Enterprise AI workspace
Employees start work from one entry point, without installing an environment, hunting for keys or picking a model. What each of them may use — models, agents, data — follows their project role. And every run lands on one ledger: who started it, and what it cost.
Who uses it
One set of workspaces, permissions and execution records underneath. A company wants AI doing actual jobs; a public institution wants the boundary settled first; an individual wants one place that holds the project and its history.
Companies
Public institutions
Individuals
Core value
Miss one and AI stays where it is: a few people trying things on their own accounts. Hold all three and you can hand it to a department, then to the company.
One entry point
Signing in drops an employee into the projects they may access. The runtime, the models, the agents and the keys are already configured. Work that used to live in personal accounts and personal laptops comes back into the project.
One set of permissions
Project roles set the models, tools, files and actions available. Agents that support permission prompts ask before running a tool or a command. Opening up access is not the same as opening up the boundary.
One ledger
Who started it, which project, which workspace, which model, how many tokens and how much money — all on the same record. Enough to review the work, charge it back, or hand it to someone else.
You do not have to lock everyone into one AI tool. We manage the models, agents and workspaces that are already integrated and licensed. During a pilot we check interfaces, licensing and deployment conditions item by item.
What it looks like
These are real product screens, and you can open each one full size. People, projects, agents, models and operating figures are replaced with demo data.
How it lands
Six steps, and a piece of AI work has an entry point, a boundary and a ledger entry. None of it is an extra approval layer; it is the path the employee was going to walk anyway.
An employee signs in through the platform and starts inside a project they may access.
The employee requests an environment; an administrator reviews and assigns resources.
The workspace exposes the models, agents, project files and tools configured by the company.
Project roles limit access. Supported agents request approval before running tools or commands.
User, workspace, project, runtime, model, status and token usage remain searchable.
Projects, files and sessions can be shared by role and expiry, then revoked when needed.
The entry point, the permissions and the ledger live across these seven screens. Employees use the first four daily; administrators watch the last three.
Workbench
Employees do not install an environment or track down keys. The workbench guides them through a workspace request, and they enter the project once an administrator approves it.
Workspace
The workspace is the project's own environment. Files, context, tools and execution records live inside it, and a colleague can be added to the same workspace and continue.
Runtime
Behind each workspace sit real vCPU, memory and disk quotas running on your own hosts. Administrators see what was allocated and what is actually in use, and can expose a service running inside a workspace by port.
Discover
Employees see only the capabilities connected by the company and allowed for the current project. They can switch models or agents without moving project files elsewhere.
Share center
The team can publish prompts, task templates and processes to the share center. They remain available when people change roles or leave the project.
AI quota
Quota is assigned by user and plan. Employees see what remains, while administrators can review usage by workspace, project, runtime and model.
Admin console
Administrators handle workspace resources, member access, model providers, approval requests and login records here.
On top of the core
The three things above decide whether you can open AI up at all. This layer decides whether people use it once you do: pick a workflow and follow it, or call in an AI team already split by role. Not required — but it sets how fast AI spreads inside the company.
Workflows
A workflow breaks the job into steps, and each step carries the skills that step needs. Starting one creates a visual workflow: nodes advance in order and the output is filed back to the project.
AI teams
The capability market holds AI teams packaged by function: academic research, design, engineering, finance, healthcare, marketing. Each team is split by role and carries the skills that kind of work needs.
Current product scope
We separate ready-to-use functions from items that need on-site confirmation, so you can estimate the implementation work.
Available now
Configuration-dependent
Permission prompts differ by agent and are configured for the deployment in use.
Confirmed in pilot
Technical access is not the same as a stable interface or commercial permission. Each item is checked before delivery.
One set of permissions, in detail
Before deployment, we confirm data boundaries, record retention and integration scope with your team.
How far it reaches
Start with one team, or plan for the whole company. The three core capabilities hold in any of these; what changes is where the data sits and who operates it.
Hosted by us and ready to use, for teams validating the value first.
For departments and companies past the trial stage, with the full directory, approval and governance set.
Run inside your own environment, or keep sensitive data internal while general work uses external models.
Your team 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 pick up the work later.
Pick work a team does every week. Run it for four to six weeks, compare it with the baseline, then decide whether to continue.
Discuss a pilotPricing depends on team size, deployment, AI resources and integration scope.
An enterprise AI workspace. Employees enter the projects they may access from one entry point, project roles decide which models, agents and data they may use, and every run's usage and cost lands on one ledger, so the work survives a handover.
Personal AI tools are useful for individual work. We add shared project workspaces, company access rules, quotas and execution records, so colleagues can work together or take over later.
Usually not. We first inventory your models, knowledge bases, repositories and business systems, then confirm the integration scope during the pilot.
Today you can manage members and roles, workspace requests and instances, model channels, user quotas, resource sharing and AI conversation records. Usage is searchable by user, workspace, project, runtime and model. Tool approval, sandbox boundaries and external integrations depend on the agent and deployment setup.
Logs are one part of it. Project roles control access, quota windows limit consumption, and supported agents ask for approval before running tools or commands. Administrators can then search records by user, project, model and status.
Yes. Alongside the SaaS service, enterprise, private and hybrid deployments are available so data boundaries and model access can match your requirements.
Employees do not install an environment or configure keys. On first sign-in the workbench guides them to request a workspace, and they begin once an administrator approves it.
A pilot normally uses one engineering or IT team and one or two recurring tasks for four to six weeks. Time, rework, cost and results are recorded before the pilot and compared afterwards. Pricing depends on team size, deployment, model and execution resources, governance requirements and integration scope.
Tell us which teams use which AI tools, and what is hardest to manage across cost, access, handoffs or execution records.
Scan to contact our business team