Guardrails for high-stakes AI

The guardrail layer for AI you can't afford to get wrong.

Every foundation model ships its own guardrails. Those guardrails protect the model. Alice protects you, across every model in your stack, every step of every agent, and every regulator you'll have to answer to.

What native guardrails leave on the table

Capability

Native Foundation-Model Guardrails

Alice

Multi-model
policy

Each vendor uses its own taxonomy and thresholds, so filters can't be reused across different model stacks.

One policy covers frontier, fine-tuned, or on-prem models with a unified audit trail.

Agentic workflow
governance

Since the model call is filtered tool calls and data passed between steps go unmonitored.

Every step is covered, including prompts, tool calls, retrieved context, and downstream actions.

Custom policy
enforcement

Limited to topics, blocklists, and PII, with complex rules leaking into system prompts as needs grow.

Your policies are enforced at the infrastructure layer, so they hold across model updates.

Automated
red teaming

You're responsible for testing your own deployment because vendors only test their models.

Adversarial testing on your configuration is ongoing, producing audit-ready evidence for regulators.

Compliance
documentation

There's no built-in reporting for EU AI Act, NIST AI RMF, or MITRE ATLAS, so you assemble the documentation yourself.

Risk classification, oversight controls, and conformity artifacts are generated automatically.

Coverage where
AI lives

Coverage is scoped to the API you call, so SaaS integrations and on-prem deployments fall outside its reach.

Covers cloud, on-prem, multi-cloud, air-gapped, and SaaS across 120+ languages and every modality.

Why high-stakes teams choose Alice

Customizable. Not off the shelf

Build your own enforcement policies and generate your own adversarial scenarios. Your risk model shapes the system, not the other way around.

Ongoing red teaming.

Adversarial tests run on a schedule, not a quarter. You find drift the day it starts, not the week before audit.

One layer across every model.

Add a vendor, swap a model, ship a fine-tune. Your policy doesn't change and your audit trail doesn't fragment.

Stable across model updates.

When the vendor ships a new version, your safety posture doesn't quietly change with it.

Built for agents, not just prompts.

Visibility and control across chains, tool calls, and downstream actions where most native systems go dark.

Enforcement, not advice.

Policy applied at the platform layer. The difference between asking an employee to follow policy and a compliance system that enforces it.

Trusted by the world’s biggest enterprises & foundation model labs

Where it matters most

Customer-facing Agents

Banking, telecom, insurance, healthcare, staffing. One bad answer becomes a regulator, a brand event, or a lawsuit.

Regulated
Copilots

Internal AI touching sensitive data, employment decisions, financial advice, or clinical workflows under sectoral rules.

Multi-agent Orchestrations

Production agent chains where a single bad step compounds across the workflow and the vendor can't see it.

Native guardrails stop at the prompt. Alice governs every step.

Alice provides one behavior control layer from pre-launch to production.

WonderBuild application inventory with four apps listed, showing status, assessments, and last assessment dates.

WonderFence Dashboard showing violation stats, enforcement rate, and six security recommendations.

WonderCheck dashboard showing risk distribution, app versions, tasks, and top violations by severity and prevalence.

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A decade of real-world adversarial data continuously updating, safeguarding 50% of the world's online experiences. Every test, guardrail, and evaluation runs on threats adversaries actually use.

3B+

Users

120+

Languages

1B+

Daily AI-Human interactions