proxon starting today:
Discover the Workforce 🔍: Map every AI tool, agent, prompt loop, and shadow AI asset running across your organization.
Govern & Assign Ownership 🛡️: Establish data policies, review cadences, and approval paths so your team can move fast safely.
Attribute Spend & ROI 💰: Tie vendor and model costs directly back to specific teams and workflows instead of guessing at line-item invoices.
Propagate What Works 📈: Automatically extract high-performing AI workflows from your power users and deploy them as templates for the rest of the team.
We’ve been building this heads-down with a team that cares deeply about getting enterprise AI right. Today, we’d genuinely love your honest feedback: the good, the bad, and the rough.
I'll be right here in the comments all day, so ask me anything! What do you think? 👇
Maker here. We built Proxon because we were flying blind on what our own AI agents were actually doing — and spending. Every tool showed you tokens. None of them told you the work: which agent, which task, what it cost, whether it was worth it.
So we built the thing we needed. It captures every agent's activity and turns it into cost intelligence you can actually act on. We've been dogfooding it on ourselves for weeks and I genuinely can't run without it now.
This is day one for us. Would love your brutal feedback — we read everything. 🚀
Co-founder here. We built Proxon because everyone we talked to was burning real money on AI — Claude, GPT, Cursor, all of it — and nobody could say who was actually using it, whether it was helping, or where the spend went.
Every tool that tried to answer that felt like surveillance. We went the other way: aggregated, privacy-safe views for managers, personal dashboards for everyone else, and a little recognition when you ship your first agent. Less Big Brother, more leveling up.
Curious how you all handle this — how do you measure if AI is actually paying off at your company? Around all day, ask us anything.