AI Visibility for High-Trust B2B
AI can’t recommend
what it can’t understand.
AEO Studio helps high-trust B2B companies improve how they are understood, verified, and recommended in AI-generated answers.
If AI systems cannot clearly read what you do and what backs your claims, you will not appear when buyers ask who to consider.
Why This Matters
Buyers use AI before they contact you.
Procurement teams, technical buyers, and decision-makers increasingly use AI systems to build shortlists. They ask ChatGPT, Claude, or Perplexity which vendors to consider. They get a list. They contact the companies on it.
If your company is not in those answers, you are not in that conversation. You do not know it happened. The buyer has already moved on.
Weak AI visibility is rarely a content volume problem. It is usually a trust and interpretation problem. AI systems default to companies they can verify clearly, even when those companies are not the strongest option.
What We Fix
Four structural gaps that keep companies out of AI answers.
Category signal
Your product or service cannot be placed in the right category by AI systems. It gets compared to the wrong competitors or left out entirely.
Entity clarity
Your company, what it does, and who it serves are described inconsistently across your site and external sources. AI cannot resolve a clear picture.
Evidence structure
Claims exist but are not connected to verifiable proof. AI systems skip or downgrade companies whose claims they cannot substantiate.
Trust signals
The sources AI uses to cross-reference and validate your company are weak, missing, or contradictory. Verification fails silently.
What We Work On
The service: AI Discoverability Infrastructure.
We rebuild the structural layer that determines how AI systems read, interpret, and verify your company. Four components. Fixed scope. Clear output.
Answer Architecture
Key pages are restructured around the questions AI systems retrieve. Not around how you prefer to describe yourself.
Entity Alignment
Your company, products, and expertise are connected to the right categories and use cases so AI can resolve them with confidence.
Evidence Alignment
Claims are matched to visible proof. Technical, commercial, and validation evidence is structured so AI can extract and use it.
Implementation
Changes are made to the pages that matter most. We test before and after using the same prompt set and show you what changed.
Who It Is For
High-trust B2B companies where being misread has a cost.
The companies that benefit most operate in categories where products are complex, buyers are experts, and being misclassified by AI leads to being excluded from shortlists or compared to the wrong alternatives.
- Medical devices and MedTech
- Cybersecurity
- Fintech
- Legal tech
- Digital health
- Industrial automation
- Research-grade technology
Not a fit if
- You need a content retainer or editorial calendar.
- You want generic SEO or link building.
- Your team is not ready to publish structural changes to your site.
- You are looking for a quick fix or surface-level optimisation.
How It Works
Three steps. No retainer.
01
AI Visibility Audit
We run your category and vendor prompts across the main AI systems. You see where your company appears, where it does not, and what is likely causing the gap.
02
Structural Diagnosis
We identify which signals are weak or missing and map exactly what needs to change to improve how your company is interpreted.
03
Implementation
We fix the pages, evidence layers, and entity signals that matter. We retest using the same prompts and show you the before and after.
Case Study
PLUX Biosignals.
PLUX Biosignals
Biosignal hardware, 1,000+ institutions
Share of Answer
Same product. Same prompts. Three months.
Problem
PLUX had strong technical documentation and good domain authority. When we ran their buyer prompts across ChatGPT, Claude, and Perplexity, they appeared in 30% of relevant answers. Competitors with narrower product lines showed up instead.
What changed
We restructured how the product category was described, reorganised clinical evidence for AI parseability, and reinforced the entity signals models use to verify the company. No new content was created.
Result
Share of Answer moved from 30% to 70% across the same prompt set. The quality of how the product was described also improved across all three AI systems.
Why it matters
In a category where procurement increasingly uses AI-assisted shortlisting, appearing in 70% of relevant answers instead of 30% is a difference in leads, not a vanity metric.
FAQ
Common questions.
What does AI say about your company right now?
Most companies do not know. If AI systems cannot clearly understand what you do, what you sell, and what backs your claims, they will not include you when buyers ask who to consider.
Start with a 10-minute call. No pitch. We will tell you whether the problem is real and whether we can help.