The way customers find information has structurally changed. Most websites are invisible to AI-generated answers - not because they rank poorly, but because they were never built to be cited. We change that.
Traditional SEO was built around one goal: rank in position 1–10 and get the click. AI Search has broken that model. Customers are increasingly getting answers directly from AI-generated responses that cite sources, summarise content, and never send the user to your site at all — unless your content was built to be cited.
AI search platforms don't crawl and rank the way Google's traditional index does. They build a model of the world from structured and unstructured content — and they cite sources that their systems recognise as authoritative, coherent, and correctly structured. Understanding those signals is the first step to being cited.
AI systems model the world as entities — organisations, products, people, concepts. Your business needs to exist as a coherent, consistent entity across the web for AI to confidently cite you.
Entity OptimisationSchema markup, knowledge graph signals, and structured content formats help AI systems parse and understand what your pages are about — and what claims they're making.
Technical SEOE-E-A-T signals — experience, expertise, authoritativeness, trustworthiness — influence which sources AI treats as reliable. Thin, generic content is rarely cited regardless of domain authority.
Content StrategyAI crawlers (GPTBot, PerplexityBot, ClaudeBot, Google's AI agents) have different access and rendering requirements. Content blocked, deferred, or poorly rendered to these bots simply doesn't exist to AI.
Technical AccessChunked, headlined, logically structured content is easier for LLMs to extract and attribute. Walls of undifferentiated prose are regularly skipped in favour of well-organised alternatives.
Information ArchitectureAI systems cross-reference what your own site says against what third-party sources say about you. Inconsistent or contradictory signals reduce citation confidence.
Brand ConsistencyLLMO is the practice of making your brand, content, and technical infrastructure legible to large language models. It's not a replacement for SEO — it's what SEO needs to become. Search Labs delivers it as both a consulting discipline and a software capability.
Establish your brand as a coherent, trusted entity across the web — structured so AI systems can confidently represent and cite you. Covers Knowledge Panel signals, structured data, NAP consistency, and cross-platform entity alignment.
Learn moreRestructure and optimise your content so it's extractable, attributable, and citable by AI systems. Covers E-E-A-T, content chunking, heading architecture, authoritative sourcing, and topical depth.
Learn moreEnsure AI crawlers can access, render, and interpret your site. Covers robots.txt for AI bots, server-side rendering, schema implementation, crawl access for GPTBot, PerplexityBot, ClaudeBot, and Googlebot-AI.
Learn moreTrack and attribute your performance in AI-generated answers — citation rate, brand mention frequency, and AI-referred traffic. New metrics for a channel that traditional analytics tools weren't built to measure.
Learn moreOur proprietary tools — Prism, Stratum, Ballast, and Slice — are built to diagnose, optimise, and track the specific signals that determine AI Search performance. Available as standalone products and as part of consulting engagements.
We start every engagement the same way: understanding your current AI Search visibility before recommending anything. There's no point fixing signals you don't know are broken.
Fill in your details and your biggest question. We'll review your site and come back with a direct, specific assessment — not a pitch deck.
