AI access is easy. AI governance is the next problem.
We’re live on Product Hunt today with the new Intrascope update.
Intrascope started as a BYOK workspace for teams using multiple AI models.
Now we’re adding managed AI usage as well, so companies can access top AI models without managing separate API keys, vendor accounts or billing dashboards.
The goal is simple:
Help teams use AI in a more organized way, with permissions, spending limits, usage tracking, projects and shared context in one workspace.
We believe that as AI adoption grows, using AI without governance will become a real problem for companies.
Would love your feedback:
https://www.producthunt.com/products/intrascope-app?launch=intrascope-byok-managed-ai-for-teams
462 views
Replies
Congrats on the launch and good luck today! 🚀
@elara_thorn Thanks a lot, really appreciate it!
We see this as an important update because it opens Intrascope to a new group of users: teams that want AI governance, permissions, spend control, and visibility, but don’t want to deal with API key setup or vendor configuration.
governance + spend limits at the org level is the macro version of what we're shipping at the runtime level (tokenwall.dev, also launched today, circuit breaker for runaway agent loops at iter 3). adjacent problems, different layers. how are u thinking about per-agent vs per-user attribution in intrascope? that's the granularity question that keeps coming up in our convos.
@atul_yadav20 Exactly, same problem, different layers.
Intrascope currently tracks usage by workspace, project, user, and model. Per-user attribution is useful for team accountability, but we see per-agent / workflow attribution as the next important layer as companies move from chat usage to automated AI workflows.
The goal is to separate human usage from agent-driven spend, instead of everything being hidden behind one shared API key.
@intrascopeai agree on per-agent / per-workflow becoming the next layer. the gap i see most often: workflows aren't first-class objects in most stacks, teams have agents and prompts but no workflow id. so even when they want per-workflow attribution they can't surface it cleanly.
curious how intrascope handles that, do you require teams to tag workflows manually or do you infer them from call patterns?
the governance gap is real and i think most teams don't feel it until something goes wrong.
everyone's focused on getting access to models, nobody's thinking about who used what, when, and with what context.
congrats on the launch!
Exactly@riya_pariyar . That is the real problem we are trying to solve.
Most teams start by giving everyone access to AI models, but very quickly the question becomes: who used which model, in which project, with what context, and under which limits?
That is why Intrascope is built around governed workspaces, project-level context, model permissions, and usage visibility. The goal is to make AI usable across teams without losing control.
Congratulations and good luck on your launch! 138 up votes in 2 hours is great work!!! Here to support!
@wereframe Thanks a lot Neal, really appreciate the support! 🚀 Still a long day ahead, but the early response has been amazing.
@intrascopeai A long, but very valuable information and feedback for your product and vision. Such a great community wanting everyone to succeed. I’ll be in that “long” day here soon haha.
governance is the part everyone skips until the bill surprises them. project-level permissions and spend visibility should be the default, not an afterthought
@tina_chhabra Couldn't agree more. Most companies only start thinking about governance after the first surprise bill or the realization that nobody knows who used what. We believe permissions and spend visibility should be built in from day one.
Yup, definitely feel this.
I'm working in a totally different space with @Basedash: AI data analyst, but governance is still the hard part, not AI. Companies need to manage who should have access to what, and AI can only help with that peripherally.
Congrats on the launch!
@maxmusing Thanks! And yeah, governance seems to be the common challenge no matter which part of the AI stack you're building. Congrats on the launch too! 🚀
Strong framing — access stopped being the bottleneck the moment ChatGPT showed up in every team's chat. Now it's all about visibility and policy without slowing teams down. Curious how you handle shadow-AI usage that doesn't go through any official integration.
@denis_berenov Great point. Shadow AI is tough because it sits outside the official stack. We try to make the managed workspace easier to use than personal accounts, with shared projects, limits and spend visibility in one place.
This is becoming a real issue as teams move from everyone tries AI to everyone uses iy daily. Access is easy but keeping spend, context and permissions under controls is the messy part. Shared limits per project sounds especially useful.
@furkan_kara1 Once AI becomes daily work, visibility and control become the hard part. Shared project limits help teams stay organized without slowing people down.
This is becoming a real team problem not just an AI problem. The easy part is giving everyone access, the hard part is knowing what's being used by who and at what cost. Shared context and limits makes sense here
@ada_johnsen Totally agree. Access is easy now, but knowing who uses what, for which project, and at what cost is the real challenge. Shared context and limits solve a big part of that.
Cheers on the launch! @intrascopeai Best of luck today!
@alyona_popova1 Thanks!🙏