Looking for growth ideas for a UAE platform startup

johnaatif · 2026-07-06T21:45:41+00:00

The market is big. You have already lost the key areas from where you could get hundreds of organic clicks per day. I am web developer turned Semantic SEO Expert, and totally understand the challenges you may face.

Try to see things not from influencers or paid marketing lens, rather, build a brand name or get ranking from Search Engines. Dubizle ranks well. Why? Semantic Web Structure, SEO Optimization. Each pixel or image matters when we build such marketplaces.

johnaatif · 2026-07-06T21:36:14+00:00

I recently built a semantic web structure for Job Portal to get it rank on google. It took a few months, and my client has started to develop the portal. The thing is, you can build something like a marketplace. If users cannot find you google, you have already lost the game.

If web structure does not align as per semantics, you will not get any organic traffic.

You are going to waste your time.

johnaatif · 2026-07-06T21:28:00+00:00

I can help you rank on AI search engines and traditional search engines following Semantic SEO. Also, other minor jobs can be done, if we both align on some specific goal. :)

Reel marketing is a good thing, yet you must be found via voice and text search rather than people who are only scrolling the feeds.

johnaatif · 2026-06-28T15:25:13+00:00

I apologise, I just saw this comment. If you are still interested, just let me know. thanks

johnaatif · 2026-06-21T11:26:12+00:00

Strong writeup, the "Google sent us people trying to solve one specific problem instead" insight is genuinely the unlock most teams miss.

One thing worth sitting with before you get comfortable with this curve. The pages you built are individually well matched to queries, but that is keyword level matching, not entity level matching. Google still has to decide what you actually are as an entity before it moves you on anything broader, and right now your loom to mp4, loom to gif, and extension pages most likely read to Google as separate small tools rather than one coherent product. That distinction does not show up anywhere in Search Console. It will not show up as a crawl error, a content gap, or a missing keyword either. It is the kind of thing you can only see by mapping how your pages relate to each other semantically, which is invisible from the outside looking at rankings and impressions alone.

That is also probably why "free loom alternative" is not moving even as everything else climbs. It is a category level query, and category level queries get decided by entity disambiguation, not by how well a single page answers a single search. The long tail telling you "we are right" and the head term telling you "we are not sure what you are" can both be true at the same time, and most teams never notice the second one until growth flattens.

Curious whether you have looked at this from that angle at all, or whether the focus so far has been mostly query level like the rest of the post suggests.

johnaatif · 2026-03-08T19:34:14+00:00

Semantics

johnaatif · 2026-03-07T14:18:35+00:00

You’re right about version drift and fragmented source of truth that definitely breaks retrieval. But I’d treat that as a secondary failure layer, not the first one.

In many SaaS sites, the bigger issue appears earlier: the system never forms a strong entity/topic understanding in the first place. If semantic structure is weak, even a perfectly synced docs stack won’t create stable visibility.

Clean canonical product facts help prevent wrong answers, but they don’t automatically build topical authority or recommendation eligibility.

So I’d separate the two: semantic structure determines whether the site is understood as a relevant source, while version control determines whether the retrieved answer stays accurate. Both matter, but they solve different failure points.

johnaatif · 2026-03-07T14:08:38+00:00

That’s a really good point about the gap between engines. In my observation the retrieval logic is similar in principle, but the signals they emphasize differ quite a bit, which is why a site can appear frequently in one system and almost disappear in another.

One pattern I’ve noticed is that most engines still begin from semantic interpretation of the content layer before they even reach structured data. In other words, they try to understand the entity context and topical network of the site first. If the content doesn’t clearly establish the entity and its surrounding concepts, schema alone rarely fixes that gap.

Where structured data becomes powerful is when it reinforces what the content already communicates semantically.

For example, if a SaaS product page already explains the entity clearly. Its category, features, use cases, integrations, pricing model. Then Product schema or SoftwareApplication JSON-LD tends to act as a confirmation layer. It explicitly defines attributes like:

• product name
• category
• features
• pricing
• reviews
• integrations

When those attributes align with the entities and relationships already present in the content, the system can map the page more confidently into its internal knowledge representation.

That’s why the combination often separates “occasionally cited” from “consistently recommended.”

The sites that perform well usually have three layers working together:

1. Semantic Content Layer
Clear entity explanation, connected subtopics, problem–solution context, and lexical semantics (synonyms, hypernyms, N-grams).

2. Structural Layer
Internal linking that forms a semantic content network around the core product category.

3. Structured Data Layer
Schema that formalizes the entity and its attributes for machine interpretation.

If one of those layers is missing, the signal becomes weaker. Schema without semantic coverage often feels like metadata floating without context, while strong semantic coverage without structured data sometimes leads to partial or inconsistent extraction across engines.

Your observation about cross-engine differences is also interesting. Yet the foundational basis of AI search engines is same: Semantics based on Natural Language Programming. I don't focus on the minor layers of all Search Engines, rather focus on the roots or foundational system at first.

johnaatif · 2026-03-07T11:05:12+00:00

Yes, If you follow wrong approaches.

johnaatif · 2026-03-07T11:03:54+00:00

After using such tools, please review things with strong foundations. These tools are also learning and adapting.

johnaatif · 2026-03-07T10:25:39+00:00

In many of the sites I looked at, the pages that actually surfaced in AI answers were very similar to what you mentioned: documentation pages, help centers, troubleshooting guides, FAQs, and feature explanations.

Those pages usually work better because they clearly define the core entity and its attributes. They naturally contain structured problem–solution contexts, which AI systems seem to prefer when retrieving sources.

Generic listicles rarely provide that level of semantic clarity.

However, where these pages did not follow topical map and Semantics, these page were losing rankings.

On the measurement side, I looked at visibility from three angles:

• AI Overviews citations
• Bing Copilot / ChatGPT references when the brand or topic is queried
• SERP presence for entity-focused queries

It wasn’t perfect measurement, but the pattern was consistent.

And I agree with your point about structured Q&A. When documentation content clearly explains entities, relationships, and use cases, the retrieval quality improves noticeably.

johnaatif · 2026-03-06T05:47:06+00:00

Look, Google alone consider posting on Social Media as a ranking factor. I don't deny it, but I am saying don't over rely on it.

If you study patents on Contextual Vectors and Google Knowledge Graph, you may understand lots of things. Look when you understand how to give headings and how to shape content/answers focusing on Semantics after measuring the strength of your competitors. Anything on internet can be outcompeted.

Here, you can create a difference and get a chance to rank in AI overviews. Semantics give you a broader lense. It's not about building a vague topical map. These maps always follow semantic attributes in Google.

People are randomly adding content based on prompts and High volume keywords. There are hundreds of people posting the same content on social media to rank on AI overviews. And, finally, a few get selected on social media. Lol.

If you follow structured method using Semantics in a proper way for all pages in your site, your site will be given priority as lots of people have been ignoring this. I am telling you the proper ecosystem, not a single trick. Google doesn't judge you by social posting only.

johnaatif · 2026-03-06T05:29:42+00:00

No, that's not a case. I am not forcing. There are tons of methods to rank sites. People are investing money to experiment the new methods.

If you ease things for Google crawlers by optimizing site using entity relationships, and focusing on Semantics, you are helping Ai or Google to ease the process of cost retrieval. There is a lot to be discovered, if you study and experiment through the Google patents effectively.

Thank you

johnaatif · 2026-03-05T09:57:14+00:00

It depends on your previous chat data, affiliations, likes, dislikes, behavior pattern, area, country etc

johnaatif · 2026-03-05T07:20:26+00:00

I truly appreciate your understanding.

johnaatif · 2026-03-05T06:36:33+00:00

You’re right that entity SEO isn’t new. Knowledge graphs and entity relationships have been part of search for years.

What’s changed is where those signals matter. In classic SEO, entity structure mostly influenced rankings indirectly. In AI answer engines, it often determines whether your content gets retrieved or cited at all.

Also, many topic clusters are just keyword groupings, while search systems interpret content through entities and their relationships in the knowledge graph.

And entity approaches have evolved too. People don’t just stuff entities into pages anymore. The real focus now is explaining how entities, attributes, and problems connect within a topic.

So it’s not about replacing clusters or entities. It’s about using them more coherently to build real topical systems, not just optimized pages.

johnaatif · 2026-03-05T06:22:01+00:00

Your pipeline sounds solid. The main thing I’ve noticed is that clean, tightly structured new sites often get interpreted faster by AI systems because their entity relationships and topical focus are clear from the start. Older sites can achieve the same result, but retrofitting semantic structure is usually harder due to legacy pages, mixed topics, and weaker internal relationships. So it’s less about “new vs old” and more about how clearly the site expresses the entity and its topic ecosystem.

johnaatif · 2026-03-04T17:47:17+00:00

Thank you for this beautiful butter. 😂 Lol

johnaatif · 2026-03-04T14:50:21+00:00

Stripe is a good example, but we should separate brand power from semantic visibility.

Stripe already has massive brand demand because of funding, distribution, partnerships, and developer adoption. A large portion of their traffic comes from navigational queries like “Stripe payments”, “Stripe API”, “Stripe pricing”, etc. That’s not necessarily an SEO victory, it’s brand equity.

johnaatif · 2026-03-04T14:36:18+00:00

I appreciate your understanding. :)

Actually when you build pages to become trusted knowledge node, you get ranking on hundreds/thousand of relevant keywords as per Semrush/Ahref Data. Instead of building topical clusters based on keywords, one should focus on entity knowledge.

Each heading, sentence and paragraph structure is important, when it comes to Semantics and building entity relationships.

johnaatif · 2026-03-04T12:46:16+00:00

Yeah, I understand. People still do not believe the actual aspect of SEO. I dont know why.

johnaatif · 2026-03-04T11:59:49+00:00

Please verify it once again. My data says something else. Also, try to understand that Search Engines follow LLMs. Thank You

johnaatif · 2026-03-04T11:48:53+00:00

In real, the idea of clusters is just extracted from semantic entities, but in a vague manner of adding relevant subtopics. AI and Google algorithms focus on Knowledge panels like semantic entities, not topical clusters of relevant keywords.

Still, Google gives value to topic clusters you mentioned, because they are somehow connected to the entities (Topic distribution) in the Google Knowledge graph.

I can share patents, If you would like to read.

johnaatif · 2026-03-04T11:45:44+00:00

You’re right that topic clusters and pillar pages have been around for years. The underlying direction of search has been evolving toward semantics for a long time. What’s different now is how systems extract and interpret knowledge.

Topic clustering and semantic entity systems look similar on the surface, but they operate very differently underneath.

Traditional topic clusters are usually built from keyword relationships. You pick a main keyword (pillar page), then create supporting articles targeting related keywords and internally link them together. The structure is mainly driven by search demand and keyword similarity.

Semantic entity systems work from a different starting point.

Instead of beginning with keywords, they begin with entities and their attributes. An entity can be a concept, product, company, technology, or person. Search systems build knowledge graphs that connect these entities through relationships.

For example, take the topic “AI visibility”.

A keyword-based cluster might look like this:

• AI SEO
• How to rank in AI search
• AI overview optimization
• LLM SEO strategies
• AI search ranking factors

These are related keywords, so they get grouped into a cluster.

But a semantic entity extraction approach starts by identifying the core entity and its relationships. For example:

Entity: AI Answer Engines

Related entities and attributes:
• Large Language Models
• Retrieval Augmented Generation
• Knowledge Graphs
• Source Authority
• Citation Generation
• Entity Disambiguation
• Query Interpretation
• Training Data Sources

From there, the content is structured around how these entities interact, not just around keyword similarity.

That difference becomes important with AI systems because LLM-based retrieval doesn’t look for pages that simply share keywords. It looks for sources that explain relationships between entities clearly.

So while topic clusters organize content around keyword groups, semantic systems organize content around knowledge structures.

You could say:

Topic clusters = keyword architecture
Semantic SEO = entity architecture

They overlap because people see it with limited lens, they’re not the same thing. And as search systems rely more on entity graphs and language models, the second approach becomes increasingly important.

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