Why does my business not appear when someone asks an AI assistant like ChatGPT or Google's AI Overview for recommendations in my industry? +

AI assistants do not browse the web in real time when answering most queries. They draw on indexed, structured information about businesses — and if your website does not provide that information in a format these tools can reliably parse, your business is effectively invisible to them. The most common reasons a business is missing from AI-generated recommendations are: No structured data (schema markup). Schema markup is code added to your website that explicitly tells search engines and AI tools what your business is, what it does, where it operates, and how to contact you. Without it, AI tools have to guess — and they often guess wrong or skip your business entirely in favour of one with clearer signals. Inconsistent business information across the web. If your business name, address, and phone number appear differently on your website, Google Business Profile, Facebook page, and directory listings, AI tools struggle to resolve these into a single, trusted entity. Inconsistency reads as uncertainty, and uncertain entities are not cited. Vague or generic content. If your website describes what you do in broad, non-specific language — "we provide quality services to businesses of all sizes" — AI tools cannot confidently categorise you. Specificity is what gets you cited. A site that clearly states the services offered, the industries served, and the geographic area covered gives AI tools something concrete to work with. No established topical authority. AI tools favour businesses that have a consistent, coherent body of content around a specific topic. A website with five pages and no blog, no FAQ section, and no structured service descriptions has a thin authority footprint. Fixing this does not require a full website rebuild. It requires a structured review of your entity signals, your schema implementation, and your content clarity — followed by targeted improvements. This is part of what Interon addresses in a website audit.

Does my website need a security audit if it's just a small business site? +

Yes — and small business sites are often more exposed than larger ones, not less. Automated scanners don't discriminate by business size. They look for outdated CMS software, unpatched plugins, weak login configurations, and missing DNS security records. A small WordPress site running plugins that haven't been updated in 18 months is a target regardless of how much traffic it gets. A Security Audit reviews the actual risk layer: CMS and plugin versions, hosting environment, backup status and restore reliability, SSL configuration, and DNS records including SPF, DKIM, and DMARC for email deliverability. The goal isn't to alarm you — it's to give you a clear picture of what's exposed so you can fix it before it becomes a business problem. Most small business sites have at least two or three addressable issues that the owner didn't know existed.

My website looks great and loads quickly — so why is it still ranking poorly in search results? +

Visual appearance and search performance measure completely different things. A site can look polished on screen while being structurally invisible to search engines and AI assistants. The most common culprits are: pages with no meaningful heading hierarchy (H1, H2, H3 used decoratively rather than semantically), content that describes your services in vague marketing language instead of answering the specific questions your customers actually type, missing or broken schema markup that prevents Google from understanding what your business does and where you operate, and internal linking patterns that leave important pages orphaned — meaning no other page on your site points to them. Search engines and AI tools do not see your site the way a human visitor does. They read structure, signals, and relationships between pages. A site that looks beautiful but lacks those signals is effectively invisible to the systems that decide whether to surface it. If your site looks fine but is not performing, the problem is almost always structural, not cosmetic. A Site Health Audit will surface exactly which signals are missing and in what priority order they should be addressed.

How does Interon make Umbraco content machine-readable?

Interon builds Umbraco on a structured content model that maps directly to JSON-LD schema. Every page is machine-readable from launch, so search engines, AI systems, and future automation workflows can parse the content without extra cleanup.

Does Interon add schema markup to Umbraco sites?

Yes. Interon generates Organisation, Service, FAQ, Product, and other relevant schema directly from Umbraco content fields. Because the schema is tied to the fields, it stays in sync with the content and remains valid as pages change.

How does Interon handle technical SEO on the .NET platform?

Interon controls technical SEO at the platform level on .NET: clean URL structures, canonical tags, XML sitemaps, metadata, performance, redirects, and full control over page output. There are no SEO plugins or page-builder overrides adding bloat or breaking the markup.

Can an Interon Umbraco site connect to other business systems?

Yes. Interon uses an API-first architecture that connects Umbraco to CRMs, ERPs, forms, reporting tools, data sources, and agentic workflows through scalable .NET integrations.

You Can't See How AI Ranks You, So Build What It Can Read

Web strategy in the AI era has a strange shape. You spend your days optimising for systems nobody will let you look inside. You publish, you watch the traffic move, and when an AI answer surfaces a competitor instead of you, there is no panel that explains why.

Transparency in AI search rankings is coming. Regulators in the EU are already pushing large AI systems to disclose more about how they surface information, and the pressure is building in the US too. But here is the position I will defend: when that transparency finally arrives, it will not change what a machine fundamentally needs from your website. The fundamentals are already knowable. Most brands are just ignoring them.

The Transparency Debate Is a Distraction From the Real Work

When people talk about AI search transparency, they usually mean one thing: they want to know why a given answer cited a competitor instead of them. That is a fair frustration. But even if a regulator forced every AI system to publish a full decision log tomorrow, the answer would almost certainly be some version of: “We could not parse your content clearly enough to trust it.”

That is the uncomfortable truth. The problem is not that the black box is closed. The problem is that most websites are genuinely hard for machines to read, and the companies running them have spent years optimising for human eyes instead.

AI systems, whether they are powering a search answer or a procurement research tool, are doing something specific. They are trying to extract structured facts, verify claims against other sources, and assign a confidence level to the information they find. If your site makes any of those steps harder, you lose. Quietly, and with no explanation.

What “Readable” Actually Means for an AI System

This is not about keyword density or meta tags in the old sense. It is about whether a machine can pull a coherent, verifiable claim from your content and match it against what it already knows.

Here is what that looks like in practice:

Clear entity definition. Does your site state plainly what your company does, who it serves, and what category it operates in? AI systems build knowledge graphs. If your About page is a collection of aspirational adjectives instead of concrete facts, you are invisible to that graph.
Consistent structured data. Schema markup is not new, but most B2B sites use it inconsistently or not at all. An AI parsing your site for a “best accounting software for mid-market manufacturers” query needs machine-readable signals, not just well-written prose.
Citable, specific claims. Vague statements like “we help companies grow faster” give an AI nothing to work with. Specific claims like “customers reduce close time by 30% on average, based on a 2024 cohort of 140 accounts” give it something to cite and cross-reference.
Source credibility signals. Who links to you? Who quotes you? AI systems weight third-party mentions heavily because they are harder to game than on-page content. A single mention in an industry publication often does more work than ten blog posts you wrote yourself.
Content that answers a question completely. AI answer engines are trying to resolve a query without sending the user anywhere. If your content answers a question halfway and then asks the reader to “contact us to learn more,” the machine moves on to a source that finishes the job.

None of these things will change when transparency rules arrive. They will just become more visible as the reason you are losing.

Why Most B2B Sites Fail This Test

The honest answer is that B2B content has been written for a very specific human: a sceptical buyer who needs to be warmed up before they will talk to sales. That means lots of benefit language, carefully hedged claims, and a deliberate withholding of specifics to create a reason for the prospect to book a demo.

That approach made sense when a human was reading the page. It breaks down completely when a machine is.

A machine does not need to be warmed up. It needs facts. It needs structure. It needs your claims to be consistent across your homepage, your case studies, your LinkedIn profile, and your press releases. When those sources contradict each other, or when your homepage says one thing and your G2 profile says another, an AI system registers that inconsistency and discounts your authority.

I have seen this play out with a B2B SaaS company that had genuinely strong customer results but buried every specific number behind a gated PDF. Their competitors, with objectively weaker outcomes, were getting cited in AI-generated buyer guides because they published concrete data in crawlable HTML. The black box was not the problem. The PDF was.

The Structural Changes Worth Making Now

You do not need to wait for regulatory transparency to know what to fix. The signals are already there if you approach your site the way a machine does.

Start with an honest audit of your entity clarity. Can someone, or something, read your homepage and state in one sentence what you do, for whom, and at what scale? If the answer is no, that is the first fix.

Next, look at where your specific claims live. If your best proof points are locked in PDFs, slide decks, or gated case studies, you are hiding your credibility from the systems that are increasingly deciding whether buyers find you at all. Pull the numbers out. Publish them in plain HTML with proper context.

Then check your structured data. Tools like Google’s Rich Results Test or Schema.org validators will show you what a machine actually sees when it crawls your pages. Most B2B sites are surprised by how little structured information they are actually surfacing.

Finally, build a third-party citation strategy. Guest articles, analyst mentions, customer reviews on indexed platforms, and podcast appearances that get transcribed and published are not just PR plays. They are the external signals that tell an AI system your claims are worth repeating.

Transparency Will Not Save You. Readability Will.

When AI search systems are eventually forced to show more of their reasoning, the brands that benefit will be the ones that were already building for machine readability. They will not be surprised by what the logs reveal. They will recognise the pattern because they built for it deliberately.

The brands that suffer will be the ones that spent the last two years waiting to see inside the box before deciding what to do. By the time the box opens, the gap will be wide.

You do not need a regulation to tell you what a machine needs. You need to stop writing your website for a human who needs convincing and start writing it for a system that needs facts.

If you want a clear-eyed assessment of how your site reads to an AI system today, and a specific plan for closing the gaps, that is exactly the kind of work we do. Get in touch and we will show you what a machine sees when it looks at your brand.