What your Neural Score is telling you
Your Neural Score is a number from 0 to 100. It measures how well AI models know, trust, and recommend your brand across five dimensions: Awareness, Recommendation, Competitive, Sentiment, and Discovery.
A score of 80+ means AI models consistently mention your brand accurately and recommend it for relevant queries. A score below 50 means you have significant gaps that are costing you visibility — and customers — right now.
This guide walks through each dimension and exactly what to do to improve it.
Dimension 1: Awareness (0-100)
What it measures: How often AI models mention your brand when asked about your category or market.
Why it matters: If AI doesn't know you exist, nothing else matters. Awareness is the foundation everything else is built on.
How to improve it:
The fastest way to boost Awareness is to create multiple high-authority sources that confirm your brand's existence and category.
Start with these:
- Wikipedia — Create or improve your Wikipedia article. Include your founding date, founders, headquarters, and primary product category. Wikipedia is one of the most heavily weighted sources in AI training data.
- Wikidata — Add a structured Wikidata entry for your brand. This gives AI models a machine-readable source of truth about your company.
- Crunchbase — Complete your Crunchbase profile with accurate, up-to-date information.
- Schema markup — Add Organization schema to your homepage with your full legal name, founding date, description, and logo.
Timeline: 2-4 weeks to see improvement after publishing.
Dimension 2: Recommendation (0-100)
What it measures: Whether AI models recommend your brand when users ask for suggestions in your category.
Why it matters: Recommendation is the money dimension. This is where AI search turns into revenue.
How to improve it:
Recommendation is driven by category association signals. AI models recommend brands they have seen consistently associated with positive outcomes in a specific category.
Key actions:
- Comparison content — Publish detailed comparison pages: "[Your brand] vs [Competitor 1] vs [Competitor 2]". These pages signal to AI that you are a legitimate option in your category.
- Roundup mentions — Get your brand included in "best of" and "top 10" articles on industry publications. These are heavily weighted by AI models.
- Use case content — Create content that explicitly answers: "best [your category] for [specific use case]". Match the exact phrasing that buyers use.
- Customer proof — Publish detailed case studies with specific outcomes. AI models use outcome language when making recommendations.
Timeline: 4-8 weeks for meaningful improvement.
Dimension 3: Competitive (0-100)
What it measures: Whether competitors are mentioned more prominently than you in AI responses — and how often AI recommends a competitor instead of you.
Why it matters: A low Competitive score means you're losing customers to competitors in AI conversations you never even knew were happening.
How to improve it:
The Competitive dimension is about relative positioning. You need to appear at least as prominently as your main competitors in AI responses.
Key actions:
- Head-to-head content — Create detailed comparison pages where your brand wins. Use objective criteria where you genuinely outperform competitors.
- Differentiation language — Develop 2-3 clear differentiators and repeat them consistently across all your content. AI models learn patterns through repetition.
- Competitive backlinks — Earn mentions from sources that also mention your competitors. This signals to AI that you belong in the same conversation.
- Category leadership claims — If you have awards, rankings, or data that supports a leadership position, publish and syndicate that information widely.
Timeline: 6-10 weeks for meaningful improvement.
Dimension 4: Sentiment (0-100)
What it measures: The tone and framing of AI responses about your brand — whether AI describes you positively, neutrally, or negatively.
Why it matters: Sentiment shapes how AI frames its recommendations. A brand with mixed sentiment gets hedged recommendations: "some users report issues with X" or "it depends on your use case".
How to improve it:
Sentiment is driven by the overall tone of content about your brand that AI was trained on.
Key actions:
- Address negative content — Identify the most prominent negative articles or reviews about your brand and create counter-content that addresses those concerns directly.
- Publish positive proof — Case studies, testimonials, and outcome data create positive sentiment signals. Make them specific and quantified.
- Earned media — Positive coverage in high-authority publications has outsized influence on AI sentiment. A glowing TechCrunch article matters more than 100 positive tweets.
- Correct hallucinations — If AI is repeating factual errors that create negative framing, correct those errors at the source (Wikipedia, news corrections, etc.)
Timeline: 8-12 weeks for meaningful improvement.
Dimension 5: Discovery (0-100)
What it measures: Whether AI surfaces your brand for broad, generic queries in your category — not just branded searches.
Why it matters: Discovery is where new customers come from. Users who don't know you exist can only find you through generic category queries.
How to improve it:
Discovery requires establishing strong category signals across a wide range of query types.
Key actions:
- FAQ content — Create comprehensive FAQ pages targeting every generic query in your category. Explicitly mention your brand name in the answers.
- Glossary and educational content — Publish authoritative educational content in your space. Brands that teach their category get associated with it.
- Directory listings — Get listed in every major directory, comparison site, and industry database in your category.
- Geographic signals — If you serve specific markets, create location-specific content that helps AI associate your brand with those markets.
Timeline: 4-8 weeks for meaningful improvement.
Prioritizing your actions
Not all improvements are equal. The fastest path to a higher Neural Score is to:
- Fix any factual errors first (they penalize your score directly)
- Address your lowest-scoring dimension next
- Work through the dimensions in order of impact
Vydit's Neural Fix Plan does this automatically — it analyzes your score, identifies the highest-impact actions, and prioritizes them by expected point improvement and effort required.
Improving your Neural Score is not a one-time project. AI models are retrained continuously. The brands that maintain strong AI visibility treat it as an ongoing channel — not a one-off campaign.