Tyler Moncrieff

His work combines technical SEO, entity engineering, structured data, web development, AI visibility research, and automated measurement.

The problem he specializes in is straightforward: a business can have a working website, good services, and years of experience while AI systems still confuse it with another company, describe it incorrectly, or leave it out of relevant answers.

Tyler works on the technical and informational reasons that happens.

From bridge construction to AI entity engineering

Before founding RankOps, Tyler worked in bridge construction, advancing from laborer to carpenter and structures foreman.

That work involved reading plans, organizing crews, tracking work, and making sure what was built matched what was specified.

He carried the same attention to consistency into web development.

A website may identify a company one way while its Google Business Profile, social accounts, directories, and structured data describe it differently. Every version gives search systems another opportunity to misinterpret the business.

An individual error might be small. The combined effect can be substantial.

Tyler became interested in solving those problems systematically rather than treating each website issue as an isolated repair.

What is an AI Entity Engineer?

AI entity engineering is the technical work of making a person, business, product, or organization identifiable and distinguishable across digital sources.

For Tyler, that work includes investigating conflicting information, defining canonical identities, connecting supporting sources, implementing structured data, improving website architecture, and examining how AI systems interpret the resulting information.

Consider two contractors operating in nearby cities with similar names.

One has a clearly defined service area and consistent business information. The other has an outdated address, conflicting categories, duplicate listings, and a website that barely explains its services.

A search engine might still index both websites. But an AI assistant trying to answer a specific customer question has to determine which company the available information actually describes.

Tyler's job is to identify and repair the gaps that can make that determination harder.

That is what the free AI Visibility Map now scans for: AI brand ambiguity, when AI cannot tell which business a name means; identity collision, when other websites use the same or a similar name; and name hijacking, when someone else's product carries your name. RankOps lives with this itself: other companies sell SEO software as RankOps.ai and RankOps AI, and two more use "RankOps" for their own products, which is why its company record lists each one.

The goal is an accurate, verifiable representation of the business, not a manufactured claim of authority.

The Moncrieff Method

Tyler developed the Moncrieff Method as RankOps' operating framework for AI visibility and entity clarity.

It follows five stages:

1. Audit the signals. Identify what the website, profiles, structured data, and outside sources currently communicate.
2. Fix the entity. Correct conflicting business facts and establish consistent relationships between the business and its digital properties.
3. Publish the answers. Create useful pages addressing the actual questions people ask about the business, its services, and the markets it serves.
4. Build the citations. Improve corroboration across appropriate directories, profiles, and other relevant sources.
5. Track what AI says. Record how AI systems mention, cite, describe, and recommend the business over time.

The order matters because publishing more content does not necessarily resolve confusion about who published it.

Read the Moncrieff Method →

Testing the work on businesses he owns

Tyler's research is not limited to client projects.

He used Buy Sell Land NC, his own land acquisition business, to develop and test geographic entity architecture across all 100 North Carolina counties.

The project gave him a working environment for studying how location-specific content, business identity, structured information, and supporting sources relate to AI visibility.

In October 2026, RankOps documented 12 AI Visibility Map runs on buy-sell.land. The published record shows the site's score moving from 52 to 71 out of 100, alongside changes in how often Google Gemini named the company for the tested questions.

That is evidence from a founder-owned project, not an independent client result. The case study also shows the limits of the experiment, including the small prompt sample and the fact that it tested Gemini rather than every AI platform. It records timing, not cause: Gemini's answers can change for reasons nobody outside Google can see.

Read the Buy Sell Land NC case study →

An engineer who builds and deploys

Tyler works on implementation, not just recommendations.

His technical work spans static HTML, CSS, JavaScript, Cloudflare Pages, Cloudflare Workers, structured data, website information architecture, SEO automation, and systems for collecting and comparing AI visibility results.

When a static website provides the appropriate level of control, he can build and deploy it.

When a business uses Shopify, WordPress, or another platform, the implementation has to respect the platform's actual capabilities.

Changing hosting providers does not automatically make a business more visible to AI. What matters is the information the site publishes, whether systems can access it, and whether reliable sources support it.

The difference between being seen and being trusted

Tyler separates three outcomes that are often grouped together.

• A mention means an AI response names the business.
• A citation means the response references a source associated with the business.
• A recommendation means the response actually suggests the business as a suitable choice.

They are not interchangeable.

A business can be cited without being recommended. It can also be described accurately while being excluded from a shortlist.

That distinction shapes Tyler's work at RankOps. Technical changes are evaluated for the specific problems they address, and AI responses are measured rather than assumed.

Current work

Tyler leads RankOps' AI visibility engineering, technical website projects, and development of the Moncrieff Method.

His focus is on building systems that help businesses maintain accurate digital identities and measure how those identities appear across search and AI platforms.

He also works with agencies that need specialist implementation support without building an internal entity engineering practice.

Questions about Tyler Moncrieff

Contact Tyler

For questions about entity engineering, technical implementation, or working with RankOps, contact Tyler directly.

Email: tyler@rankops.net · Phone: (828) 471-0032

Get your free AI Visibility Map →

Learn about RankOps · What is RankOps? · View the case studies