Google Ranks Pages.
AI Cites Meaning.
AI doesn't rank your content. It cites it, or treats it as a footnote. Get your score, verdict, and fix list before you publish, not after traffic drops.
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[09:14:01] URL received: fetching...
[09:14:03] Content: 1,842 words extracted
[09:14:05] Entities found: 23
[09:14:08] Relationships mapped: 14
[09:14:11] Sem. Density: 4.8% ✓ optimal
[09:14:13] Coherence score: 82/100
[09:14:16] Retrieval Conf.: HIGH
[09:14:19] Fix list: 3 actions found
Your Content Is Either the Source or the Footnote.
AI systems are choosing which content to cite right now, on every query your buyers are running. The only question is whether yours is one of them.
58.5%
Zero-Click Searches
searches end without a click
102%
AI Overview Growth
year-over-year, still accelerating
80%
Using AI for Answers
before visiting any website
Other Tools Give You Scores.
You Need Architecture.
Scores tell you something is wrong. DecodeIQ tells you exactly what to fix and where.
Three Steps to Actionable Semantic Fixes
Submit Your Content
Enter any public URL or paste draft text directly. Works on published pages and pre-publish drafts alike.
Deep Semantic Analysis
60 secondsWe map every entity, relationship, and structural gap AI systems evaluate when deciding whether to cite your content.
Prioritized Fix List
Specific rewrites ranked by retrieval impact. Not just what's wrong — what to write and exactly where to put it.
Different Category.
Not Just Different Features.
| Capability | Keyword Tools (Clearscope, Surfer) | AI Writing Tools (Jasper, Copy.ai) | DecodeIQ DecodeIQ |
|---|---|---|---|
| Entity extraction & analysis | |||
| Specific fix recommendations | |||
| AI retrieval prediction | |||
| Relationship mapping | |||
| Keyword optimization | |||
| Content generation |
DecodeIQ isn't a better SEO tool. It's a different layer entirely.
SEO tools optimize for Google's ranking algorithm. DecodeIQ engineers the semantic architecture that AI systems evaluate when choosing what to cite.
What's Inside Every Analysis
Scroll to explore the journey
Entity Gap Analysis
See exactly which concepts are defined, which are mentioned but undefined, and which critical entities are completely missing from your content.
Entity Gap Analysis
See exactly which concepts are defined, which are mentioned but undefined, and which critical entities are completely missing.
Add definition for ‘usage-based pricing’
“Usage-based pricing charges customers based on their actual consumption of the product...”
Prioritized Fix List
Not just problems, solutions. Each fix includes priority, effort, and example text you can adapt directly into your documentation or site.
Retrieval Prediction
Know which queries your content will be retrieved for — and which it will miss. Our predictive engine maps your relevance in real-time.
Retrieval Prediction
Know which queries your content will be retrieved for — and which it will miss.
Relationship Mapping
Visualize how concepts connect. Weak or missing relationships are why content gets skipped by AI models that prioritize semantic depth and structural clarity.
Two Ways to Use It
Engineer Before Publishing
“Will AI systems actually retrieve this draft?”
- Check drafts before they go live
- Catch missing definitions early
- Build retrieval-ready content
Diagnose What's Already Live
“Why isn't my content showing up in AI?”
- Analyze published pages that don't rank
- Find semantic gaps competitors filled
- Get specific fixes without rewriting
Built for Tech,
Not Everything.
Our semantic models are trained specifically on technology content patterns. Precision over breadth.
Sample Report Preview
What you'll get when you analyze a page.
This is what ‘architecting retrievability’ looks like in practice.
Define ‘usage-based pricing’
The content assumes the user understands the core mechanic without explicit semantic anchoring. Defining this entity early establishes a necessary conceptual foundation for retrieval algorithms.
Suggested Rewording
“Usage-based pricing is a billing model where customers only pay for the specific resources or volume they consume. This contrast with traditional flat-fee subscriptions by aligning cost directly with value realized.”
+ 6 more fixes in your full report
Semantic Architecture
Below Target
Key entities are sparse. Increase related technical terminology.
Needs Work
Logical flow breaks at section 3. Entity relations are weak.
Good
Search engines can parse the primary intent effectively.
Entity Map
Get your full report with all fixes and example rewrites
Get Your Full ReportProprietary Metrics
Three Metrics That Predict
AI Retrievability.
Every report includes these research-validated measurements.
Entity concentration per 1,000 words. The core signal AI retrieval systems use to evaluate content depth.
More ↓Less ↑
Measures relationship depth and concept specificity. Low density means content lacks sufficient entity structure for AI retrieval — retrievable content typically falls in the 4–6% range, where entities are dense enough to signal expertise without overwhelming semantic coherence.
Logical flow consistency score. Evaluates how well concepts chain together across your content.
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Low coherence means scattered topical focus that retrieval systems struggle to categorize — content scoring below 80 typically contains section-level topic drift that breaks the semantic chain AI needs to synthesize a coherent answer.
Likelihood of being surfaced in AI-driven search results, based on your content's semantic proximity to cited sources.
More ↓Less ↑
Based on semantic proximity to high-performing technology content corpus (n=1,200+ articles). Scores above 60 indicate content is structurally competitive with sources currently being cited by ChatGPT, Perplexity, and Google AI Overviews for similar queries.
Validated Methodology
Calibrated against 1,200+ technology articles with documented retrieval outcomes.
Scoring engine validated across 4.2M successful RAG queries.
Start Engineering.
Scale When Ready.
Every plan includes the full semantic analysis engine.
Choose based on volume, not feature gates.
- Semantic Density score
- Contextual Coherence score
- Retrieval Confidence score
- Entity map
- 10 Pages per month
- Top 3 fixes only
- 48-hour report history
- Semantic Density score
- Contextual Coherence score
- Retrieval Confidence score
- Entity map
- 30 Pages per month
- All fixes
- Example rewrite text
- Paragraph-level placement
- Effort estimate per fix
- 30-day report history
- Email support
- Semantic Density score
- Contextual Coherence score
- Retrieval Confidence score
- Entity map
- 100 Pages per month
- All fixes
- Example rewrite text
- Paragraph-level placement
- Effort estimate per fix
- Unlimited report history
- Export PDF / CSV Soon
- API access Soon
- Email support
- Priority support
Frequently Asked Questions
Stop Optimizing Keywords.Start Architecting Meaning.
Measure your content's retrieval confidence in 60 seconds.
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