Enterprise AI agent management and execution governance
We put project workspaces, models and agents, access, quotas and execution records in one place. Employees get a clear starting point. Administrators can see who used what and where.
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.
From access to review
We bring work scattered across personal accounts, laptops and chat histories back into the project workspace.
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.
Employees work inside project workspaces. Administrators manage access, resources and the same execution records.
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.
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.
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.
Real product screens
These are product screens. We replaced people, projects, agents, models and operating figures with demo data to protect internal information.
Security and boundaries
Before deployment, we confirm data boundaries, record retention and integration scope with your team.
Start with one team, or plan for the whole company. It depends on your stage and your data requirements.
Hosted by us and ready to use, for teams validating the value first.
For a department already rolling out, with directory, project spaces and usage reporting.
For companies managing AI use across the organization, with the full governance set.
Runs inside your own environment, with data boundaries and model access defined by you.
Sensitive data stays inside, general capability uses external models, split by scenario.
Let CRM, ERP, OA and ticketing systems use AI through one entry point.
We put the company entity, deliverables, acceptance measures and pricing basis in the project material and agreement.
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.
We help companies manage project workspaces, models and agents, access, quotas and execution records in one place. The work stays with the team when people change.
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