AI visibility
engineering.
We don't do content marketing. We don't do SEO. We engineer your brand into the retrieval layer of AI systems — so when buyers ask ChatGPT, Claude, or Perplexity who to hire, your name comes up.
Traditional SEO doesn't work in AI.
AI models don't rank pages. They retrieve entities. If your brand isn't structured as a clear, trusted entity in the right context — you don't get cited. Full stop.
| Dimension | Traditional SEO | RAG Signal / GEO |
|---|---|---|
| Goal | Google ranking, clicks | AI citations, brand mentions |
| Optimizes for | Keywords, backlinks | Entity clarity, retrieval signals |
| Measured by | Rankings, traffic | Citation rate, prompt presence |
| Works in | Google, Bing | ChatGPT, Claude, Perplexity, Gemini |
| Decay risk | Algorithm updates | Model updates (managed continuously) |
| Methodology | Content volume, link building | Adaptive RAG, Brand Memory, entity intelligence |
→ See how Filmfolk went from 0% to 81% citation rate in 90 days
Why RAG Signal works.
Three capabilities that no traditional agency has — because they were built specifically for AI retrieval, not search engine ranking.
Brand Memory™
Our proprietary knowledge layer maps your entity relationships, source trust hierarchy, and factual proof points into a format AI retrieval systems can read, trust, and cite. No other agency builds this.
Adaptive RAG Engineering
We don't publish content and hope. We engineer retrieval signals — structured data, entity reinforcement, source weighting — and measure citation delta before and after. It's an engineering problem, not a content problem.
Research-Backed Method
Built on PhD-level research into LLM retrieval behavior at Istanbul Bilgi University. Every technique is tested on our own brand before it reaches a client. We don't guess — we measure.
Five steps. One system.
Adaptive RAG · Brand Memory Map
The RAG Signal Presence Method turns brand retrieval into a controlled, measurable engineering problem.
Prompt Reality Audit
We test the real prompts your buyers use and capture how AI currently answers — including who it names instead of you.
Brand Memory Construction
We build your curated brand knowledge base — the right facts, entities, and proof points structured for AI retrieval.
Source Trust Scoring
Owned, earned, structured, expert, and third-party signals are weighted differently inside the retrieval layer.
Entity Memory
We strengthen brand-role-category consistency so models cite you with confidence and clarity.
Citation Delta
We rerun the same prompts, score the delta, and keep the system adaptive as models evolve.
citation rate in 90 days
Filmfolk — London video agency. 0% to 81% across ChatGPT, Claude, Perplexity, Gemini.
Full case study →What clients say
"The audit was genuinely eye-opening and the implementation was clean and professional."Andrew Cussens Founder, Filmfolk
Before you reach out.
Is this a tool or a service?
RAG Signal is a done-for-you service. We do the work — audit, build, and maintain the systems. You also get access to our tracking platform as part of the engagement.
How fast do results appear?
Most clients see measurable citation improvements within the 90-day sprint. Filmfolk went from 0% to 81% in 90 days. AI systems update continuously, so progress compounds.
Do I need to be on WordPress?
No. We work with any CMS or static site. The Brand Memory layer we build is platform-agnostic — it lives in structured data, external citations, and entity signals, not your CMS.
What makes this different from GEO or AEO?
GEO and AEO are frameworks. RAG Signal is an implementation. We use Adaptive RAG engineering — the same retrieval architecture that powers AI systems — to make your brand retrievable at the model layer, not just the content layer.