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Comparison

Raidu vs Liminal AI

Liminal gives you a governed ChatGPT inside a workspace. Raidu proves your governance worked across every model and connector your enterprise actually uses, inside or outside that workspace. A workspace is a destination. An Accountability Layer is the substrate.

Liminal AI: Governed AI Workspace (SaaS) Raidu: AI Accountability Layer
Liminal AI

What it is

A SaaS workspace where users access AI through Liminal's controlled UI with PII protection, policy guardrails, and prompt sharing. Users sign into Liminal, type into Liminal, get answers from Liminal. Governance applies to interactions inside the workspace.

Raidu

What it is

The AI Accountability Layer. Raidu intercepts AI traffic across every tool the enterprise uses (Cursor, Claude Code, Cline, Continue, Windsurf, internal apps, agents, third party SaaS where outbound traffic can be routed) and produces a per interaction signed record. Governance applies to every interaction, regardless of UI.

How a governed workspace differs from an Accountability Layer

A governed workspace is a destination. Users go to the workspace, interact with AI inside it, and the workspace enforces guardrails on what happens within its UI. The unit of governance is the workspace session.

An Accountability Layer is a substrate. AI traffic flows through it from any UI: developer tools, agents, SaaS, internal apps. The unit of governance is the AI interaction, regardless of where it originated.

A workspace solves a bounded problem (give this user group a safe AI). An Accountability Layer solves an unbounded problem (govern every AI interaction the enterprise produces).

Side by side

DimensionLiminal AIRaidu
CategoryGoverned AI workspace (SaaS)AI Accountability Layer (runtime)
CoverageInside the workspaceAll enterprise AI traffic that can be routed
User experienceLiminal UINo UI change to underlying tools
Developer tools (Cursor, Claude Code, Cline)Out of scopeNative integrations
Agent trafficLimitedFive checkpoint runtime
PII redactionYes, in workspaceYes, on all routed traffic, 99.2% across 60+ entities
Per interaction signed recordLogs availableRSA-4096 signed, SHA-256 chained, WORM stored
EU AI Act Article 12 loggingInside workspaceAll routed traffic
DeploymentSaaSCloud, Dedicated VPC, Self hosted, Air gapped
ModelsMostly OpenAI / Anthropic175 models across 24 providers

When to pick which

Pick Liminal alone when the requirement is to give a specific user group (legal, finance, customer support) a single governed UI for AI. The buyer is the team owner who wants a turnkey product.

Pick Raidu alone when the requirement is to govern AI traffic across multiple surfaces (developer tools, agents, internal apps, third party SaaS) with regulator readable evidence. The buyer is the CISO or CTO standardizing AI usage at the enterprise level.

Pick both when you have a target user group that benefits from a packaged workspace and a broader enterprise that needs runtime accountability across everything else. The Liminal traffic can be routed through Raidu so all interactions live in the same signed chain.

The structural difference

A workspace bounds the problem to its UI. An Accountability Layer bounds the problem to its runtime, which is wider. For a regulated enterprise running AI in multiple places, the workspace is a feature inside the broader accountability question, not a substitute for it.

Where to read more

Common questions

Buyers ask, before they pick a side.

Why pick a workspace over an Accountability Layer? +
A workspace is the right answer when the requirement is to give a defined user group a single safe place to use AI: legal team using ChatGPT, customer service using a governed assistant. The workspace bounds the surface. It does not address AI usage outside that surface.
Why pick an Accountability Layer over a workspace? +
Because most enterprises have AI traffic in many places: developers in Cursor, support agents in Zendesk, analysts in Excel copilots, in house agents calling third party APIs. A workspace cannot govern what does not happen inside the workspace. Raidu intercepts the traffic regardless of the originating UI.
Can I run both? +
Yes, and many enterprises do. Liminal serves users who want a governed AI UI; Raidu provides the runtime substrate that records every interaction across every other AI tool the enterprise uses. Liminal traffic can also be routed through Raidu so its interactions land in the same signed audit chain.
Which one helps with the EU AI Act? +
A workspace addresses Article 13 (transparency to users of the AI system) for users inside the workspace. Raidu addresses Article 12 (automatic logging of high risk events) and Article 13 across all enterprise AI traffic, not just one workspace's. For a high risk AI use case spanning multiple tools, the workspace alone is insufficient.
Which one helps with HIPAA AI? +
Both can help inside their respective scopes. The HIPAA AI rule expected May 2026 inherits the Security Rule's audit trail and access control requirements. A workspace can prove compliance for AI usage inside the workspace. Raidu proves compliance across every AI surface that touches PHI, which usually includes more than one workspace.
What about agent traffic? +
Agents tend to make many AI calls per task across multiple tools and connectors. A workspace typically governs the user prompt entering the agent, not the chain of model and tool calls the agent makes downstream. Raidu's five checkpoint runtime governs the full agent loop (User Input, Before LLM, Before Tool, After Tool, Agent Response).
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See it in production

Decide on the proof, not the pitch.

Bring a use case. We will show you the runtime, the signed record, and what a regulator readable trail looks like for your AI stack. Thirty minutes.

Book a demo → What is an Accountability Layer?