Resonance launches GEO Search: the AI visibility engine for B2B technology brands
LONDON, Nov. 10, 2025 — Resonance today announced the launch of GEO Search, a free tool that allows B2B brands to see exactly what generative AI models say about them during the buying process and what drives visibility.
- 110,000 LLM queries. 463,000 web searches, 113 B2B tech sectors. 946,000 brand mentions.
- As 89% of buyers now use generative AI during their vendor research, the new battle isn’t SEO — it’s being selected by AI.
Built on the largest AI search research project carried out to date in B2B technology marketing, GEO Search is powered by over 100,000 buyer-intent prompts run through the latest generation of large language models. The research generated:
- 946,000 brand mentions from 34,000 companies
- 1 million URL citations categorized
- 113 B2B tech sectors benchmarked
The findings reveal that AI search is creating a new battleground for visibility and that public relations and earned media are now the primary drivers of whether a brand appears in LLM-generated buying recommendations.
Recent research indicates that 89% of B2B buyers now use large language models (LLMs) like ChatGPT, Claude, and Gemini at some point during their vendor research. Unlike traditional search, AI does not return a list of links — the shortlisting now happens inside the model. It generates a single answer, and that answer influences vendor selection.
“If you are not visible in AI answers, you are invisible to the buyer,” said Claire Williamson, Founder and CEO of Resonance.
Key findings from the GEO study
Across 100,000 prompts, there were four constants:
- Earned media drives AI visibility: Publications are the number one source LLMs cite when shortlisting vendors.
- Analyst reports and peer-review sites dominate during evaluation (for example G2, Gartner, IDC, TrustRadius).
- Visibility varies by sector: GEO visibility differs across categories — cybersecurity buyers consult different sources to DevOps or cloud infrastructure buyers. There is no single algorithm; category context matters.
“The myth is that AI search is random. It isn’t,” said Tom Fry, CTO and co-founder. “LLMs follow patterns, and those patterns can be influenced. Once you know which sources AI trusts in your category, you can shape visibility. The research proves that in AI search, citations and brand mentions are the new clicks.”
A new category of search
GEO Search introduces a new category: the GEO search engine for brands. Instead of showing web results, GEO Search:
- Reveals the questions buyers are asking inside LLMs (for example: ‘Best DevOps platforms for scale’)
- Shows the answers LLMs generate in real time
- Exposes which competitors appear beside the brand
- Reveals how the LLM is framing the brand’s positioning
Users can enter their brand or category and instantly see how AI responds. “For the first time, brands can see the AI ‘shortlist’ they’re being compared on. This isn’t SEO anymore. This is LLM-based vendor selection.”
Resonance is making GEO Search available free to the public so that every brand can understand how they show up inside AI-driven research.
Try GEO Search: https://geosearch.co/
The AI visibility problem
Most marketing teams assume that showing up on Google means showing up in generative AI answers. That assumption is no longer reliable. GEO Search testing shows three emerging shifts:
- AI introduces new competitors for incumbent brands.
- Some brands are framed incorrectly by LLMs.
- Many brands are missing entirely from AI shortlists.
Resonance helps brands influence their AI positioning — from buyer narrative ownership to the content and messaging signals that LLMs trust. Resonance runs AI visibility programmes for B2B technology brands.
Methodology (for analysts / press)
- 113 B2B tech segments analysed (from cybersecurity to orchestration platforms)
- 500 buyer-intent queries per segment (covering awareness → evaluation → purchase decision)
- Queries run in buyer context using persona + intent overlays
- LLM outputs analysed for:
- brand mentions in answers
- URLs cited
- source types (media, analysts, peer reviews, communities, vendor)
Segments follow multiple intent types including informational, benchmarking, comparison, purchase decision, technical evaluation, risk/compliance, and community validation.
Full dataset & visual explorer: https://geosearch.co/
Media contact
[email protected]
Account Director
Photo: https://mma.prnewswire.com/media/2818652/Resonance_Infographic.jpg
Logo: https://mma.prnewswire.com/media/2818651/Resonance_Logo.jpg
Source: Resonance