Sentinal AI
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REAL-TIME HALLUCINATION DETECTION

Your AI sounds confident.
That isn't the same as correct.

Sentinal AI checks every prompt and response pair, catches claims your model can't back up, and rewrites them before your users ever see the mistake.

Try the analyzer See how it works
Drop-in API, no fine-tuning Model-agnostic Self-hostable
verify_exchange.jsonMonitoring
PROMPT

RESPONSE

TRUST SCORE
—
< 400msAdded latency
6Verification passes
0–100Unified trust score
1API call
— THE PROBLEM

Hallucinations don't look like errors.
They look like answers.

“

A customer support bot invents a refund policy that doesn't exist — and a customer acts on it.

“

A research assistant cites a study that was never published, with a confident page number attached.

“

A legal copilot merges two real case names into one that sounds plausible — and is entirely fictional.

— DETECTORS

Six ways a model can mislead you. One layer that catches all of them.

Every detector runs in parallel against the full exchange. Nothing here requires touching your model or your training data.

Unsupported Claim

critical

A factual statement presented with confidence but with no basis in the provided context or verifiable source.

Fabricated Citation

critical

References a paper, case, statistic, or quote that does not exist, or misattributes a real one.

Numeric Drift

high

Numbers, dates, or quantities that are internally inconsistent or contradict the source material.

Entity Confusion

high

Conflates two similar people, places, products, or organizations into one incorrect answer.

Context Contradiction

high

The response directly contradicts a fact stated earlier in the prompt or conversation.

Overconfidence Marker

medium

Hedge-free, definitive language wrapped around a claim the model has no way to verify.

— HOW IT WORKS

From answer to evidence in five steps.

01

Exchange arrives

The prompt and the model’s draft response are sent to /api/verify before the user ever sees them.

02

Claims are extracted

Sentinal AI parses the response into discrete factual claims — names, numbers, dates, citations, causal statements.

03

Each claim is checked

Every claim is cross-referenced against the supplied context, retrieved sources, and internal consistency rules.

04

Risk is scored

Flagged claims are weighted by severity into a single 0–100 hallucination score for the exchange.

05

A correction is drafted

For anything above threshold, Sentinal AI proposes a corrected span — not just a flag, a fix — ready to swap in.

— SCORING

One score. Three plain outcomes.

No black-box probability to interpret. Every exchange lands in a band that tells your application exactly what to do next.

Trusted0 – 24

No unverified claims found. Ship the response as-is.

Needs review25 – 59

Minor or unverifiable claims. Flag for a human, or auto-correct low-risk spans.

Hallucinated60 – 100

High-confidence fabrication detected. Block, or serve the corrected version.

— INTEGRATION

Two lines of code. Any model.

Sentinal AI sits beside your existing stack — OpenAI, Anthropic, an open-weight model you host yourself. Send the exchange, get back a verdict.

  • Async or blocking — your call
  • No customer data leaves your infrastructure in self-hosted mode
  • Open SDKs for Python, Node, and Go
verify.ts
import { SentinalAI } from "sentinal-ai-sdk";

const sentinalAI = new SentinalAI({ apiKey: process.env.SENTINELAI_API_KEY });

const result = await sentinalAI.verify({
  prompt: userMessage,
  response: llmResponse,
});

if (result.status === "hallucinated") {
  return result.corrected; // serve the fix, not the flaw
}

Stop shipping confident mistakes.

Paste a real exchange and watch Sentinal AI find what's wrong with it — right now, in your browser.

Open the analyzer Read the docs
Or get early access —