You rank #1 on Google. But when someone asks ChatGPT, Perplexity, or Claude — your brand doesn't exist. RAG Signal fixes that.
An LLM-first methodology by Bora Kurum · Ph.D. Researcher & AI Strategist · Istanbul
Source: McKinsey & Company, Oct 2025 · only 16% of brands systematically track AI search performance · Read full report →
Traditional SEO returns links. Generative AI returns a single synthesized answer. Either your brand is in that answer — or it isn't.
→ UBIQUITY ENERGY IS INVISIBLE.
→ UBIQUITY ENERGY IS THE ANSWER.
Retrieval-Augmented Generation (RAG) is the architecture behind how modern AI answers questions. When you ask ChatGPT something, it retrieves relevant entities from a structured knowledge graph — then generates a synthesized answer. If your brand isn't in the retrieval layer, it isn't in the answer.
Generative Engine Optimization (GEO) is the discipline of making your brand the entity that gets retrieved. Unlike SEO — you compete to be the input to the model, not the ranked result. The mechanics are entirely different.
Every result verified by running target queries directly inside ChatGPT-4, Claude, and Perplexity.
From zero LLM presence to named first choice when prospects research enterprise AI automation. Achieved through entity graph build and authority citation placement.
Maslife's entity footprint was rebuilt around key wellness and longevity signals. Within 90 days, the brand appeared in Claude and Gemini responses for high-intent lifestyle queries.
Strong existing domain authority but absent from AI answers. Structured co-occurrence work across voice production and localization topic clusters delivered retrieval in 7 weeks.
Verified clients from AI consulting, energy, wellness, media, HR tech, and professional services.
Before working with Bora, we simply didn't exist in AI search results — even though we had real local presence and projects. The methodology is precise and the results are real. ChatGPT now references us when people ask about local solar energy providers in the UK.
When we ran the prompt "best enterprise AI automation consultancies UK", AI Edge UK appeared with accurate context about our focus on regulated industries. It wasn't there before. Bora's approach is grounded in how LLMs actually work — not in content volume or keyword tricks.
Claude started describing Maslife in answers about holistic wellness platforms without us doing any traditional marketing push. That's exactly what we needed — brand presence in the places where our future customers are discovering products.
The entity graph framework made complete sense immediately. Enkronos needed to be understood — not just found. Bora's audit identified the exact structural gaps that were making us invisible to AI systems, and the roadmap was specific and actionable.
I'd been focused entirely on SEO and social. Bora showed me that AI search is a completely different game — and that Filmfolk had zero presence there. The audit was genuinely eye-opening and the implementation was clean and professional.
From an operations perspective, what impressed me most was the measurement framework. We knew exactly which prompts we were targeting, we could test them ourselves at any point, and the results were verifiable. No black box.
We had strong product-market fit but weak entity signal. Faselis needed to be retrievable by AI when HR decision-makers ask about talent pipeline solutions. Bora understood the B2B buyer journey in AI search better than anyone we'd spoken to.
We had good SEO already. But nobody was finding us on Perplexity or ChatGPT. Within seven weeks of working with Bora, that changed. Voice Crafters now appears consistently in AI answers about professional voice production and audio localization.
Before an LLM generates an answer, it runs a retrieval pass. We engineer every layer of that pass.
Your entity must co-occur with these signals across high-trust digital nodes:
We map which signals are missing from your entity graph, then place your brand on authoritative sources where those signals live. The LLM learns to associate you with the query before anyone asks it.
Enter your website and category. We'll flag the most common structural gaps that prevent brands from appearing in LLM answers.
A proprietary 8-dimension scoring framework that quantifies your brand's current position in LLM retrieval — and maps exactly what needs to change.
Each dimension is scored by running live prompts inside ChatGPT, Claude, Perplexity, and Gemini — then cross-referencing entity graph signals, schema coverage, and citation density. The score reflects actual retrieval probability, not surface-level SEO metrics.
Most brands score below 30 on first audit — meaning they are effectively invisible in AI-generated answers. A score above 70 means your brand appears consistently when buyers ask the right questions. We engineer that gap.
Every engagement starts with an audit. The depth depends on your goals and timeline.
One-time diagnostic.
Full implementation. Measurable results.
Continuous optimization as LLMs evolve.
Sprint: €299 on kickoff · €699 on verified results. Retainer from €349/mo.
I'm Bora Kurum. As a marketing manager and active practitioner, I was already running AI visibility experiments on client brands when I started my Ph.D. research at Istanbul Bilgi University — where my focus is the intersection of communication science, LLM retrieval behavior, and brand representation in generative AI systems.
What I do at RAG Signal is not theoretical. Every methodology comes from running real prompts against real LLMs, measuring what works, and iterating. The academic research informs the strategy; the client work tests it in production. My clients span the UK, EU, USA, and Turkey — across AI consulting, wellness, media, HR tech, and professional services.
If your brand needs to exist in the answers AI gives your future customers — let's talk about what that actually takes.