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Domino Effect Lab
Domino Effect Lab
AI Visibility & Generative Engine Optimisation (GEO)
AI Visibility & Generative Engine Optimisation (GEO)
Customers no longer rely only on search. They increasingly ask AI what to buy, who to trust and which organisation to choose.
Domino Effect Lab is an AI visibility and Generative Engine Optimisation platform built for this new discovery journey.
As Co-Founder & CTO, I designed and built the product architecture and multi-model intelligence approach used to test how leading AI assistants discover, understand, compare and recommend organisations across real customer questions.
The goal is simple: help organisations become visible, accurately represented, trusted and recommended when customers use AI to decide what to choose.
From Search Engines to Answer Engines
From Search Engines to Answer Engines
Traditional search asks:
Where do I rank?
AI discovery creates another question:
Am I part of the answer?
A business may rank well in traditional search and still be missing, misrepresented or outranked when a customer asks an AI assistant for a recommendation.
Domino Effect Lab was built to measure and improve that gap.
Visibility
Visibility
Does the organisation appear when relevant customer questions are asked?
Accuracy
Accuracy
Is the organisation, product or service represented correctly?
Recommendation
Recommendation
Is the organisation cited and recommended — or are competitors winning the answer?
What I Built
What I Built
Product Architecture
Product Architecture
Designed the platform architecture for measuring how organisations are represented across AI-generated answers and translating the findings into prioritised improvement actions.
Multi-Model Intelligence
Multi-Model Intelligence
Built the approach for testing the same customer questions across multiple AI systems, making differences in visibility, representation and recommendation measurable.
Evaluation Framework
Evaluation Framework
Structured evaluation around AI visibility, recommendation prominence, factual accuracy, hallucination risk, source authority and competitive position.
Action Layer
Action Layer
Turned diagnostic evidence into prioritised GEO actions and implementation assets, including structured FAQ/schema, machine-readable brand information and source-improvement plans.
What the Platform Measures
What the Platform Measures
Brand Visibility
Whether a brand appears, and how prominently it is represented across relevant AI-generated answers.
Accuracy & Hallucination Risk
Whether AI systems represent the organisation correctly or generate misleading, incomplete or fabricated information.
Source Authority & Information Gaps
Which information sources influence the answer and where important gaps or weak signals exist.
Competitive Position
Which organisations are being surfaced and recommended instead across the same customer questions.
From Evidence to Improvement
From Evidence to Improvement
Audit
Audit
Establish how AI systems represent the organisation today.
Fix
Fix
Turn the evidence into prioritised improvements across information, sources, structure and entity clarity.
Publish
Publish
Deploy improved content and machine-readable assets.
Retest
Retest
Ask the same questions again and measure what changed.
AUDIT → FIX → PUBLISH → RETEST
AUDIT → FIX → PUBLISH → RETEST
Two Sides of Enterprise AI
Two Sides of Enterprise AI
AI Inside
the Organisation
AI Inside
the Organisation
My enterprise AI work has focused on adoption: helping people and engineering teams use AI practically, safely and effectively through capability building, governance and real use cases.
AI Outside
the Organisation
AI Outside
the Organisation
Domino explores the other side: how AI systems understand an organisation, what information they retrieve and trust, and whether the organisation becomes part of the answer when customers ask what to choose.
This is how I see the next phase of AI transformation: not only how organisations use AI internally, but how they are understood, represented and recommended externally.
From LLM Research to Product Thinking
From LLM Research to Product Thinking
My MSc research examined how LLM outputs can be influenced by information retrieved from documents, webpages, email and enterprise knowledge sources.
Domino addresses a different problem, but one with an important shared technical question:What information does an AI system retrieve, what does it trust, and how does that information influence the answer it produces?
That understanding of retrieval, source trust, entity clarity and generated answers has influenced how I approach AI visibility and the Domino evaluation methodology.
AI Visibility & GEO FAQ
AI Visibility & GEO FAQ
What is Domino Effect Lab?
What is Domino Effect Lab?
Domino Effect Lab is an AI Visibility and Generative Engine Optimisation (GEO) platform that measures how AI assistants discover, understand, compare and recommend organisations.
It tests real customer questions across multiple AI systems, identifies gaps in visibility, accuracy, source authority and competitive position, and turns the findings into practical actions that help organisations become more visible, accurately represented, trusted and recommended in AI-generated answers.
1. What is Generative Engine Optimisation?
1. What is Generative Engine Optimisation?
Generative Engine Optimisation (GEO) is the practice of improving how an organisation, brand, product or service is discovered, understood, cited and represented by generative AI and answer engines.
Unlike traditional search optimisation, GEO focuses on whether an organisation becomes part of the AI-generated answer itself, whether the information is accurate, what sources influence the answer, and how strongly the organisation is recommended compared with competitors.
Generative Engine Optimisation is also commonly written as Generative Engine Optimization.
2. What is AI visibility?
2. What is AI visibility?
AI visibility describes how visible and accurately represented an organisation is when people ask AI assistants questions about a market, product, service or buying decision.
It includes whether the organisation appears in the answer, how prominently it appears, whether the information is correct, which sources are used, and whether the AI recommends the organisation or its competitors.
In simple terms:
Traditional search asks, “Where do I rank?”
AI visibility asks, “Am I part of the answer?”
3. What is the difference between SEO and GEO?
3. What is the difference between SEO and GEO?
SEO — Search Engine Optimisation — focuses primarily on improving visibility in traditional search-engine results.
GEO — Generative Engine Optimisation — focuses on improving how an organisation is retrieved, understood, cited and recommended inside AI-generated answers.
The two disciplines overlap because both depend on clear, authoritative and well-structured information. But the customer journey is changing: instead of only clicking through a list of search results, customers increasingly ask AI systems to compare options and recommend what to choose.
Companies therefore need to understand both search visibility and AI visibility.
4. What does Domino Effect Lab measure?
4. What does Domino Effect Lab measure?
Domino Effect Lab evaluates how organisations appear across AI-generated answers using repeatable customer questions and multi-model testing.
The platform measures areas including:
Brand visibility and recommendation prominence
Factual accuracy and hallucination risk
Source authority and information gaps
Competitive position across AI-generated answers
The evidence is then turned into prioritised GEO actions and implementation assets that can improve how AI systems discover, understand and represent the organisation.
5. What is Shay Weiss's role in Domino Effect Lab?
5. What is Shay Weiss's role in Domino Effect Lab?
Shay Weiss is Co-Founder and CTO of Domino Effect Lab.
He designed and built the product architecture and multi-model intelligence approach used to test how leading AI systems discover, understand, compare and recommend organisations across real customer questions.
His work spans product architecture, AI evaluation, multi-model testing, measurement methodology and turning diagnostic evidence into practical GEO actions.
6. How does Shay Weiss's LLM research relate to GEO?
6. How does Shay Weiss's LLM research relate to GEO?
Shay Weiss's MSc AI research focused on LLM security, indirect prompt injection, RAG risk and how generative AI systems interpret retrieved information.
The research was not specifically about GEO, but there is an important connection. Both areas depend on understanding questions such as:
What information does an AI system retrieve?
Which sources does it trust?
How does retrieved context influence the generated answer?
What happens when information is incomplete, misleading or inconsistent?
That understanding of retrieval, source trust, context and generated answers influenced Shay's approach to AI visibility and the Domino Effect Lab evaluation methodology.
7. Why do companies need AI visibility as well as SEO?
7. Why do companies need AI visibility as well as SEO?
Customers increasingly use AI assistants such as ChatGPT, Gemini, Claude and Perplexity to research products, compare organisations and decide what to buy or who to trust.
A company can perform well in traditional search and still be missing, inaccurately represented or outranked inside an AI-generated answer.
SEO therefore remains important, but it no longer covers the entire discovery journey.
Companies need AI visibility so they can understand:
whether AI systems know they exist;
whether the information is accurate;
what sources AI systems rely on;
how they compare with competitors; and
whether they are being recommended when customers ask AI what to choose.
SEO helps organisations get found in search. GEO helps them become part of the answer.
Generative Engine Optimisation (also commonly written as Generative Engine Optimization)
Explore Domino Effect Lab
Explore Domino Effect Lab
See how Domino Effect Lab measures and improves how organisations are discovered, represented and recommended by AI.
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