Enterprise AI workspace and execution governance

AI does real work.Govern AI work.

Stacklane-ai governs employee and business-system AI tasks across identity, access, data, budget, execution and results.

Stacklane-ai employee workbench showing projects, workspace status and AI resources
Employees enter projects, request resources and start AI work from one workbench.

Manage every AI execution in one place.

Models and agents still do the work. Stacklane-ai links each execution to its owner, access rules, project, budget and result.

  • Who started it
  • What it may use
  • What it costs
  • What happened

Fragmented AI use

Accounts, models, API keys and work records sit across personal tools and business systems.

It is hard to verify who used AI, for which project, at what cost, with which data, or whether the result was accepted.

With Stacklane-ai

Each AI task is linked to identity, access, budget, execution and results.

Employees, business systems and agents follow one set of rules. Identity, usage, execution and results can be reviewed together.

One platform connects people, business systems and AI capabilities.

The employee workspace, business-system entry point and governance console share the same identity, access, budget and task records.

Stacklane-ai employee workbench with project access and workspace onboarding
Employees open the workbench to enter approved projects, workspaces and AI resources.
Employee workspace
Use approved models, agents, files and tools inside a project.
Business-system entry
Submit model calls or AI tasks through one company interface.
Execution governance
Manage identity, access, budgets, usage, execution records and results.
Stacklane-ai project workspace where an employee completes a task
For employeesUse project files, tools and company-approved AI in one workspace.
Stacklane-ai administrator console showing workspaces, model access and resources
For administratorsReview users, projects, workspaces, model access and resource use in one place.

Keep identity, access, cost and results connected.

From task start to acceptance, key records stay in one platform instead of personal accounts, chat windows and separate business systems.

Task record
Know who started it, what ran and how it endedKeep project context, execution, acceptance rules and results in one task record.
Access and data
Control what each task may useConfigure models, agents, tools, access and data rules instead of relying on unmanaged personal accounts.
Budget and usage
Keep every AI cost attributableReview usage and cost by person, project, team and model, with budgets set and checked in the same place.
Project continuity
Keep the work with the projectFiles, context and work records remain available for review, handoff and reuse.

Prove one reviewable task first.

Start with one engineering or IT team and one or two recurring tasks. Compare time, rework, cost and results over four to six weeks.

Discuss a pilot
Scope
One team and one or two recurring tasks
Period
Normally four to six weeks
Review
Compare the outcome with the baseline

What the pilot leaves ready for review

  • Configured projects, access, models and execution resources
  • Task baseline, acceptance rules, usage and execution records
  • A before-and-after comparison to stop, adjust or expand with evidence

Pricing depends on team size, deployment, AI resources and integration scope.

Common questions.

What is Stacklane-ai?

Stacklane-ai is an enterprise AI workspace and execution governance platform. Employees use approved models, agents, files and tools inside project workspaces. Business systems submit AI tasks through a common entry point. Administrators manage identity, access, budgets, execution records and results.

How is this different from personal AI tools such as ChatGPT or Cursor?

Personal AI tools help one person complete a task. Stacklane-ai does not replace them. It connects them to a company environment where each AI task has an identity, access rules, a budget, project context, execution records and a result.

Do we have to replace our existing AI tools?

Usually not. Existing models, knowledge bases, repositories and business systems can be connected within the agreed delivery scope. The pilot confirms what should be retained and what needs to change.

How are the pilot and pricing determined?

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.

Start with one real AI task.

Tell us which teams use which AI tools, and what is hardest to manage across cost, access, handoffs or execution records.

Phone400-030-8696 Email[email protected]
WeCom WeCom contact QR code Scan to contact our business team