Diagnose · The Veltis AI commercial stack

Veltis Intelligence

An AI-readiness audit that quantifies how AI systems select, trust, and reuse your pharmaceutical content.

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What it is

AI-readiness digital audit

A comprehensive diagnostic that reveals how leading AI systems perceive, evaluate, and cite your pharmaceutical digital content compared to competitors.

We analyse and score content along multiple dimensions: content factors (clarity, specificity, terminology patterns), authority signals (citations, sourcing, credential indicators), technical factors (metadata, schema markup), and experience factors (readability, hierarchy, information architecture).

Analysis 10-dimension diagnostic
Benchmark Citation probability scoring
Guidance Actionable insights
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10 Evaluation Dimensions
All Major AI Models

The blind spot

Current analytics miss what matters most

Current analytics

Track engagement after exposure

vs
AI systems

Decide before exposure

  • Whether content is retrieved
  • Whether it is trusted
  • How it is summarised
  • Whether it is cited or ignored

These evaluations occur upstream of existing metrics. This creates a critical blind spot in performance insight.

Our core metric

VeltisScore: AI perception quantified

VeltisScore is our proprietary citation probability metric that quantifies how likely AI systems are to select, cite, and recommend your content versus competitors.

What it measures

Citation probability is the likelihood that AI systems will select your content when answering health-related questions. A higher score means your content is more likely to be surfaced, summarised, and cited.

How it works

We analyse your content across 10 evaluation dimensions using all leading AI models. Consistent patterns across multiple models reveal genuine content quality gaps, not model-specific quirks.

Why it matters

Traditional analytics tell you what happens after someone arrives. VeltisScore tells you whether AI systems are helping people find your content in the first place.

Scoring scale

0-1 Critical
2-3 Weak
4-5 Moderate
6-7 Good
8-10 Excellent

The framework

Four categories of AI selection

AI systems do not evaluate pharmaceutical content holistically. They assess specific, repeatable signals that determine whether content is selected, summarised, or cited. We measure these signals across four categories.

1

Content structure

Is the content easy for AI to parse, segment, and reuse?

2

Authority & trust

Does it demonstrate credibility through sourcing, authorship, and consistency?

3

Technical accessibility

Is the content readable and interpretable via clean structure and metadata?

4

Maintenance & experience

Does it stay fresh, optimised, and aligned to the user intents AI is satisfying?

The 10 dimensions

What gets measured

Inside the four categories, we score 10 specific dimensions that AI systems use to decide whether your content gets cited.

Content structure

Semantic structure quality

Logical organisation, headings, and information hierarchy.

Content structure

Information completeness

Coverage of key concepts required to answer the user intent.

Content structure

User intent alignment

Directness and relevance of the answers provided.

Authority & trust

Content authority signals

Citations, references, and institutional credibility indicators.

Authority & trust

Human authorship indicators

Evidence of expert authorship or authentic voice.

Authority & trust

Factual accuracy signals

Internal consistency and verifiable claims.

Technical accessibility

Technical content accessibility

Readability, formatting, and machine interpretability.

Technical accessibility

Keyword density distribution

Natural and contextual terminology usage.

Maintenance & experience

Content freshness

Update signals and recency indicators.

Maintenance & experience

Image & media optimisation

Use of visuals and descriptive metadata supporting comprehension.

Multi-model analysis

We diagnose AI perception across all leading AI models

Different AI systems evaluate pharmaceutical content differently. What works in one may not work in another. We measure citation probability, identify anything that would reduce AI's capability of recognising and referencing your content, and provide actionable insights.

GPT-5

GPT-5

OpenAI

Frontier AI, enterprise-grade reasoning, agents, multimodal systems

Claude Opus

Claude Opus

Anthropic

Enterprise AI, safety-aligned LLMs, regulated industries

Gemini

Gemini

Google

Multimodal AI, search integration, Google ecosystem

Grok

Grok

xAI

Real-time information, social media integration, conversational AI

Llama 4 Maverick

Llama 4 Maverick

Meta

Social platforms, consumer AI, open-weight LLMs for research

Mistral

Mistral

Mistral AI

Open-source LLMs, European enterprise AI, privacy-first

DeepSeek

DeepSeek

DeepSeek

Open-source reasoning, cost-efficient inference, research AI

Gemma

Gemma

Google

Open LLMs for research, edge AI, developer experimentation

Qwen

Qwen

Alibaba Cloud

Enterprise AI, cloud services, Asian market commercial AI

GPT Oss

GPT Oss

OpenAI

General-purpose AI, enterprise automation, developer platforms

Consistent disadvantages across multiple AI models indicate genuine content gaps, not model-specific biases or random variation. Our cross-model validation ensures your optimisation strategy addresses real issues.

17%+

Citation probability disadvantage identified

We identified a 17%+ AI citation probability disadvantage for a leading treatment brand: a performance gap completely invisible in Google Analytics. Traditional metrics showed strong engagement, but AI systems consistently favoured competitor content.

This is what measurement reveals: genuine gaps that compound silently across your digital strategy.

Deliverables

What you receive

Insights designed for action, not just analysis.

Business

For leadership

  • Executive summary with competitive positioning
  • Performance dashboard by asset
  • Stakeholder-ready presentation materials
  • Strategic recommendations
Marketing

For marketing teams

  • Detailed diagnostic reports by asset
  • Specific optimisation recommendations
  • Prioritisation by impact and effort
  • Implementation guidance for agencies

Common questions

What teams ask us about Intelligence

How is this different from SEO?

SEO optimises for search engine rankings. We measure how AI systems perceive and cite content, a different evaluation process with different criteria.

Do you understand pharmaceutical compliance?

Yes. Our team has decades of pharmaceutical industry experience. All recommendations are designed to work within MLR constraints.

How long does an audit take?

Typical engagements deliver initial insights within 4–5 weeks, including analysis, benchmarking, and recommendation development.

Can we start with just one brand?

Absolutely. We recommend starting with a pilot to prove value before expanding. Most clients begin with a single brand audit.

Platform

Inside Veltis Intelligence

From site-wide mapping to category scores and page-level recommendations, everything your team needs in one place.

Site Overview: content mapping, coverage index, and compliance summary across the brand

Brand Categories: Veltis Score & Coverage Index per category

Category Detail: page-level scores, compliance audit, and coverage gaps

Ready to understand your AI visibility?

Let's discuss your specific situation and whether an AI-readiness audit makes sense for your pharmaceutical team.

Schedule a connect