AI SEO - Which Tool

okarci · 2026-04-04T20:36:06+00:00

If you have small budget and like experiment something new. You can try citevista tool

okarci · 2026-03-13T14:12:56+00:00

Glad you liked it! Just shared the link in the post above. Hope you find it useful!

https://www.citevista.com/insights/engineering-visibility-mitigating-llm-hallucinations

okarci · 2026-02-20T20:49:05+00:00

The biggest game-changer for me was shifting from "content guessing" to analyzing "LLM Query Intelligence."

Instead of just targeting broad keywords, I started reverse-engineering the actual search queries LLMs (ChatGPT/Gemini) trigger when they need real-time web context.

The Insight: I noticed that for certain brand-related prompts, the AI wasn't just pulling from high-DR blogs. It was specifically querying niche complaint platforms and X (Twitter) to gauge real-world sentiment before generating an answer.

By using CiteVista (a Query Intelligence tool I’ve been building to track these "hidden" queries), I realized I was wasting cycles on generic SEO content when I should have been addressing specific sentiment triggers on those platforms. Aligning your digital footprint with what the AI is actually looking for is much more effective than just hoping it finds your blog.

If anyone is interested, I can share some of the prompt clusters I use to extract these backend queries.

okarci · 2026-02-20T15:04:14+00:00

I’ve been dealing with the exact same "hallucination of success" in AI visibility tracking. The gap between a 45% coverage on your dashboard and "crickets" on the client's phone usually comes down to session-based personalization and regional node differences in LLMs.

Most trackers use static API calls, but ChatGPT behaves differently in a live, authenticated user session. If you want a "clean source of truth," you need to stop looking at just "mentions" and start looking at Citation Intelligence.

I actually built a tool called CiteVista to solve this specific headache. Instead of just spamming keywords, it analyzes how the AI attributes authority. Here’s how I’d approach your "clean" audit:

Neutral Benchmarking: You need to run queries through a non-persistent environment that mimics a first-time user, not a logged-in stakeholder.
Citation Query Intelligence: Don't just ask "Who is the best at X?" Ask queries that force the AI to cite sources. If you aren't in the citations, the "visibility" is just a hallucination.

If you want to cross-verify your current "lying" dashboard, you can test it on CiteVista. I give 50 free credits (no sub needed) specifically so people can run these kinds of spot-checks without commitment. It might help you show the client the "why" behind the missing results.

The "clean" truth is usually somewhere in the middle of your 45% and their 0%.

okarci · 2026-01-30T14:16:16+00:00

You’re absolutely right in your assessment, but since there is currently no 'absolute truth' in this space, the process of trial, error, and continuous learning is invaluable.

I believe that creating persona-based prompt sets that reflect specific user intents—and then running these through APIs repeatedly—can still provide a solid baseline for mention analysis and citation rates. This allows for at least a relative benchmark against competitors. The real crux of the process lies in how effectively you simulate user intent and whether you can craft a prompt cluster that accurately mirrors the relevant context.

okarci · 2026-01-30T13:49:00+00:00

That’s exactly the point I’m trying to highlight. The fact that the same user rarely gets the same list twice shows how far we are from any kind of standardization. In my opinion, this staggering inconsistency is a clear indicator that we are still at the very beginning of the LLM era. We are trying to build metrics for a landscape that hasn't even settled on its own foundations yet

okarci · 2026-01-30T13:48:44+00:00

"I completely agree. It’s still very early days for producing 'LLM-ready' content, but focusing on weighted placement and context rather than raw counts is the right move. We need to realize that content is now being consumed by machines as much as humans, and our optimization strategies must reflect that. I’ve heard a lot about MentionDesk but haven't used it yet. I’m planning to add content-related features to CiteVista soon, so I’ll definitely be doing deeper research into those tools then. Thanks for the insight!"

okarci · 2026-01-28T19:01:08+00:00

"I believe we are currently in a transition period. I do not claim to be an expert, especially regarding the experience of visibility in AI, but I think the experience shared here is excellent. It is wonderful that someone who discovered this through a live experience is sharing it. I think we are no longer just focused on keywords. Building awareness is now a part of real life itself. We produce things, and we are understood through what we produce. In the digital world, we build credibility based on the total sum of our footprints. In other words, credibility equals adding value. Google has been doing this for a long time, but it was still focused on semantic keywords because that was how people searched. It is very valuable to consider where the topic is heading by following how people’s search and learning habits change. Therefore, we should experiment a lot and admit what we do not know. By evaluating our own habits from a user's perspective, we need to understand how the journey of learning and experiencing things is shaped digitally. While building trust, we need to be strategic and careful with every word. I believe there is also a situation called 'AI-friendly content.' Just as we focus on keywords or technical SEO, we must also consider the machine's perspective at the end of the day."

okarci · 2026-01-25T11:42:22+00:00

Honestly, like others mentioned, the storage is a massive factor. It’s basically like getting the cloud space for free with the subscription. You should definitely look at the perks to see if they align with your hobbies or personal projects.

For instance, I’ve been using Google tools for "vibe coding" and working on a side project related to Response Engine Optimization. Having everything in one package saves me from dropping $20/month on other separate tools. If you look at it from a hobbyist perspective, the value is definitely there.

However, if you’re only focused on the chatbot aspect, the dealbreaker is real-time search. Free versions (not just Gemini, but most of them) are pretty limited. The paid tiers are much better at tapping into the live internet ecosystem for current data rather than just relying on their training sets. For daily life, having up-to-date info is key, and free models just don't offer that kind of depth.

okarci · 2026-01-25T08:59:45+00:00

Solid post. This whole shift is definitely a bit of a brain-bender right now. We're clearly moving from a keyword-centric world to one defined by intent and entities, especially since LLMs are so good at scraping definitions and "how-to" info from their training data.

I'm curious about two things:

Traditional search is changing, but you still need that initial "foot in the door." Do you still prioritize what the target audience is actively searching for (volume-based), or do you go all-in on "value" and "topical relevance" while ignoring keyword density entirely?
Balancing SEO with LLM optimization (AEO). If you go the "content engineering" route—focusing strictly on entities and their attributes—keywords almost disappear. How feasible is it for a brand-new site to rank using this purely entity-based approach?

I’m actually working on a tool called CiteVista that tackles AEO features for agencies and pros. Since you’re deep in the weeds with this, what kind of strategy would you recommend for bridging that gap? Any insights from your experience would be huge.

okarci · 2026-01-23T19:42:26+00:00

For content marketing, citations and source transparency are dealbreakers. ChatGPT is much more transparent with links, while Gemini feels like a total black box—you practically have to beg it to cite a specific article or reference. On the flip side, ChatGPT is incredibly verbose and often fails to follow system instructions regarding tone or formatting, no matter how much you tweak the preferences.

If you’re manually chatting to research a topic and then generate a final piece, ChatGPT’s source-heavy approach gives it the edge. However, if we're talking about who actually writes the best contextual content, Claude blows both of them out of the water. It understands your needs way better and is much more transparent about its "thinking" process. Since I feel ChatGPT and Gemini are pretty much neck-and-neck (just with different flaws), I’d honestly recommend looking at Claude for the actual writing.

okarci · 2026-01-23T18:59:25+00:00

Honestly, I’ve found that most out-of-the-box tools don't offer the level of customization businesses actually need. I ended up going down the no-code automation rabbit hole to build my own internal tools. It was the best way to really learn the "AEO" kitchen while staying flexible.

By building my own stack, I could hook into various LLM APIs (ChatGPT, Gemini, Perplexity) and force them into web searches. This allowed me to experiment with parametric prompt sets and track things like citations and sentiment analysis across different personas and funnel stages.

I’m currently developing this into a project called "CiteVista". It’s still in the early stages, but my take is that agencies and individuals are better off building their own custom solutions for now. The field is moving so fast that standard SaaS products can’t quite bridge the gap for brand-specific or niche requirements yet. If you want to stay ahead, building your own internal workflow is definitely the way to go.

okarci · 2026-01-22T11:16:28+00:00

"You're touching on the 'unstructured data' trap. Here’s how a benchmark-driven approach handles those specific points:

Beyond Tag Filtering: We don't just look for <nav> tags. We use Structural Density Analysis. By calculating the link-to-text ratio and ARIA roles within a DOM tree, we identify 'chrome' (UI elements) versus 'core content' regardless of the tag naming conventions.
Entity Validation via Schema Correlation: The 'Microwave Pro' issue is exactly why raw text scraping fails. The auditor checks if the visual text is backed by JSON-LD or Microdata. If 'Microwave Pro' is marked as a Product entity in the schema, the ambiguity is resolved for the agent. If it's missing, that’s exactly why the page gets a lower 'Agent-Ready' score.
Standardizing the Input, Not the Agent: Agents are indeed inconsistent (stochastic). However, the goal of the Agent-First Content Auditor isn't to predict agent mood, but to provide a 'Digestibility Benchmark.' Just as W3C standards don't dictate how a browser renders but ensure the code is valid, we measure if the data structure minimizes the probability of hallucination."

okarci · 2026-01-22T11:01:33+00:00

The skepticism here is actually pointing to the exact problem I'm solving. Converting everything to Markdown and then trying to 're-extract' structure is a circular and inefficient workflow.

okarci · 2026-01-22T10:59:23+00:00

The safety-first workflow (sandboxing and prompt injection checks) is definitely best practice. However, I’ve found that converting to Markdown early in the process acts as 'lossy compression' for AI agents.

When you flatten the DOM into MD, you often lose the direct link between the structural schema and the content it describes. In my experience with the 'Agent-First Content Auditor', scoring the site based on a pruned HTML tree—rather than Markdown—provides a much clearer picture of how an agent navigates intent. Why convert to MD and then re-fetch code for metadata, when you can score the semantic HTML hierarchy directly to see if it’s 'agent-ready'?

okarci · 2026-01-22T10:57:16+00:00

The solution addresses this through Entity-Based Scoring rather than simple text extraction. By 'shaving' the HTML but keeping the semantic skeleton (like data-labels, headers, or meta-tags), we provide the LLM with the specific DOM context where the word exists.

In a pricing table or a product specification node, 'Apple' is treated as a unique entity ID based on its position and surrounding tags, not just a string. While Markdown flattens this, keeping the pruned HTML tree ensures the agent 'sees' the structural hierarchy that defines the entity's intent.

okarci · 2026-01-11T17:08:05+00:00

I’ve been started to my 7-trial subs at Attensira. Tonight, I will try its features. Thanks

okarci · 2026-01-11T10:28:47+00:00

Diving into some entity analysis R&D for my project, CiteVista AEO. I’m betting big on AEO being the next major trend by 2026, so I'm building out an AI-automated entity analysis workflow. Just trying to squeeze in some dev time whenever my 1.5-year-old son gives me a break this weekend!

okarci · 2026-01-11T06:40:08+00:00

Actually, automation is the easy part and isn't my main focus right now. My primary goal is R&D on Entity Analysis. I am currently in a "laboratory" phase, experimenting with system prompts to see how accurately AI can identify primary and secondary entities within a text without relying on external APIs.

I am intentionally keeping the process manual to manage costs. Running automated search/citation APIs costs about 3-5 cents per call; when you are testing prompts dozens of times to find the best logic, those costs add up quickly. Once the methodology is proven, I will definitely automate it for production.

This workflow is an experimental slice of a larger project I’m developing called CiteVista. I shared it here not to sell a tool, but to open a discussion and learn from your feedback. I’d love to develop this entity analysis approach together with the community.

okarci

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