Search is evolving into something faster, smarter, and less visible.Retrieval-First™ is the framework I built to bridge that change: a system designed for how AI engines actually see, store, and surface information. It’s not about chasing rankings anymore — it’s about engineering content that can be retrieved, cited, and trusted by machines and people alike.
Martin Jeffrey, Founder of Harton Works
Retrieval-First™ is the framework I built to bridge that change: a system designed for how AI engines actually see, store, and surface information. It’s not about chasing rankings anymore — it’s about engineering content that can be retrieved, cited, and trusted by machines and people alike.
Why Retrieval-First™ Matters
From a user’s perspective, retrieval feels instantaneous. You type a question and an AI system delivers an answer in a blink.
But that answer isn’t chosen in the moment.
It’s the product of weeks and months of groundwork. AI engines lean on cached embeddings — mathematical maps of meaning — and entity graphs like Google’s Knowledge Graph to understand relationships. They also use infrastructure such as Google’s Mustang, which pre-maps billions of connections so retrieval happens faster.
When the query is asked, real-time systems like Navboost (learning from clicks and behaviour) and Glue (tying signals together) decide what makes the cut. Visibility isn’t about the instant. It’s about whether your content has already been made discoverable, extractable, and citable.
And yet, retrieval can also move quickly. We’ve seen new pages cited in Gemini within 24 hours. In the AI era, depth compounds over time, but speed can surface opportunity almost immediately — if your content is engineered for it.
That’s why we created the Retrieval-First™ framework at Harton Works: to give brands a methodology built for how AI search actually works, not just how Google used to.
Why I Built Retrieval-First™
After 27 years in SEO — working with brands from ASDA and Tesco to Nike and Reebok — I saw the same pattern repeating across sectors. Traditional SEO, even when executed flawlessly, was no longer enough to secure visibility. Clients held top Google rankings but were invisible in AI search. The industry needed a framework that recognised this shift.
Since 1998 I’ve seen every wave of change: meta tags, link building, mobile-first, semantic search. Each altered how brands were discovered. But nothing has disrupted the fundamentals as dramatically as AI-powered retrieval.
By 2023–2024, the warning signs were clear. Pages that dominated Google weren’t surfacing in Gemini or Claude. Brands with huge SEO investment disappeared in ChatGPT or Perplexity. Strategies built for rankings weren’t translating into retrievability.
Retrieval-First™ was born from this necessity — not theory, but practice, forged through client work and decades of experience.
What Retrieval-First™ Means
Most agencies still optimise for rankings and treat AI as an optional add-on. At Harton Works, Retrieval-First™ is the foundation.
→ Optimise for search engines. Technical SEO and content discipline remain non-negotiable.
→ Engineer for retrieval. Content is structured so AI can parse and cite at the passage level — using headings, schema, chunking, embedding similarity, and entity linking.
→ Measure differently. Rankings matter, but the real signal of visibility is retrievability across AI engines.
Because in AI search, being indexed isn’t enough. You have to be retrieved.
The Retrieval-First™ Engine™
Retrieval-First™ is a framework for visibility in the age of AI.
We deliver it through a five-stage framework that adapts to each client, while following a consistent logic: discover, align, engineer, monitor, and evolve.
3.
Content Engineering
We transform content into AI-readable, atomic units.
→ Passage chunking and optimisation.
→ Retrieval-focused rewriting
→ Embedding and prompt alignment.
4.
Monitoring & Optimisation
We keep pace with shifting AI behaviour.
→ AI citation monitoring
→ Performance dashboards
→ Competitor refresh cycles
Gemini FAQ Retrieval: A new FAQ page optimised with this approach was cited in Gemini within 24 hours.
Sector Audits: In finance and travel, audits exposed clusters where competitors ranked strongly in Google but were absent in AI retrieval.
Content Ecosystem Design: Using Query Fan-Out Mapping, we rebuilt content ecosystems to reflect how AI actually connects questions and answers.
Why Harton Works?
Because this is where Retrieval-First™ started.
We formalised the framework at Harton Works in 2024 — building on early industry conversations, but codifying it into a tested methodology supported by proprietary tools like Language Radar™, Content Sufficiency Audits, and AI Retrieval Tracking.
After 27 years of leading SEO for brands like ASDA, Tesco, Nike, and Reebok, I knew the industry needed a new model for visibility. Retrieval-First™ was the answer.
Others may reference “retrieval-first SEO” as a concept. At Harton Works, Retrieval-First™ is a proven methodology — applied in the field, refined in practice, and measurable in results.
Final Thought
SEO has always evolved — from tags and backlinks to semantic search and zero-click results. Retrieval is the next evolution.
Retrieval-First™ was built to lead that shift, a framework forged from decades in the field, engineered for how AI systems actually see, store, and surface information.
Because as AI engines deepen their models, visibility will no longer belong to those who simply publish. It will belong to those whose content can be retrieved — who understand that in the new search economy, retrievability is the true currency of visibility.
Retrieval First SEO FAQs
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