Your policy,
enforced properly in 24 hours
Off-the-shelf classifiers enforce someone else's policy. LLMs behind an API are accurate but too slow and costly to run on every message. Alice builds custom guardrails trained on your exact policy, live in 24 hours, running inline on 100% of your traffic, and improving every day after.
Get a DemoFrontier accuracy, small-model speed
A large model reads your policy and labels examples, recording its reasoning. A compact model learns to reproduce those judgments directly, with no reasoning at inference. You pay for the deliberation once, at training, and run something small and fast in production.
The result: the judgment of a frontier model, scored inline in under 120ms at P95, at a fraction of the per-call cost of an LLM, on every message, not a sample.
Red team, guardrail, repeat
1. Red team before launch
2. Turn the findings into guardrails
3. Optimize on a continuous loop
Each round mines your real traffic for what the model gets wrong, so the classifier keeps matching your policy as it actually appears in production. The whole round runs in 24 hours, unprompted.
How the classifier is built
Book a demoIt starts with a teacher and a student
Alice uses a distillation pipeline. A frontier model (the teacher) labels examples against your policy and records its reasoning. A compact model (the student) reproduces those judgments with no reasoning at inference. The deliberation is compiled once, at training, so what runs in production stays small and fast. Your policy itself arrives however it exists, a PDF, a compliance doc, or rules grown over years, and Alice reads it end to end, returning a validation pack so you confirm it was understood before any model is trained.
Then it's toughened against real-world input
Real traffic isn't clean prose. It arrives as HTML fragments, emoji standing in for words, letters s p a c e d out, lookalike characters, and deliberate evasion by people who know a classifier is watching. Alice normalizes every input to canonical form before scoring, using a preprocessing stack built from years of real abuse traffic, so evasion doesn't win just by reaching the model intact.
And finally, the rules you set yourself
Some things shouldn't be left to judgment. You give Alice the exact words, names, or terms that must always be blocked, and they're enforced as a fixed rule alongside the classifier, not something the model might learn and might miss.
Custom guardrails built for any scenario
8
10B+
<120ms
24/7
Trusted by security and product teams in the world's most regulated industries
Alice brings years of adversarial intelligence expertise to AI security. We give enterprise teams the coverage that generic guardrails and one-time audits can't match.
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Alice Data Advantage
Alice is the world’s largest collector and manager of adversarial intelligence data. Our data is the cornerstone for protecting platform, tech, and users online.
Explore Rabbit Hole intelligenceWhat’s new from Alice
From policy document to inline classifier in 24 hours
Your content classifier works during training time. Then, it fails three weeks into production. A content classifier's metrics are true on the day they're printed. They describe performance on a held-out split drawn from the same distribution as the training data, and real traffic does not live in that distribution. It lives at the edges.
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