Fact-check AI output before your users see it.
Built for AI products that cannot afford to publish hallucinations. Eight frontier models across a 5-stage adversarial pipeline with immutable primary source citations.
Four Primitives. Choose Your Latency & Depth.
From synchronous 5-second pre-send filters to 90-second deep adversarial audit investigations.
Atomic Claim Extraction
Pulls testable factual claims out of unstructured human or AI text. Resolves pronouns and strips rhetorical filler.
3-Model Frontier Panel
Fast multi-model verdict for synchronous user experiences and runtime pre-send checks before delivering drafts.
8-Model Adversarial Pipeline
Full 5-stage pipeline: live research, 2-round Pro vs. Con debate, 3-expert panel review, and 20+ primary citations.
Verification Follow-Ups
Conversational exploration strictly grounded in the verified evidence corpus. Ask about edge cases and jurisdictions.
The Multi-Model Adversarial Pipeline
Click each stage below to explore how raw claims turn into audit-grade verdicts.
Adversarial Debate
Two AI advocates argue opposing sides across two rounds. One builds the affirmative case, the other constructs the counterargument, followed by cross-rebuttals.
Why One LLM Is Never Enough
Standard benchmarks test multiple-choice trivia. When we tested 5 frontier models on 1,000 real-world claims, they failed to reach consensus on 63% of claims.
Each model self-reported 9/10 or 10/10 confidence over 75% of the time, even when completely disagreeing with peer models.
Frontier LLM Agreement & Confidence Variance
Models evaluated on 1,000 user-submitted claims. 63% resulted in split verdicts.
Integrate in Minutes with Typed SDKs
Available on PyPI (pip install acuityio) and npm (npm install acuityio). Built-in idempotency keys, automatic retries, and HMAC webhook verification.
from acuityio import Acuityio
client = Acuityio(api_key="acuity_live_...")
# 1. Extract atomic claims from unstructured text
claims = client.extract("Einstein won the 1921 Nobel Prize for general relativity.")
# 2. Run full 8-model adversarial verification
result = client.verify_and_wait(claims[0])
print(result.verdict) # "False"
print(result.acuity_score) # 1 (out of 10)
print(result.summary) # "Won for the photoelectric effect, not relativity."Stop AI hallucinations before they reach production.
Start verifying claims with high-throughput multi-model panels and comprehensive citation trails today.