AI FAILS
SILENTLY.
WE DON'T.
(Security layer for LLMs.)
Detect hallucinations, jailbreaks, and unsafe responses before they hit production. Your prompt → detectors → aggregator → log pipeline, automated.
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// Live Risk Assessment Simulation
// Trusted by engineering teams at
THE SENTINEL PIPELINE
Every LLM interaction passes through four layers — automatically. Anomalies caught. Risks scored. Threats stopped.
Every prompt enters SentinelAI before hitting your LLM. We capture the raw input, embed it, and compare against your baseline distribution to detect distribution shifts instantly.
Prompt anomaly detection runs in parallel with output risk scoring across 7 risk categories — violence, hate speech, misinformation, privacy violations, and more. Rule-based heuristics with weighted severity.
Weak signals get fused into a single unified risk score with explainable flags. Confidence scoring tells you exactly how certain the system is. Threshold-based decisions — you set the rules.
Coming next: plagiarism detection via text fingerprinting, hallucination verification against knowledge graphs, and citation validation. The /api/analyze endpoint returns everything — score, flags, confidence, and evidence.
AI FAILS SILENTLY.
UNTIL NOW.
Silent Failures, Loud Damage
Hallucinations and prompt injections don't announce themselves. They ship to users. SentinelAI catches them before they ever do.
Explainable, Not a Black Box
Every flag comes with a reason. Every score is decomposable. Your team understands exactly why something was flagged — no guessing.
One API. Zero Overhead.
Drop in /api/analyze anywhere in your stack. Async processing. No latency spikes. Works with any LLM.
ENGINEERS LOVE IT
We shipped a hallucinated citation into a client report and only found out when they flagged it. SentinelAI catches these before they leave our pipeline. It's like having a security team watching every LLM call.
Prompt injection attacks were hitting our customer support bot weekly. After integrating SentinelAI, detection went from reactive to proactive. The explainable flags saved us hours of debugging.
Our compliance team needed visibility into every AI output. SentinelAI's unified risk score gave us exactly that — one number, full context. Integration took less than an hour.
We were burning engineering cycles manually reviewing LLM outputs. SentinelAI automated 80% of that. The threshold-based blocking means risky content never reaches our users.
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