Jenn Ortiz Navarro · Founder and Director of Shymow
Businesses do much more than they show. AI shouldn’t have to guess.
And they often change faster than their pages. I founded Shymow in 2015, after years working in marketing, digital strategy, SEO and digital advertising. Today I lead it to close that gap: we structure what your business knows, does and can demonstrate so that people and AI assistants have verifiable facts, rather than gaps they must fill with assumptions.
- Digital career
- Since 2008
- Shymow
- I founded it in 2015
- Current work
- Leading Shymow and our own knowledge governance system
I have seen the same problem recur: businesses gain experience, refine their services and find their own way of working, yet online they still appear simpler, older or more generic than the real business.
When knowledge is neither clear nor governed, every page, document or assistant can end up reconstructing a different version of the business.
How can there be strategy without knowledge?
That question sums up how I work. Before rewriting a page or choosing a tool, I need to understand what someone must know to choose that business and which facts support that choice.
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Identify. I understand the business, find the value the team has come to take for granted and look for what has been lost or contradicted across pages, data and sources. I also run dated tests to observe what search engines and assistants retrieve and omit.
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Decide. I separate what the organisation can demonstrate from what it merely wants to claim, and define what must be clear: identity, offer, audiences, evidence and limits.
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Coordinate and verify. I turn that definition into content, information, measurement and technology decisions, with criteria for verifying the implementation.
The result is not necessarily more content. It is a more faithful representation of the business, actionable priorities and a way to verify the work.
I can work on the sources a business controls and observe what third parties show at a particular point in time. I cannot decide what ChatGPT, Gemini or another external platform will answer. It would not be honest to promise that.
One problem, two levels of intervention
They are not chosen according to the size of the business, but according to where the mismatch originates.
- 01When the problem is concentrated on the website
- The business knows who it is, what it offers and what it can demonstrate, but its website does not represent that clearly. In that case, a focused intervention covering strategy, key pages, information architecture, content, structured data and technical checks may be enough.
- 02When the knowledge itself also needs structure
- Valid information is distributed across sources, versions, teams and decisions. The website is no longer the only unit of work: before publishing, it is necessary to decide what knowledge exists, how it is classified, which relationships it maintains, what evidence supports it, which version is current and who can review or approve it.
The second level requires a system
At Shymow, we develop and evolve our own knowledge governance and engineering system. Its architecture models knowledge as identifiable, classified objects: it makes explicit what they are, what function they fulfil and what kind of claim they contain; it also allows them to be related and associated with evidence where appropriate. It records their version and governance status too.
Its technical signature
- Unit
- Objects with explicit identity, function and classification.
- Context
- Relationships, sources and evidence that can be associated where appropriate.
- Governance
- Version, status and validation criteria.
- Representations
- A canonical source visible to people and machine-readable JSON and Markdown outputs.
This allows an organisation to review, approve and reuse knowledge while reducing its dependence on scattered versions and the need to reconstruct its context each time.
The software maintains that governed record and generates its representations. The system has already been implemented in software and continues to be developed and to evolve. It is not yet presented as a finished product: the work is approached in agreed phases.
What changes according to where the problem originates
| Diagnostic question | Website level | System level |
|---|---|---|
| Where does the mismatch originate? | In the public representation: offer, pages, content or technical signals. | In knowledge distributed across sources, versions, teams and decisions. |
| What do we work on? | Strategy, information architecture, content, structured data and checks within a defined website scope. | Knowledge objects, classification, relationships, evidence, versions, statuses and governance rules implemented in software. |
| What output do we control? | A clearer web representation and defined implementation priorities. | A governed record, canonical sources and machine-readable representations. |
They are not two equivalent plans, nor one route for small businesses and another for large ones. The diagnosis determines the level and phase each problem requires.
The method came later; the questions were already there
Long before generative AI became a commercial category, I was already interested in how we build, transmit and lose knowledge. That concern guided my educational choices: I hold a degree in Humanities, specialising in Cultural Management; a degree in Advertising and Public Relations; and a master’s degree in the Information and Knowledge Society, on the research track.
Then came years of marketing, digital strategy, SEO, digital advertising, content and web project management. AI changed the scale of the problem, not the thread running through my career. Over time, I recognised the same concern in different work: how to represent what we know without simplifying it until it becomes distorted, and how to allow that representation to evolve when better facts or perspectives emerge.
Today, I put that foundation into practice by leading Shymow. I have created my own knowledge governance and engineering system. I am fortunate to have an exceptional technical team that turns that vision into a technological reality.
A career built across different settings
I have worked mainly with small businesses and specialist B2B firms, as well as with larger brands, editorial projects and media outlets. That variety matters more than a list of logos: it has required me to translate value, coordinate people, organise information and connect commercial decisions with systems capable of executing them.
| Setting | Work and learning | References |
|---|---|---|
| Small businesses and specialist B2B firms | Strategy, SEO, content and digital representation. I learnt to identify the value a team has come to take for granted and still cannot explain. | Most of my professional career since 2008. |
| Larger-scale brands and content | Editorial and SEO work within structures involving volume, shared criteria and a need for consistency. | By 2015, Westwing was already working with me directly on SEO projects. |
| Editorial projects with media outlets | Establishing relationships, the homepage, SEO guidelines, keywords, liaison with media outlets and coordination of writers. | La Vanguardia and Mundo Deportivo, through Ever-Growing, which provided the platform and technical development. |
| Shymow and knowledge systems | I founded Shymow and today I lead it, supported by my own knowledge governance and engineering system. None of this would be possible without my incredible technical team, who turn this vision into a tangible technological reality. | Shymow since 2015 and the system we are currently developing and evolving. |
The names support specific work; they do not, by themselves, sum up my career.
What connects both contexts
AI is part of the current problem, but it does not explain all of my work. GEO, AEO, SEO, content and structured data can form part of a website intervention. At the second level, they are components of a broader knowledge governance and engineering architecture.
I read a website — and the system supporting it — as a commercial narrative, a decision, an experience, data and a technical representation. This is how I connect offer, content, measurement and implementation without treating them as isolated pieces.
Preserving meaning does not mean freezing it. It means making clear which version is current, which facts support it and what must change when the business changes.
I do not add technology to make a business look modern. I use it when it helps preserve what the business knows, explain what it does more clearly and support what it claims with facts.
Frequently asked questions about Shymow and my work
Labels help locate a problem; they are not enough to define its scope. These answers explain what we do, how the two levels work together and what can and cannot be promised.
Does Shymow work with SMEs, or only with complex organisations?
With both. An SME may need a focused website intervention or have a deeper knowledge problem; a large organisation may only need to correct a few pages. Size does not determine the level. We first identify where the mismatch originates and then define the scope.
What does “knowledge system” mean here?
It is our own system that models knowledge as identifiable, classified objects. Where appropriate, it allows them to be related and associated with evidence, and allows their version and governance status to be recorded. The software maintains that record, links it to a canonical source visible to people and generates machine-readable representations in JSON and Markdown. The system is under development and continues to evolve.
Is Shymow a GEO, AEO or AI SEO consultancy?
If you arrived here looking for a GEO (Generative Engine Optimisation), AEO (Answer Engine Optimisation) or AI SEO consultancy, then yes: a website intervention can address that challenge. But those labels describe only one part of Shymow. When knowledge is distributed across sources, teams and systems, SEO and structured data are components of a broader architecture, not the core of the work.
What can you actually do for my business?
Everything begins with understanding where your business’s information is being lost or distorted. Depending on that, our work may involve:
- Auditing your current footprint. Through dated tests, we check what your website, search engines and AI assistants currently show about your business, and identify contradictions, omissions or differences compared with what it can actually demonstrate.
- Working directly on the website. If the problem lies in the public representation, we adjust the strategy, information architecture, content, structured data and technical checks so that your business is expressed more clearly, consistently and verifiably.
- Building a governed record. If knowledge is scattered across teams, documents and versions, we work in phases with our own system so that your organisation can identify which information is current, review it, approve it and reuse it without losing context.
The scope is defined in writing before work begins. A diagnosis does not automatically include implementation, and the system is not yet presented as a finished product.
Why can ChatGPT, Gemini or other systems invent or distort information about a business?
Because they work with information from different sources and can make mistakes even when those sources are good. We compare dated responses with verifiable sources to identify omissions or contradictions in what the organisation controls, and separate those corrections from errors that we cannot attribute with certainty or control.
If your business knows more than it manages to explain, let’s talk.
The first step is not to choose a tool, but to understand what is being left out and where the problem originates.