Everything ASURIQ checks.

A single number that factors in topic, complexity, domain risk, and language patterns. Not a guess — a structured assessment across seven dimensions.

Factual accuracy, completeness, recency, balance, domain expertise, source quality, and internal coherence — each scored independently.

Finds the single weakest axis dragging the score down. Your answer might be 80% solid but 30% on recency — that changes what you do with it.

Counts and highlights qualifying language: “potentially,” “in some cases,” “generally.” An answer with 7 hedges in 4 sentences isn't as confident as it sounds.

Detects assertive, reassuring, academic, cautionary, and conversational tones. Flags when the tone doesn't match the topic — like reassuring language on a medical question.

Catches when AI is highly confident about claims it frames as surprising or counterintuitive. That combination means: verify this independently.

Identifies studies, statistics, organizations, dates, and named claims. Each one flagged as verifiable — because “a 2023 study found...” means nothing until you confirm the study exists.

Counts how many claims are sourced versus unsourced. Finds the specific claims that need sources but don't have them — the gaps where hallucination hides.

Identifies the single most important sentence in the answer by salience scoring. The claim that matters most — highlighted so you don't miss it.

Scores how likely you'd notice if the answer was wrong. High-risk domains with assertive language and no qualifications = errors you'd never catch on your own.

Finds when the AI says one thing in sentence 3 and the opposite in sentence 7. Internal contradictions are a signal the AI is pattern-matching, not reasoning.

Measures whether the answer holds together as a whole. An answer can be factually accurate sentence by sentence and still tell an incoherent story.

When AI generates code, ASURIQ checks for security vulnerabilities (eval, innerHTML, SQL injection), deprecated APIs (Buffer(), substr()), error handling gaps, and anti-patterns. The same engine that verifies medical claims verifies your code.

Specific, actionable advice generated from the weakest axis. Not generic “ask better questions” — actual rewrites targeting the specific gap in the answer you got.

A full rewritten question you can paste directly. If your question was binary (“should I refinance?”), the rewrite restructures it into scenario analysis.

Generates 2\u20133 adversarial questions your AI\u2019s answer doesn\u2019t address. For financial advice: \u2018What about tax implications?\u2019 For medical: \u2018What are the interactions?\u2019 The questions the AI should have covered but didn\u2019t.

When the answer touches specialized territory, hints include domain-specific guidance: “Ask what a cardiologist would say” not just “consult an expert.”

The engine has access to Claude, GPT-4o, Gemini, DeepSeek, and dozens of specialist models. It doesn't use the same three every time — it builds the right panel for your specific question.

Medical specialists for health questions. Code experts for programming. Legal models for contracts. The Oracle router analyzes complexity, domain, and risk level, then selects the optimal strategy.

Parallel consensus for fast answers. Specialist-first cascade for domain expertise. Adversarial tribunal (defend, attack, analyze) for high-stakes decisions. The engine picks automatically.

When one model says something unusual that the others miss, Deliberation doesn't dismiss it. Minority opinions are preserved and flagged — because sometimes the outlier is right.

The complete forensic record of how an AI output was evaluated. Every model's assessment, every disagreement, every axis scored. Timestamped, immutable, exportable as PDF.