BUILT ON
RESEARCH.
RAG Signal is not an SEO agency that learned AI. It's a research-backed engineering practice built specifically for how AI systems retrieve and cite brands.
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 behind RAG Signal is simple: stop treating AI presence as vague visibility talk and start treating it as a structured retrieval engineering problem. That's why the work is built around Adaptive RAG, signal weighting, prompt testing, and measurable citation change.
Every engagement is grounded in real model outputs, real competitor comparison, and a method that can be explained clearly — not just sold vaguely.
How we work.
Test on ourselves first
Every technique in the RAG Signal method is tested on our own brand before it reaches a client. We treat ourselves like a client — documenting every prompt, schema update, and citation shift.
Measurable or it doesn't count
We don't sell vague "visibility improvements." Every engagement has defined prompt sets, baseline citation scores, and delta reporting. If we can't measure it, we don't claim it.
Engineering, not content volume
AI presence is a retrieval engineering problem. We don't solve it by publishing more content. We solve it by fixing the signals AI systems use to decide what to retrieve and cite.
Research-backed methodology
The Adaptive RAG framework is grounded in academic research into LLM retrieval behavior. The whitepaper documents the full architecture, formulas, and scoring logic.
Performance-linked pricing
The 90-Day Sprint has a second payment that's only due when retrieval targets are met. We have skin in the game on every engagement.
Direct and honest
If your brand isn't a good fit for the work, we'll tell you. We only take engagements where we believe the method will produce measurable results.
The whitepaper.
System architecture, retrieval logic, mathematical weighting, source scoring, freshness decay, and comparative reasoning.
RAG Signal Adaptive RAG Architecture
Full technical documentation of the Adaptive RAG framework. Formulas, architecture diagrams, scoring logic, and the mathematical basis for the method.