Our Four-Step Solution

How We Work

A systematic methodology for optimising for AI Search. We diagnose, architect, prepare, and amplify—turning AI visibility from guesswork into engineering.

Competitive Visibility

See where you stand relative to competitors in the AI's knowledge layer. Understand who gets recommended, and why.

AI Selection Rate

Quantify how often AI systems choose your brand when answering queries in your category. The metric that matters in the generative era.

LLM Brand Sentiment

Analyse how AI models perceive your brand by examining semantic associations in their training data. No guesswork, just observable patterns.

SXO & Semantic Architecture

Optimising site architecture and EEAT signals to provide a crawlable, authoritative base for AI agents.

Entity Optimisation

Strengthen your brand's knowledge graph presence. Clear entity relationships mean consistent AI representation across platforms.

Content Authority Signals

We execute technical and content strategies to build "Machine Trust," ensuring your brand is the cited authority for your core topics.

AI Agent Optimisation

Prepare for the shift from human-driven search to AI-driven transactions by optimising for autonomous agent protocols.

Data Provenance

Verifiable, trustworthy data is the currency of AI commerce. Establish clear provenance chains that agents can validate.

Agentic Protocol Setup

Optimising product data for OpenAI's ACP, Google's UCP, and Anthropic's MCP protocols to ensure "choosability."

Generative Engine Advertising

Prepare the strategic infrastructure needed to leverage paid advertising opportunities within generative AI environments.

AI Advertising Insights

Analyse competitor Ad placements & copy.

Run campaigns with incredible intent signals

Ads in ChatGPT and Google's AI Mode offer incredible relevance & customer intent.

The Four Pillars of Relevance Engineering

01. Machine Readability First

We build structured data architectures that move beyond "best practices" to create verifiable machine trust.

02. Probabilistic, Not Guaranteed

We acknowledge that LLMs are probabilistic; our work focuses on influencing the weights and associations within the latent space to favor your brand.

03. Data Provenance

In the era of synthesis, verifiable data is currency. We establish clear provenance chains that AI agents can validate for transaction.

04. The Long Game

We avoid "hacks." We build technical foundations that evolve as models update, ensuring long-term visibility without technical debt.

Ready to Start?

Every engagement begins with understanding where you stand. Book a consultation to discuss your AI visibility challenges and explore the right starting point.

Get in Touch

Ready to become machine-readable?

Contact us to learn how Relevance Engineering can help your brand become AI-ready.