Mantle Task-aware context
Evaluate information in the context of the work rather than treating every field identically.
Employee AI Workspace · Current prototype
Useful AI work, through a governed boundary.
Ask, attach and analyse in one workspace. Mantle applies task-aware data controls before an approved model receives the exact payload.
Control language
Clear treatments. Exact consequences.
01Keep02Mask03Remove04Human review
Product capabilities
Control around intelligence.
Available in prototype
Data governance for AI
Govern how organisational data is classified, minimised, approved, exposed, routed, retained and evidenced in AI workflows.
Policy enforcement
Keep deterministic organisation rules as the final authority over what AI may receive.
Exact outbound visibility
Show users and reviewers the precise payload before an external model call is made.
Human approval
Escalate ambiguous or higher-risk situations instead of silently allowing them.
Multi-model routing
Support approved routes chosen by task, data classification and organisation policy.
Audit and evidence
Record governed decisions around AI work without retaining raw removed values.
Permissions
Align access and actions with organisational authority—not simply model capability.
Architecture in development
Data → AI → action governance.
Data
What information may be used?
Classification · relevance · minimisation · permissions · retentionAI
Which intelligence may receive it?
Model eligibility · routing · private or local options · policyAction
What may happen next?
Propose · approve · execute · expire · auditPilot and design partner conversations
Bring one real AI workflow.
Start with one workflow, one team and one real policy boundary. We’ll explore where AI can create useful productivity without sacrificing control.