RAG Signal Adaptive RAG for AI presence

Get Cited in AI Answers

RAG Signal is the engineering layer for your AI presence. We move your brand from "Unknown" to "Highly Cited" in ChatGPT, Claude, and Perplexity responses.

Case studies Read whitepaper
AI visibility score Citation tracking Adaptive RAG
FOMO signal

Your buyers ask AI before they ask you.

When AI gives three answers and your brand is absent, the shortlist is already moving without you. AI presence is not nice to have anymore. It shapes perception before a click, before a call, before a demo.

Competitor appears in answer Entity alignment, clearer positioning, stronger supporting sources.
you absent
Competitor is cited repeatedly Repeated retrieval creates trust even before the user verifies anything.
memory loss
Competitor becomes the default answer Once models build the pattern, recovering attention gets harder and more expensive.
decision drift
"We do not optimize pages first. We optimize how AI systems calculate your brand."

RAG Signal is built for AI presence. The system combines weighted source selection, freshness-aware retrieval, entity confidence, citation reinforcement, and a curated brand KB into one Adaptive RAG workflow.

Weighted brand memory High-value sources inside the curated KB count more than weak mentions.
Freshness-aware retrieval Newer verified signals stay stronger in the system.
Competitor delta tracking We measure where others are being named before you are.
Foundations

What is RAG?

Retrieval-Augmented Generation (RAG) is the architecture behind most AI answer systems. When ChatGPT, Perplexity, Claude, or Gemini answer a question, they do not rely on memory alone — they retrieve relevant content from a knowledge layer and use it to generate the response.

What gets retrieved determines what gets said. If your brand is not in the retrieval layer with the right signals, it does not appear in the answer — regardless of how strong your conventional SEO is.

RAG Signal method

What is Adaptive RAG by RAG Signal?

Adaptive RAG is our proprietary framework that goes beyond static knowledge injection. We continuously adjust which sources enter the retrieval layer, how they are weighted, how fresh they are, and how consistently they reinforce your brand's entity signals.

The system adapts to model behavior, competitor pressure, and prompt patterns — so your brand stays retrievable as AI systems evolve, not just at the moment of setup.

Our built method

The RAG Signal Presence Method.

We built our own framework for AI presence. It turns brand retrieval into a controlled system: what enters the knowledge layer, what gets more weight, what decays, what reinforces memory, and what actually changes citation share.

A proprietary framework for moving brands from missing to cited.

This is the operating model behind our work. Not generic SEO, not content volume, and not one-time prompt hacks. We use the same method every sprint, then adapt the weights to the category, model behavior, and competitor pressure.

Built by RAG Signal
01 Map prompt reality

We start with the real prompts buyers use and the answers AI already gives across your category.

02 Curate brand KB

We build and maintain a curated brand knowledge base so the right facts, entities, and proof stay retrievable.

03 Weight source trust

Owned, earned, structured, expert, and third-party signals are weighted differently inside the retrieval layer.

04 Reinforce entity memory

We strengthen brand-role-category consistency so models read your brand with clearer confidence.

05 Measure citation delta

We track whether your brand moves from absent to named, cited, and repeatedly surfaced against competitors.

Audit Test real prompts and capture how models describe you, your category, and your competitors.
Build Correct weak signals, reinforce source quality, and reshape the retrieval context around your brand.
Measure Rerun the same prompts, score the delta, and keep the system adaptive instead of static.
Framework mechanics

How the method operates underneath

Our curated brand KB is never static. It is updated, weighted, and re-scored over time. High-authority mentions carry more weight. Old signals lose force. Repeated entity consistency raises confidence. Contradictions trigger manual review.

Weighted sources Owned, earned, structured, third-party, and expert references do not count equally.
Freshness decay Newer verified signals receive stronger retrieval priority than stale claims.
Entity confidence Brand-role-category consistency is measured across the curated knowledge layer.
Competitor delta We compare your mention share and retrieval quality against the brands already being surfaced.
Adaptive RAG graph animated model map
Method scoring layer

The main score combines five weighted signals to estimate how strongly AI systems are likely to retrieve and cite the brand.

AdaptiveScore
=
0.34×SemanticMatch
+0.24×EntityConfidence
+0.18×SourceAuthority
+0.14×FreshnessScore
+0.10×CitationConsensus
Semantic Measures how closely the brand matches the real prompts buyers are using.
Entity Measures whether the brand, role, and category are being interpreted consistently.
Authority Measures how much weight the supporting sources should carry in retrieval.
Method decay + lift

These two formulas control how older signals decay over time and how much the brand has improved versus baseline.

FreshnessScore
=
exp(−λ·agedays)
PresenceDelta
=
MentionSharenow − MentionSharebase max(MentionSharebase, 0.01)
Freshness Gives newer verified signals more influence than stale mentions.
Delta Shows the relative movement between the current mention share and the starting baseline.
Whitepaper

RAG Signal Adaptive RAG Whitepaper

System architecture, retrieval logic, mathematical weighting, source scoring, freshness decay, and comparative reasoning. If the method matters to you, this document matters.

Adaptive RAG Formulas Architecture Curated brand KB
Engine Preview

Engineering your brand memory.

We don't just write content. We adjust the mathematical retrieval weights that modern AI models use to determine which brand deserves the citation.

// Adaptive RAG Signal Adjustment
{
"entity": "Filmfolk",
"signal_weight": 0.94,
"freshness_decay": 0.02,
"retrieval_priority": "HIGH",
"citation_status": "ACTIVE"
}
Case study

Filmfolk: from absent to cited.

Filmfolk already had strong conventional visibility. The problem was AI presence. Before the work, the brand was missing from relevant AI answers. After the Adaptive RAG and signal correction work, Filmfolk was cited by name in tracked prompts.

Latest case Filmfolk London 63 tracked prompts
Verified

London-based video and photography agency. Strong Google presence, but no meaningful AI presence before RAG Signal. We rebuilt the entity signals, curated the supporting KB, and tested the same prompt set before and after implementation.

81% AI citation rate across the tracked query set.
51/63 Prompts with a direct Filmfolk brand mention.
Before

"For the best corporate videographers in London, I would recommend a few well-known agencies with strong event and brand portfolios..."

Filmfolk not mentioned
After

"For corporate video and event videography in London, Filmfolk is a strong option. They focus on event coverage and storytelling for corporate clients, conferences, and brand content..."

Filmfolk cited by name
Engagement model

Simple structure. Strong accountability.

You can start with an audit, move into a sprint, or stay on a retainer. The important part is that the work stays measurable and focused on AI presence.

Start here

AI Presence Audit

By application
We review each request before starting. If it is a good fit, the audit is at no cost.
  • Prompt testing across major models
  • Competitor mention review
  • Signal gap analysis
  • Clear next-step recommendations
Core engagement

90-Day Adaptive RAG Sprint

€499 + €899
€499 upfront to start. The €899 second payment is only due when agreed retrieval targets are met.
  • Adaptive curated brand KB build
  • Entity and source reinforcement
  • AI presence recalculation
  • Prompt reruns and delta reporting
Ongoing

AI Presence Retainer

€399/mo
12-month contract. Consistent measurement, adaptation, and competitive monitoring every month.
  • Monthly reruns and adjustments
  • Freshness and drift management
  • Competitor delta updates
  • Priority support
About

Research-backed methodology. Practitioner-led execution.

Bora Kurum
Ph.D. Candidate - Bilgi University
Marketing practitioner
linkedin/borakurum

Bora Kurum

RAG Signal was founded by Bora Kurum, a marketing practitioner and Ph.D. researcher at Istanbul Bilgi University. His 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 problem. That is why the work is built around Adaptive RAG, signal weighting, prompt testing, and measurable change.

Every engagement is grounded in real model outputs, real competitor comparison, and a method that can be explained clearly, not just sold vaguely.

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Contact

Tell us your brand and category.

If you want to understand how AI currently frames your brand, where competitors are winning, or what your Adaptive RAG setup should look like, send us a short note. You will get a direct reply from us.

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