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.
Stacklane-ai
Enterprise AI workspace and execution governance
Stacklane-ai governs employee and business-system AI tasks across identity, access, data, budget, execution and results.
Models and agents still do the work. Stacklane-ai links each execution to its owner, access rules, project, budget and result.
Fragmented AI use
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
Employees, business systems and agents follow one set of rules. Identity, usage, execution and results can be reviewed together.
The employee workspace, business-system entry point and governance console share the same identity, access, budget and task records.
From task start to acceptance, key records stay in one platform instead of personal accounts, chat windows and separate business systems.
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 pilotPricing depends on team size, deployment, AI resources and integration scope.
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.
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.
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.
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.