AI access is easy. AI governance is the next problem.

@sanjau_sanjay Thanks Zachary, really appreciate it! 🙌 Big day for us and every bit of support means a lot.

@christian_onochie Yes, admins can control this on two levels.

First, access is handled at the project level. Users are assigned to specific projects, and each project contains its own context, files, instructions, and information related to that workspace. This way, team members only see and use the context they are supposed to work with.

Second, admins can define which AI models or vendors are allowed across the workspace. For example, they can enable certain models for the team and restrict others depending on cost, policy, or internal governance needs.

So the idea is not just “who can use AI”, but also “which project context they can access” and “which models they are allowed to use.”

@tessa_lynch Thanks Tessa, appreciate the support! 🚀 We’re excited to see how teams respond to the new version.

@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.

@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.

Hey @debra_salt ,

That’s a great point, and definitely the direction we want to go in.

Right now, Intrascope already provides analytics around usage and cost per model, per user, and per workspace, so managers can see how different AI models are being used across the team.

The next step is adding more business-focused metrics, not just consumption tracking. We want companies to better understand where AI is actually creating value, which workflows are being improved, and how teams are using AI across different projects.

So yes, usage reports for managers are very aligned with our roadmap.

Thanks Debra, fully agree. From the engineering side, we see usage and cost analytics as the foundation, but not the end goal.

The real value is helping managers understand which workflows are actually improved by AI, where adoption is creating measurable impact, and where teams may need better tools or guidance.

That is very much aligned with how we are thinking about the next layer of Intrascope. 

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.

@carolina_ellen Thank you Carolina, really appreciate it! 🙌 Excited to finally have this update live.

@wereframe Thanks a lot Neal, really appreciate the support! 🚀 Still a long day ahead, but the early response has been amazing.

@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.