✨ The Safety Layer for Enterprise RAG

Stop AI Hallucinations
Before They Happen

Don't let your chatbot guess. AnswerGate sits between your vector DB and LLM, blocking irrelevant context in under 500ms.

Critical for Sensitive Industries

When "I don't know" is better than a wrong answer. Standard RAG pipelines are not enough for high-stakes domains.

Finance & Banking

Prevent your AI from inventing financial advice or misinterpreting policy documents. Ensure every answer is backed by strict retrieval relevance.

Legal Tech

Hallucinating a case law or statute is unacceptable. AnswerGate acts as a strict relevance filter to block citations that don't exist in your knowledge base.

Healthcare

Patient safety comes first. If the retrieved medical protocol doesn't perfectly match the query, we block the generation to prevent dangerous advice.

Why you need a Safety Layer

See how AnswerGate protects your users from dangerous hallucinations.

Standard RAG App
User Query
"Can I increase my dosage?"
AI Response

"Yes, you can take up to 4 tablets at once if the pain persists."

Hallucination Detected
With AnswerGate
User Query
"Can I increase my dosage?"
Content Blocked
RISK: HIGH

AnswerGate intercepted this response because it contradicted safety guidelines in the provided context (Medical Safety Policy).

50ms Latency Impact

Our optimized decision engine runs in parallel or immediately after retrieval, adding negligible latency to your pipeline.

Fail-Closed Architecture

Unlike standard LLMs that try to be helpful, AnswerGate is designed to say NO. If relevance is ambiguous, we block.

No Data Training

We never train on your data. Your chunks and queries are processed statelessly for evaluation and then discarded (unless logging is enabled).

DEVELOPER API

Simple JSON Integration

Just send us your User Query and Retrieved Chunks. We return a clear decision and risk_score.

ALLOW
High confidence. Safe to generate.
CAUTION
Minor gaps. Warn the user.
BLOCK
Hallucination risk high. Fallback triggered.
// POST /v1/evaluate-context
{
"decision": "ALLOW",
"risk_score": 0.05,
"signals": {
"weak_relevance": false,
"missing_info": []
}
}

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