At EWR Digital, we’ve spent the last few years not just executing digital strategy but actively redefining what modern strategy means. And one role that’s quietly becoming the backbone of next-gen search, content discovery, and digital performance?

Mike King discussed the the term: “Relevance Engineering” at SEOWeek.

This concept, but not this lexicon, I seen emerging across enterprise search teams, AI product squads, and high-level SEO/UX conversations. And frankly, it’s the missing link most organizations haven’t realized they need yet.

Love it! Thanks, Mike I will jump on this trend with you and support you in it.

So, What Is a Relevance Engineer?

A Relevance Engineer sits at the intersection of information retrieval, user experience, AI, content strategy, and digital PR (see the visual we use in our frameworks). They’re not just optimizing for clicks or rankings, they’re optimizing meaning. They’re engineering the connection between intent and outcome.

Think of it like this:

Relevance Engineers don’t just get people to a page. They make sure what’s on the page is what the user actually needed—and that it loads fast, ranks right, and scales.

Here’s what that looks like in practice:

• They own parts of the discovery stack: search, autocomplete, recommendations, structured data, and even how AI Overviews are triggered.
• They use a blend of proven techniques (BM25, keyword-based models, semantic search) and advanced strategies (LLMs, hybrid retrieval, NLP).
• They focus on real-time performance, uptime, and scalability because all time kills deals.
• They use schema, SERP feature targeting, and technical SEO not as checkboxes, but as levers for meaning-making.
• They’re data-driven. Measuring not just CTR, but engagement, conversion, and velocity.

They’re not trying to win Kaggle competitions. They’re trying to drive revenue and relevance for real businesses in real time.

The Relevance Engineering Stack (Based on What We’ve Built at EWR Digital)

We look at Relevance Engineering through a “modern marketing” lens:

• Information Retrieval → search engines, vector databases, and AI pipelines
• User Experience → site speed, mobile readiness, UX architecture
• Content Strategy → matching search intent with meaningful content at every stage of the funnel
• Digital PR → off-page signals, trust-building, and entity development
• Artificial Intelligence → using LLMs, embeddings, and AI agents to augment both discovery and production

At the center? Relevance Engineering—the art and science of creating signal, not noise.

Machine Learning Engineer vs. Relevance Engineer

There’s a lot of overlap. Both care about systems that scale. Both build with the user (and business) in mind. But here’s the difference:

Machine Learning Engineer	Relevance Engineer
Focuses on model deployment at scale	Focuses on matching information to intent
May work across any vertical	Lives in search, discovery, and user experience
Cares about performance and optimization	Cares about precision, speed, AND trust
Builds generalizable ML pipelines	Designs relevance signals customized to the vertical or brand

Why This Matters Now

With the rise of AI Overviews, multimodal search, and conversational UX, the old playbook of stuffing keywords and building random links just doesn’t work.

We’re moving into a world where:

• Intent must be interpreted in real-time
• Authority must be proven across platforms
• User experience must be near-instant
• Algorithms must be understood, not feared

I think what parts of the SEO community are starting to call Relevance Engineering, which I agree it’s no longer SEO, I would call a subset of a form of digital orchestration—aligning content, context, and user intent across systems to make search experiences truly resonate.

Digital orchestration refers to the coordinated management and integration of multiple digital systems, channels, tools, and touchpoints to create seamless, personalized, and efficient user experiences across the entire customer journey.

Think of it like conducting a symphony; each “instrument” (SEO, content, ads, CRM, analytics, automation, etc.) plays its part, but it’s the orchestration that ensures they all work together in harmony to drive business goals.

In the context of SEO and Relevance Engineering, digital orchestration might involve:

• Coordinating search data, user behavior, and machine learning models
• Aligning content strategy with real-time user intent signals
• Integrating structured data (like JSON-LD) with on-site UX and backend systems
• Ensuring consistency across platforms (Google, on-site search, chatbots, etc.)

Here’s the distinction:

Relevance Engineering

• Focus: Precision in matching user intent to search results/content.
• Scope: Primarily within search systems (e.g., search engines, site search, recommendations).
• Tools: Search algorithms, ranking models, ML/NLP, structured data.
• Goal: Maximize relevance — making sure users get the most useful content at the right moment.

Digital Orchestration

• Focus: Coordinating multiple digital systems and experiences.
• Scope: Cross-channel — search, content, CRM, automation, ads, web UX, etc.
• Tools: APIs, CDPs, marketing automation platforms, personalization engines.
• Goal: Create cohesive, high-performing journeys that feel intentional and connected.

Relevance Engineering is a tactic within the broader strategy of Digital Orchestration.

So yes, they’re connected. Orchestration is the conductor, and relevance engineering is one of the star performers!

And if you’re an executive, a founder, or a marketing leader (CRO, CMO, VP, Director, etc) wondering why your content isn’t performing like it used to, this might be the missing puzzle piece. (New Marketing Technology will be needed to deliver this.)

“Relevance isn’t just about showing up—it’s about showing up in the right moment, with the right answer, in the right way.”
— Matt Bertram

Want to Learn How to Think Like a Relevance Engineer?

At the agency I lead strategy for EWR Digital, we help companies architect their own Digital Growth Operating Systems powered by search, scaled by AI, and rooted in relevance. We offer consulting, training, and done-for-you engagements that bring together SEO, UX, content, and AI under one strategic lens.

If this resonated, the next logical step is understanding how your brand shows up not just in Google, but inside AI answer engines, chat interfaces, and multimodal search. That’s where our LLM Visibility™ program comes in.

It’s a framework we developed at EWR Digital to help brands harden their entity footprint, strengthen semantic relevance, and consistently surface as the authoritative answer inside AI-driven results. If you want your organization to not only rank, but be recognized across the new discovery landscape, explore LLM Visibility™ or reach out—this is where the future of search is already being built.

If you’re ready to step into the future of digital, let’s talk.