Transcend

Transcend is positioned as a real-time data governance and decision layer, with the core purpose of answering "Can I use this data?", thereby unblocking permissions for AI, agents, and first-party data initiatives and accelerating enterprise adoption of AI and agents.

[Main Capabilities]

1. Agent and data controls: Provides data permission infrastructure for agent control. All agents pass through a unified policy layer before acting, centrally enforcing business rules, regulations, and customer preferences.
2. Agent policy as code (Transcend Rails): Deploy once, use everywhere at enterprise scale. Designed for the most sensitive data use cases, it controls what each agent can browse, consume, and deploy at runtime.
3. Always auditable: Every decision is recorded, not sampled. Each record captures the agent, tool, role, data, policy version, and outcome, using open standards such as OTEL.
4. Technology-stack agnostic: More than a tool, it is the critical data context and decision infrastructure enterprises need to scale agents and AI.
5. Run the platform in your own environment: The data permission layer unifies every record, agent, and system. Through Sombra's secure-by-design architecture, it provides end-to-end encryption to protect sensitive data use cases.
6. Consent and preference management: Collect consent, sync preferences, and enforce them instantly. Enforce business consent and preferences on web, mobile, and backend so permissions stay in sync everywhere customer data resides.
7. Real-time decision API: The MCP-native decision API controls what agents can access and do without rewriting the data layer.
8. Automated data subject request (DSR) fulfillment: Automated across all systems, eliminating backlogs.
9. Policy as code: Encode data policies into infrastructure without every request going through a manual review queue.
10. Answer in real time: "Can an agent use this data for this purpose?": Column-level classification, updated in real time.

[How to Use] Enterprises can use Transcend as a data permission layer, putting permissions and policies into the infrastructure once so that every new AI initiative automatically inherits controls. With agent policy as code, deploy once and use it in any technology stack. The platform supports running in your own environment. Users can get in touch by submitting a form or learn more by joining an online seminar. Specific integration steps and code examples are not currently provided.

[Applicable Scenarios]

• Enterprises accelerate AI and agent adoption while controlling data access.
• Need to unify consumer preferences, business policies, andOrganizations required by jurisdictions.
• Need real-time enforcement of consent and preferences to activate authorized audiences at a larger scale, improving return on ad spend (ROAS) and customer lifetime value (LTV).
• Privacy and compliance teams that need to automate DSR fulfillment, eliminate backlogs, and meet audit requirements.
• Enterprises that want to control agent behavior through MCP-native APIs without rewriting the data layer.

[Limitations] The materials do not provide specific information on limitations such as pricing, free trials, supported platform languages, or deployment details; these facts are not yet available.