# RAG Signal — AI Visibility Engineering

> RAG Signal engineers your brand into AI answers. We move brands from absent to cited in ChatGPT, Claude, Perplexity, and Gemini through Adaptive RAG, Brand Memory, and entity intelligence.

## What We Do

RAG Signal is an AI visibility engineering firm. We solve a specific problem: your brand is not being cited when buyers ask AI systems who to hire, what to use, or who the best option is in your category.

Traditional SEO does not fix this. 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.

We fix that through:
- **Brand Memory™** — our proprietary knowledge layer that maps entity relationships, source trust hierarchy, and factual proof points into a format AI retrieval systems can read and cite
- **Adaptive RAG Engineering** — structured retrieval signal work: entity reinforcement, source weighting, schema, and continuous prompt testing
- **Citation Tracking Platform** — real-time monitoring of brand mentions across ChatGPT, Claude, Perplexity, and Gemini

## Proven Results

- Filmfolk (London video agency): 0% → 81% citation rate in 90 days across 63 tracked prompts
- Active clients: UEC Energy (UK), Secret Brokerage (TR), ABS Middle East (AE), ABS Philippines (PH), AI Edge UK (GB), Benoplast (TR), Volimax (TR), Centrum Plaza (TR)

## Services

### AI Presence Audit — Free (by application)
Test real buyer prompts across all major AI models. Identify where your brand is missing, who's winning instead, and what the signal gaps are.

### 90-Day Adaptive RAG Sprint — €599 + €1099 (performance-linked)
Full Brand Memory build, entity reinforcement, source weighting, and citation delta reporting. €599 upfront. €1099 only when agreed retrieval targets are met.

### AI Presence Retainer — €599/month
Ongoing Brand Memory maintenance, freshness management, competitor monitoring, and monthly delta reporting.

## Brand Memory™

Brand Memory is a RAG Signal-exclusive methodology. No other agency builds or maintains this layer. It maps:
- Entity relationships (brand, role, category, location, proof)
- Source trust hierarchy (owned, earned, structured, expert, third-party)
- Factual proof points optimized for LLM retrieval

## Methodology

1. **MAP** — Prompt Reality Audit: test real buyer prompts, capture baseline citation data
2. **BUILD** — Brand Memory Construction: curated knowledge base with weighted source hierarchy
3. **WEIGHT** — Source Trust Scoring: signal weighting for retrieval priority
4. **REINFORCE** — Entity Memory: brand-role-category consistency reinforcement
5. **MEASURE** — Citation Delta: rerun prompts, score improvement, adapt continuously

## About

Founded by Bora Kurum, Ph.D. researcher at Istanbul Bilgi University. Academic focus: communication science, LLM retrieval behavior, and brand representation in generative AI systems.

## Key Pages

- [Home](https://ragsignal.com/)
- [How We Work](https://ragsignal.com/services/)
- [Platform](https://ragsignal.com/platform/)
- [Case Studies](https://ragsignal.com/results/)
- [Pricing](https://ragsignal.com/pricing/)
- [About](https://ragsignal.com/about/)
- [FAQ](https://ragsignal.com/faq/)
- [Contact](https://ragsignal.com/contact/)
- [Free Audit](https://ragsignal.com/audit/)
- [Entity Directory](https://ragsignal.com/entities/)
- [Whitepaper](https://ragsignal.com/RAG%20Signal%20Adaptive%20RAG%20Architecture.pdf)

## Contact

- Website: https://ragsignal.com/
- Email: info@ragsignal.com
- LinkedIn: https://www.linkedin.com/in/borakurum/
- Location: Kadikoy, Istanbul, Turkey

## Technical

- Schema types: Organization, ProfessionalService, Person, FAQPage, Service
- Primary entities: RAG Signal, Bora Kurum, Adaptive RAG, Brand Memory, AI visibility, GEO
- Target platforms: ChatGPT, Claude, Perplexity, Gemini
- Canonical: https://ragsignal.com/