Employees already use AI on their own
Accounts, chat history and files stay in personal tools, so the company cannot see which projects rely on AI.
Bring scattered AI work back into company projects.A managed AI workspace for the enterprise
Teams use AI.You stay in control.
Projects, models, agents and files share one workspace. The business manages access, cost and audit records centrally.
- One entry point for AI work
- Projects, workspaces, shared resources and account settings sit in one place.
- Know where AI cost goes
- Review use and consumption by person, project, team and model.
- Methods survive handoffs
- Pass templates, source material and workflows to the next person.
Stacklane-ai is a managed AI workspace for the enterprise.
Employees start from one workbench, enter a project and use models, agents, files and tools. Leadership, IT and security teams manage accounts, permissions, budgets, logs and audit records in the same platform. Existing models, knowledge bases and business systems can be connected within the agreed delivery scope, so they do not all have to be replaced at the start.
Companies usually need a shared AI workspace when these problems start to appear.
Employees usually have access to AI already. The hard part begins when scattered use makes collaboration, cost and security difficult to manage.
Several teams are buying AI
Departments buy separate accounts, APIs and models, making use, ownership and duplicate spend difficult to explain.
Manage accounts, models, usage and budgets together.Every project handoff needs a reset
Background material is spread across local files, chats and tools, so a new owner has to reconstruct the project.
Give the next person the files, history and working method.AI is reaching internal information
Employees upload documents, code and business data, but the company cannot tell who accessed what or which model was used.
Keep permissions, logs and audit records with the project.Front-line teams and management teams need different things from the platform.
People doing the work need fewer handoffs and less repetition. People managing it need visibility into use, cost and risk.
Front-line business teams
- Sales and marketing
- Complete research, proposals and content inside the project.Share customer material, brand requirements and common templates instead of repeatedly rebuilding the brief.
- Service and operations
- Use the same source material for recurring work.Keep common questions, handling steps and escalation rules inside the project for the next person.
- Engineering and delivery
- Keep code, documentation and task context together.Colleagues can collaborate, review and continue the work without another round of explanation.
Management and support teams
- Leadership
- See where AI is actually being used.Review activity and resource consumption by team and project before making the next investment decision.
- IT and platform teams
- Manage fewer scattered accounts and API keys.Configure users, projects, models and resources centrally, with integrations agreed to the actual requirement.
- Security, finance and procurement
- Keep a record of spend and use.Review permissions, consumption, cost and related logs as evidence for procurement reviews and audits.
How to start
Choose one real workflow and run the whole job.
There is no need to standardize every model and tool first. A pilot starts with one team, one recurring job and a clear set of acceptance criteria.
Agree on three things before the pilot: how the work is done now, what counts as acceptable, and who signs it off.
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Choose work worth changing
Start with a frequent task that has repeated steps, reviewable output and a business owner.
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Record how it works today
Document current time, people involved, rework, cost and quality before changing the workflow.
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Run real tasks in the workspace
Configure project material, models, tools, permissions and review steps, then let the team use them for actual work.
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Review before expanding
Compare time, output, rework and quality with the baseline. Extend only the parts that held up in real use.
The work and the controls live in the same platform.
These are current product screens. Employees work inside projects while administrators review organizations, resources and activity.
A useful pilot leaves four things behind.
A product demo and a few good conversations are not enough.
- A real business scenario that has run
- The team has completed actual input, processing, review and delivery.
- A written set of working steps
- Where the material comes from, how it is handled and who checks it are clear.
- Configuration that remains with the company
- Projects, models, templates and permissions can be used again.
- Data for an investment decision
- Time, output, rework, cost and quality have a before-and-after comparison.
Before expanding use, the business will usually check these points.
Who can use what, what it costs, which information was accessed and whether the work can be traced after the project ends.
How are projects and access separated?
Users enter only the projects and capabilities assigned to them. The company configures organization, project, model and resource boundaries.
Can cost be broken down?
Related use and resource consumption can be reviewed by person, project, team and model for budget and procurement reviews.
Can activity be traced?
The platform retains records related to projects, models and tasks as evidence for operating review and audit.
How are deployment and integration handled?
SaaS, hybrid or private deployment can be assessed against data and network requirements. Integration scope is agreed before the pilot.
Run a small pilot before deciding whether to expand.
Choose one team and one or two frequent workflows, then run for four to six weeks. Record the baseline first and review the result with the business owner at the end.
- Choose the team and workflowName the participants, real tasks, owner and acceptance criteria.
- Record current dataDocument time, output, rework, cost and quality first.
- Run real workThe team completes day-to-day tasks in the platform instead of using demo data.
- Review togetherCompare the result with the baseline, then stop, adjust or expand.
Questions companies usually ask before a pilot.
How is Stacklane-ai different from tools such as ChatGPT or Cursor?
Tools such as ChatGPT and Cursor help individuals complete specific tasks. Stacklane-ai is organized around company projects, bringing files, context, models, agents and collaboration into one workspace with central management for accounts, access, cost, logs and audit records.
Do our existing AI tools have to be replaced?
Usually not. Existing models, knowledge bases, repositories and business systems can be connected within the agreed delivery scope. The integration approach is confirmed before the pilot.
Will employees have to learn another complicated system?
Employees enter a project and use files, models and capabilities that are already configured instead of rebuilding the environment and context for each task. The pilot shows whether the team can use it comfortably in day-to-day work.
How are permissions, cost and audit records managed?
The company can configure user, model and resource access by organization and project, review related usage and cost, and retain work records for operating review and audit. Exact boundaries depend on the company configuration and delivery scope.
Can a pilot guarantee a tenfold efficiency gain?
We cannot promise the same gain for every role. An order-of-magnitude gain can be a pilot target for suitable high-frequency, repeatable and digital work. Actual improvement is judged against the baseline and final acceptance results.
Show us how your teams use AI today.
Tell us which teams use which tools and the one issue that is hardest to manage. We will tailor the demo and suggest a sensible pilot scope.
Scan to contact our team