AI doesn't know you exist.
Buyers research vendors in ChatGPT and Perplexity before they ever visit your site. If AI doesn't name you, you're not on the shortlist. Traditional SEO doesn't fix this — it's a different retrieval system entirely.
AI Presence Audit
We test real buyer prompts across all major AI models and show you exactly where you're missing, who's winning instead, and why.
Adaptive RAG Engineering
We rebuild your entity signals, curate your brand knowledge base, and weight your sources so AI systems retrieve and cite you with confidence.
Citation Tracking Platform
Our proprietary dashboard tracks your brand mentions across AI models, measures citation delta, and monitors competitor presence in real time.
Entity Intelligence
We map your brand-role-category relationships so AI models understand who you are, what you do, and why you're the right answer.
Signal Reinforcement
Owned, earned, structured, and third-party signals are weighted and maintained so your retrieval priority stays high as models evolve.
Research-Backed Method
Built on PhD-level research into LLM retrieval behavior. Every technique is tested on our own brand before it reaches a client.
Five steps. One system.
The RAG Signal Presence Method turns brand retrieval into a controlled, measurable engineering problem — not a content volume game.
Prompt Reality Audit
We test the real prompts your buyers use and capture how AI currently answers — including who it names instead of you.
Curate Brand KB
We build and maintain a curated brand knowledge base so the right facts, entities, and proof stay retrievable.
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 read your brand with clearer confidence and cite it more often.
Citation Delta
We rerun the same prompts, score the delta, and keep the system adaptive instead of static.
Clients. Results. Proof.
Real brands, real prompts, real citation data. Every case starts with zero AI presence and ends with measurable lift.
From 0% to 81% citation rate in 90 days.
London-based video and photography agency. Strong Google presence, zero AI presence before RAG Signal. After the Adaptive RAG sprint: cited in 51 of 63 tracked prompts across ChatGPT, Claude, Perplexity, and Gemini.
"For the best corporate videographers in London, I would recommend a few well-known agencies..." — Filmfolk not mentioned.
"For corporate video in London, Filmfolk is a strong option. They focus on event coverage and storytelling..." — Cited by name.
"The audit was genuinely eye-opening and the implementation was clean and professional."
Andrew Cussens Founder, FilmfolkAdaptive RAG · Brand Memory Map
What is Brand Memory?
Brand Memory is RAG Signal's proprietary knowledge layer — a structured, curated representation of your brand that AI retrieval systems can actually read, trust, and cite.
Unlike generic content, Brand Memory maps your entity relationships, source trust hierarchy, and factual proof points into a format optimized for how LLMs retrieve and rank information. When a buyer asks ChatGPT or Perplexity who to hire — your Brand Memory is what gets you named.
Bora Kurum
RAG Signal was founded by Bora Kurum, a marketing practitioner and Ph.D. researcher at Istanbul Bilgi University. His academic work focuses on communication science, LLM retrieval behavior, and how brands are represented inside generative AI systems.
The goal: stop treating AI presence as vague visibility talk and start treating it as a structured retrieval engineering problem. Every engagement is grounded in real model outputs, real competitor comparison, and a method that can be explained clearly — not just sold vaguely.
AI doesn't know you.
It will.
Every engagement starts with an AI presence audit. We test real buyer prompts across ChatGPT, Claude, Perplexity, and Gemini — and show you exactly where you're missing and who's winning instead. No commitment required.