Measurement System

Semantic Metrics

Measure what AI systems actually evaluate—not vanity metrics. Six metrics that predict AI retrievability, not just human engagement.

The Problem

Traditional content metrics—word count, keyword density, readability scores—were designed for human readers and search engine crawlers. They don't measure what AI systems evaluate when deciding what to cite.

AI retrieval depends on semantic structure: entity relationships, topical coherence, knowledge density. These patterns are invisible to traditional analytics.

You need metrics that predict AI behavior, not just track human clicks.

The Solution

DecodeIQ's semantic metrics measure what AI systems actually evaluate. Each metric is derived from analysis of how LLMs process and retrieve content—not guesswork.

You get actionable targets for each metric. Your briefs and drafts are scored automatically. You know exactly what to improve to increase retrievability.

Quick Reference

All metrics at a glance

MetricRangeTargetStatus
Semantic Density0–10%4–6%Production-Ready
Contextual Coherence0–10080+Production-Ready
Retrieval Confidence0–10060+Production-Ready
DecodeScore0-100Topic-dependentIn Validation
Friction Index0-100Higher = more opportunityIn Validation
Brand Voice Consistency0-100%80%+In Validation

How Metrics Work Together

These metrics aren't isolated scores—they form a system. High Semantic Density without Contextual Coherence produces dense but disjointed content. Perfect Coherence with low Density produces on-topic but shallow content.

DecodeIQ optimizes across all metrics simultaneously. Your briefs and drafts are balanced for maximum AI retrievability, not just hitting individual targets.

Explore the Knowledge Base

Deep documentation on each metric, including algorithms, interpretation, and optimization strategies.

View all Knowledge Base articles

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