Enterprise AI adoption
Provision. Observe. Govern.
The control panel for AI adoption across the company.
Standardize how teams work with AI, see adoption across supported tools, and keep organizational guardrails current from one place.
- Six supported AI tools
- Built for employee devices
- No prompts or chats collected
Supported employee-device AI tools
- Claude Code
- Cursor
- Gemini CLI
- Copilot CLI
- Kiro
- OpenCode
- Engineering14 skillsReady
- Support9 skillsUpdated
- Finance6 skillsReady
- Marketing8 skillsReady
- Data7 skillsReview next
shared capabilities
capabilities used this week
roles equipped
supported AI clients
The adoption gap
AI adoption is happening without a shared operating view.
Teams are moving quickly with different AI clients, but leadership cannot see the whole rollout and employees repeatedly rebuild the same setup. The result is fragmented adoption, uneven standards, and no reliable baseline for improvement or governance.
No visibility
Leadership sees purchased licenses, not which teams, tools, and shared capabilities are actually being adopted.
Limited control
Useful skills, MCP connections, and instructions remain scattered across employee laptops and separate AI clients.
Fragmented guardrails
Approvals, updates, drift, and recalls are managed tool by tool instead of at the organization level.
How Bakara works
One organizational layer across the AI tools employees already use.
Bakara controls the shared setup and its lifecycle while teams keep working in their preferred supported clients.
Provision
Deploy role-specific skills, MCP connections, and instructions across the AI tools employees already use.
Observe
See usage, adoption gaps, client coverage, and drift through the signals each supported integration exposes.
Govern
Approve, update, repair, and recall managed capabilities from one organizational control panel.
Three common use cases
Start where enterprise AI adoption is already breaking down.
The first value is operational: make rollout consistent, make adoption visible, and keep managed configurations current.
Standardize role-based rollout
Give developers, analysts, and other roles a ready-to-work setup without forcing the company onto one AI client.
Find adoption gaps
Give leadership one content-free view of rollout, activation, repeat usage, and coverage across supported tools.
Keep every setup current
Detect configuration drift, repair managed loadouts, and recall obsolete capabilities without chasing individual devices.
A clear privacy boundary
Control the agent setup, not the conversation.
Leadership gets the signals needed to operate adoption and governance without turning Bakara into an employee-surveillance layer.
Content stays private
Prompts, conversations, and source content do not become Bakara telemetry.
Leadership gets an operating view
Rollout, activation, repeat usage, capability reuse, and configuration drift become visible without conversation surveillance.
Request a demo
Make your AI operating model deployable.
Start with one role, package the workflows that already work, and distribute them through the AI tools your teams already use.