Platform, IT and security manage workspaces, model access, budgets and audit in one place.
Enterprise AI agent platform
Give every team an AI workspace your enterprise can govern.
One console for platform teams to set policy, budget and access. One workspace where employees run agents inside it.
Everyone runs agents from an approved project, with files and history that stay in the org.
Templates and skills become company assets instead of scattered personal habits.
One platform, two surfaces.
The console governs the work. The workspace is where the work happens. Both share the same policy, budget and audit layer.
For platform & IT teams
A central console to run AI operations.
Set launch templates, model channels, quotas and access from one back office, then watch usage and audit evidence build up as teams work.
- Workspace, agent runtime and model-access control
- Quota, credit and budget visibility per user and project
- Template library and capability marketplace
- Tenants, projects, roles and approval review
For every employee
A workspace that keeps the work in the org.
People pick a project, equip skills, choose a model and send the task. Process, files and history stay in one workspace the team can pick up later.
- Project context with isolated accounts
- Model choice: Claude Code, Fable, Deepseek and more
- Equipped skills from the shared capability library
- Files, logs and quota kept with the project
Governance
Make AI usage visible before it becomes shadow infrastructure.
The platform that starts the work also records cost, policy, risk and evidence.
Budget visibility
Read usage by person, project, department, model and agent workflow.
Sensitive-action control
Review high-risk file, command, network and model actions before they spread.
Audit-ready history
Keep session activity, tool calls, file access and model requests traceable.
Project operating model
Manage members, templates, policies and reusable output around the work itself.
A controlled path for every agent session.
People still move fast, but each step runs through identity, workspace policy, budget routing and audit capture.
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01
Select a project
The user picks an approved project space and agent template.
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02
Create the workspace
Stacklane applies repositories, tools, runtime, budget and access scope.
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03
Run the agent
Model and tool usage routes through policy instead of unmanaged credentials.
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04
Capture the evidence
Output, logs, cost and reusable practice stay with the organization.
Capability market
Turn good sessions into equipped skills.
Prompts, review checklists, delivery templates and operating procedures live in a shared market. Teams equip them per project instead of rebuilding them alone.
- Code review, debugging and engineering starters
- Research, writing and analysis workflows
- Versioned, published and reusable across tenants
Built for the teams making AI adoption real.
Engineering leaders
Standardize code generation, review, refactoring, testing and documentation workspaces.
AI platform teams
Manage providers, keys, templates, usage routing and internal AI operating standards.
Security and finance
See the cost, access, risk and evidence trail behind each agent session.
Pilot
Plan a governed AI workspace pilot.
We can map Stacklane to your team structure, model providers, repositories, access system and compliance needs.
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