RAG Signal
The Brand, Defined

What is RAG Signal?

RAG Signal is an AI visibility engineering company. We make brands retrievable, trusted, and cited by AI models — ChatGPT, Claude, Perplexity, and Gemini. Our methodology is published, peer-reviewable, and open source under Apache 2.0.

Get Your Citation Baseline → Read the Methodology

Our Mission

The buyer journey changed. B2B buyers no longer search first — they ask first. When a buyer asks ChatGPT "who is the best provider of X?", the answer is determined by retrieval: which brands exist in the model's knowledge, how clearly they are defined, and how consistently they are cited. RAG Signal exists to make sure that answer includes you.

We treat brand presence as a data engineering problem, not a marketing narrative. We measure, engineer, and monitor the signals AI models use to decide who to cite — with a 77.1% average measured attribution rate across 10 deployments.

Our Methodology: Adaptive RAG

Adaptive RAG is RAG Signal's proprietary 5-step engineering process for brand visibility in AI retrieval systems. Unlike standard RAG — or traditional SEO — it continuously adjusts to model updates and competitive changes.

MAP prompt map BUILD Brand Memory WEIGHT signal weights REINFORCE across models MEASURE Citation Delta same prompt set Change the set and the comparison with the previous measurement is gone.
The process is a loop, not a list. The final phase re-runs the exact prompt set fixed in the first — that repetition is what makes the measurement comparable.

MAP

Discover the prompts where your buyers ask about your category.

BUILD

Construct your Brand Memory: entities, assertions, citation anchors.

WEIGHT

Score signals across 7 dimensions via the Signal Scoring Engine.

REINFORCE

Deploy and reinforce across all major AI models.

MEASURE

Track Citation Rate and Citation Delta at 30/60/90 days.

Signal Weighting — the third phase — scores every brand signal across seven dimensions: Source Authority (25%), Factual Consistency (20%), Entity Linkage (15%), Cross-Model Persistence (12%), Temporal Freshness (12%), Citation Frequency (10%), and Competitive Differentiation (6%). Weights are re-calibrated per model, because what earns a citation on ChatGPT differs from Gemini. Full details in the published whitepaper.

Source Authority 25% Factual Consistency 20% Entity Linkage 15% Cross-Model Persistence 12% Temporal Freshness 12% Citation Frequency 10% Competitive Diff 6% total 100
The seven signals do not carry equal weight. The first three account for 60% of the score on their own, which is also the order the work follows.

Founded by Bora Kurum

RAG Signal was founded by Bora Kurum — a consultant and trainer who spent years in software and systems engineering before turning to AI retrieval. The founding insight: brands were being left out of AI answers not because they lacked content, but because they lacked structured, machine-readable entity presence. The platform — 40+ modules for citation tracking, Brand Memory health, and competitive mapping — was built in-house. The methodology was published openly, because transparency is the point.

What Makes RAG Signal Different

Retrieval-first, not ranking-first

Google ranks pages; AI models cite sources. We engineer the retrieval layer — the place AI actually looks before it speaks.

Published, peer-reviewable methodology

Our Adaptive RAG architecture is open source (Apache 2.0). We don't sell black boxes — we publish the math behind the 77.1%.

Measured, not promised

Citation Rate, Citation Delta, Cross-Model Persistence — every engagement is tracked against concrete metrics at 30/60/90 days.

Cross-model by design

We deploy Brand Memory across ChatGPT, Claude, Perplexity, and Gemini simultaneously — not optimized for one platform at the expense of others.

Is your brand retrievable?

Start with a baseline audit — a complete report on your current AI citation landscape, delivered in 5 business days.

Get Your Citation Baseline →