Enterprise AI workspaces · one bot per project
Employees start work from a single entry point, without installing an environment, obtaining keys or selecting a model. The models, agents and data available to each of them follow their project role. Every run is recorded on one ledger: who started it, and what it cost.
Who uses it
One set of workspaces, permissions and execution records underneath, with different priorities on top. A company needs AI doing actual jobs; a public institution needs the boundary settled first; an individual needs one place that holds the project and its history.
Companies
Public institutions
Individuals
Core value
One entry point, so employees build nothing themselves. One set of permissions, deciding what AI may see and do. One ledger, where every run is costed and traced. Miss any of them and AI stays with a few people trying it privately.
What it looks like
These are real product screens, and each can be opened full size. People, projects, agents, models and operating figures have been replaced with demo data.
The entry point, the permissions and the ledger are spread across seven screens. Employees use the first four daily; administrators oversee the last three.
Staff who do not know how to prompt can follow a workflow or bring in an AI team divided by role; agents arrive as sealed packages the platform distributes.
One set of permissions, in detail
Before deployment, we confirm data boundaries, record retention and integration scope with your team.
See how governance is set upHow far it reaches
Four forms, the same core capabilities. What differs is where the data sits and who runs it.
Hosted by us, ready to use.
Ask about itFor departments and whole companies, with governance fully open.
Ask about itIn your own environment, or sensitive data inside and general work outside.
Ask about it
The company still needs to know who started the work, what it used, what happened and where the output went.
We call that a finished run: the record stays with the project, someone owns the result, and a colleague can take the work forward later.
Select work a team does every week. Run it for four to six weeks, compare the result against the baseline, then decide whether to continue.
Discuss a pilotInsights
Decide whether employees need a good tool, or the company already needs to manage accounts, data, cost and records across teams.
Read itWhose identity it runs under, which actions wait for a person, and whether a run can be reconstructed afterwards.
Read itA supplier invoice shows a total; project, person and task attribution has to be recorded separately.
Read itAn enterprise AI workspace: one entry point into the projects you may access, capabilities opened by project role, every run on one ledger.
Personal tools finish one person's task. We manage what happens once a team shares projects, data and cost.
Usually not. We inventory what you already run, and confirm the connection scope during a pilot.
Members and roles, workspace requests and instances, model channels, user quotas, project sharing, and AI conversation records with status and usage.
Nothing is lost. The workspace belongs to the project, so an administrator takes it over with sessions, files and records intact.
Yes — alongside SaaS, enterprise and hybrid deployment. The data boundary decides which fits.
Tell us which teams use which AI tools, and what is hardest to manage across cost, access, handover or execution records.
Scan to contact our business team