The twelve questions we are asked on the call

An enterprise AI workspace. Employees enter the projects they may access from a single entry point, project roles decide which models, agents and data they may use, and the usage and cost of every run are recorded 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.

It stays. The workspace belongs to the project rather than the person, so an administrator takes it over from the console with sessions, files and execution records intact. No personal device has to be recovered, and no second handover is needed from someone who has already left. Sessions and files are also not casually deletable, so the record holds.

Yes. Whether the employee side may download project files is an administrative setting, enabled or disabled as required, not an open default.

Calls to external models leave through a fixed egress over an encrypted tunnel rather than varying with each employee's network. A stable outbound identity is easier for the provider to recognise and easier for you to audit.

Yes. Extensions that sit on top of an existing process are in scope for adaptation. During the pilot we review the requested additions alongside the process they serve, then confirm effort and delivery.

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.

Project roles decide which models, agents, data and tools each person may use. Downloads, outbound sends and production changes are granted separately, and supported agents can ask for confirmation before they run. Provisioning and revocation go through approval, usage is attributed to a person, a project and a model, and execution records stay available.

A gateway covers the model call: unified access to several models, quota allocation and call logging. Execution governance also covers project workspaces, files and context, what an agent is allowed to do, human approval and project handover. The two coexist — an existing gateway and its models can stay, and we manage the capabilities, permissions and execution records once they are connected and authorised.

Administrators can query AI conversation records by user, workspace, project, runtime and model, with run status, token usage and completion time, and set quota plans and per-user limits in the same place. Which fields are available depends on how a tool is connected, which is confirmed during a pilot.