SpyderBot has been recognized among the first set of winners in HackerNoon’s Proof of Usefulness Hackathon, marking an important milestone for the company as it continues to build analytics infrastructure for the AI Search era.
The official announcement was published by HackerNoon under the title “Proof of Usefulness Hackathon: First Set of Winners Announced.”
The Proof of Usefulness Hackathon is organized by HackerNoon and supported by Bright Data, Neo4j, Storyblok, and Algolia. The program recognizes software projects that demonstrate practical usefulness, real-world value, and measurable relevance beyond pitch deck promises.
SpyderBot was recognized under the Bright Data Awards category, reflecting the platform’s focus on GEO analytics, AI visibility, and LLM brand monitoring.
A Recognition Focused on Real-World Utility
The Proof of Usefulness Hackathon is built around a simple but important idea: useful products should solve real problems for real users.
In a technology landscape where many products are judged by vision, presentation, or early-stage hype, HackerNoon’s Proof of Usefulness framework places emphasis on practical value. It asks whether a product works, whether it addresses a real need, and whether it can create meaningful value for users.
For SpyderBot, this recognition is significant because it aligns directly with the problem the company is trying to solve.
Search behavior is changing. Users are no longer relying only on traditional search engines and blue links. Increasingly, they are asking AI systems for recommendations, comparisons, summaries, and vendor suggestions.
That shift creates a new visibility challenge for brands.
A company may rank on Google, but still be absent from AI-generated answers.
A brand may have strong website content, but still be misunderstood or underrepresented by large language models.
A competitor may appear more often in AI recommendations, even when another brand has stronger expertise, better positioning, or a more relevant product.
SpyderBot was built to help companies understand and monitor this new layer of visibility.
What SpyderBot Does
SpyderBot is a GEO analytics platform designed to help businesses track how AI systems understand, mention, and compare brands across generative search environments.
The platform helps teams monitor AI brand visibility, LLM mentions, competitor presence, prompt-level performance, sentiment, and how different AI models describe a brand across multiple contexts.
This includes visibility across AI systems such as ChatGPT, Gemini, Grok, Claude, Copilot, Perplexity, and other large language models.
At its core, SpyderBot helps brands answer two increasingly important questions:
What do LLMs mention about your competitors to users?
And how are LLMs analyzing and tracking your website?
These questions are becoming critical as AI-generated answers begin to influence how users discover products, evaluate companies, and make decisions.
Why AI Search Requires a New Measurement Layer
Traditional SEO has long focused on rankings, backlinks, organic traffic, and keyword visibility. These metrics remain important, but they no longer provide a complete picture of brand visibility.
In traditional search, a user sees a list of results and chooses which page to visit.
In AI Search, the answer is often generated directly. The AI system may summarize a market, recommend a short list of brands, compare competitors, or explain which solution best fits the user’s intent.
This means brands are no longer competing only for rankings. They are competing to be included, understood, and recommended inside AI-generated responses.
That is where Generative Engine Optimization, or GEO, becomes important.
While SEO focuses on search engine rankings, GEO focuses on how brands appear inside generative AI answers. It looks at whether a brand is mentioned, how it is described, what context surrounds the mention, which competitors appear nearby, and whether the brand’s positioning is accurately represented.
SpyderBot focuses on this emerging data layer, helping marketing, SEO, growth, and brand teams monitor their presence in AI-generated discovery journeys.
Supported by a Strong Technology Ecosystem
The Proof of Usefulness Hackathon is supported by Bright Data, Neo4j, Storyblok, and Algolia, bringing together important areas of the modern technology stack, including data infrastructure, graph technology, content architecture, and search experience.
This broader ecosystem makes the recognition especially relevant for companies building at the intersection of data, AI, and product usefulness.
SpyderBot’s recognition under the Bright Data Awards category reflects the growing importance of real-world data and AI-driven analytics in understanding how brands appear across generative systems.
As more users turn to AI tools for discovery and decision-making, brands will need more reliable ways to measure how they are represented across these systems.
A Milestone, But Only the Beginning
For SpyderBot, this recognition from HackerNoon is both a milestone and a starting point.
The company will continue developing its platform with a focus on practical insights, clearer analytics, and better support for brands entering the AI Search era.
SpyderBot’s goal is not only to help companies monitor mentions. It aims to help brands understand how AI systems interpret their identity, compare them against competitors, and surface them in response to real user questions.
The team also looks forward to continued trust, feedback, and support from users, partners, and businesses exploring GEO, AI visibility, and LLM brand monitoring.
The Bigger Signal for Brands
SpyderBot’s recognition in HackerNoon’s Proof of Usefulness Hackathon points to a broader shift in digital visibility.
Brands no longer need to focus only on being indexed by search engines. They also need to be understood by AI systems.
They no longer need to measure only where they rank. They also need to measure whether they are mentioned, how they are framed, and which competitors appear more often in AI-generated answers.
In the AI Search era, visibility is no longer only about traffic.
It is about being present in the answers that shape user decisions.
For SpyderBot, this milestone reinforces the importance of building tools for that future.
LLM brand mentions are the ways large language models such as ChatGPT, Gemini, Claude, Copilot, Grok, and Perplexity include, describe, compare, and recommend brands in generated answers.
This includes:
Whether a brand is mentioned
How often the brand appears
Which prompts trigger the mention
How the brand is described
Whether the brand is recommended or only listed
Which competitors appear alongside it
Whether the brand is framed positively, neutrally, or weakly
In traditional search, brands compete for rankings.
In AI-generated answers, brands compete for inclusion.
That is why LLM brand mentions are becoming an important part of AI visibility and Generative Engine Optimization.
II. Why LLM brand mentions matter
LLM brand mentions matter because AI systems increasingly influence how users discover products, compare companies, and make decisions.
In traditional search, users see multiple links and decide what to click.
In AI systems, users often receive a synthesized answer.
That means the AI system may decide which brands are worth mentioning before the user visits any website.
If your brand is not mentioned, you may be invisible at the decision stage.
If your brand is mentioned poorly, users may misunderstand your positioning.
If your brand is mentioned strongly, you can influence decisions before the click.
III. LLM brand mentions vs SEO visibility
LLM brand mentions are different from SEO rankings.
SEO visibility
LLM brand mentions
Based on rankings
Based on inclusion
Focuses on pages
Focuses on entities
Measures traffic
Measures AI visibility
Uses keywords
Uses context and meaning
Competes on SERPs
Competes inside answers
SEO asks:
Where do we rank?
LLM visibility asks:
Are we included in the answer?
This is a major shift.
A company can rank well on Google but still be missing from ChatGPT answers.
IV. The 4 dimensions of LLM brand mentions
To understand LLM brand mentions properly, companies should analyze four dimensions:
Inclusion
Frequency
Context
Framing
Together, these dimensions show whether a brand is visible, how often it appears, when it appears, and how AI systems position it.
V. Inclusion: is your brand mentioned at all?
Inclusion is the most basic layer of LLM brand visibility.
It answers:
Does your brand appear in AI-generated answers?
Key questions include:
Is the brand mentioned in relevant prompts?
Does it appear when users ask for recommendations?
Does it appear in comparison prompts?
Does it appear in problem-based prompts?
Is it included alongside competitors?
If the brand is not included, it has no AI visibility in that context.
No inclusion means no presence in the AI-generated decision layer.
VI. Frequency: how often does your brand appear?
Frequency measures how consistently a brand appears across relevant prompts.
It answers:
How often does AI mention the brand?
Useful metrics include:
Mention rate
Mention share
Prompt coverage
Competitor mention comparison
Visibility consistency across AI systems
A brand mentioned once is not necessarily strong.
A brand mentioned consistently across different prompts, categories, and use cases has stronger AI visibility.
VII. Context: when does AI mention your brand?
Context explains the situations where a brand appears.
It answers:
In what kinds of questions does AI include the brand?
Examples of useful contexts include:
Best tools for a category
Alternatives to a competitor
Product comparisons
Use-case recommendations
Industry-specific solutions
Problem-solving prompts
Buying decision prompts
Context matters because not all mentions are equally valuable.
A brand appearing in irrelevant contexts may not drive meaningful visibility.
A brand appearing in high-intent recommendation prompts is more valuable.
VIII. Framing: how does AI describe your brand?
Framing is one of the most important parts of LLM brand mentions.
It answers:
How does AI position the brand?
AI may frame a brand as:
A market leader
A niche solution
A beginner-friendly option
A technical platform
A budget alternative
A premium solution
A competitor to another brand
A less complete option
Framing influences perception.
Being mentioned is not enough.
The way AI describes the brand can shape whether users trust it, ignore it, or compare it seriously.
IX. The LLM Brand Mention Model
A simple way to understand AI brand visibility is:
For more than two decades, SEO has been the default language of digital visibility.
If your website ranked high on Google, you had a chance to be discovered. If your content matched the right keywords, earned backlinks, and satisfied search intent, your brand could win traffic.
But now users are not only searching.
They are asking.
They ask ChatGPT, Gemini, Claude, Perplexity, Copilot, and Google AI Overviews questions such as:
“What is the best software for my business?”
“Which brand should I choose?”
“What are the top tools in this category?”
“Is this company trustworthy?”
And instead of showing a traditional list of blue links, AI systems generate direct answers.
That creates a new question for every marketer, founder, SEO team, and brand owner:
Is SEO still relevant for ChatGPT?
The answer is yes.
But not in the way most people think.
SEO still matters. It is still part of the visibility system. It still helps your content become discoverable, structured, and accessible.
But SEO alone is no longer enough to guarantee visibility in AI-generated answers.
In the AI search era, the goal is no longer only to rank.
The goal is to be understood, selected, mentioned, and correctly represented.
That is where traditional SEO ends, and AI visibility begins.
I. Why People Think SEO Should Work the Same Way in ChatGPT
Most people assume SEO should automatically work for ChatGPT because they still think of ChatGPT as another search engine.
That assumption is understandable.
ChatGPT can now search the web and provide answers with links to relevant sources, according to OpenAI’s official ChatGPT Search documentation. OpenAI also explains that ChatGPT can use online sources such as news or search results when creating informed responses.
Google also provides official guidance for how AI features such as AI Overviews and AI Mode work from a website owner’s perspective.
So yes, there is overlap between search engines and AI systems.
But they are not the same.
Google Search traditionally works like this:
It crawls pages
It indexes content
It ranks URLs
It shows a list of results
The user chooses what to click
ChatGPT works differently.
It may retrieve information, but the final output is not a search result page. It is a generated answer. It synthesizes information, interprets context, and may choose which brands, entities, products, or sources to include.
That difference is critical.
Google ranks pages.
ChatGPT selects answers.
Google gives users options.
ChatGPT often compresses options into a recommendation.
Google visibility is page-level.
ChatGPT visibility is often brand-level, entity-level, and context-level.
This means SEO can help you enter the information ecosystem, but it does not fully control whether ChatGPT will mention your brand.
That is the core shift.
II. SEO Is Still Relevant, But It Has Become an Input Layer
SEO is not dead.
That idea is lazy and inaccurate.
SEO still matters because AI systems are influenced by the broader web. Your content, documentation, reviews, citations, brand mentions, and structured information all contribute to how your brand is understood online.
The real issue is this:
SEO is now an input layer, not the final visibility layer.
In traditional search, SEO could directly influence rankings.
In AI search, SEO contributes to the data environment that AI systems may use, but the final answer depends on more than keyword position.
SEO still helps with several important things.
First, SEO improves content discoverability. If your website is not crawlable, not indexable, not structured, or not clear, you are weakening the foundation that AI systems may rely on.
Second, SEO helps build topical authority. A brand with detailed, consistent, and high-quality content across its category has a stronger chance of being interpreted correctly.
Third, SEO supports source availability. Retrieval-based AI experiences, such as ChatGPT Search or Google AI features, may use online sources to support answers. If your content cannot be found, it cannot easily contribute to those responses.
Fourth, SEO improves technical hygiene. Clean site structure, schema markup, fast loading, internal linking, and strong content architecture still matter.
But SEO has a limit.
It can make your content available.
It cannot guarantee that ChatGPT will select your brand.
That is why companies can rank well on Google but still fail to appear in AI-generated recommendations.
III. Where SEO Fails in ChatGPT
The biggest mistake brands make is assuming that Google ranking equals ChatGPT visibility.
It does not.
A company can rank in the top five for important keywords and still be invisible in ChatGPT answers.
Why?
Because ChatGPT does not behave like a traditional SERP.
There is no fixed position number one.
There is no standard list of ten blue links.
There is no guaranteed traffic loop.
There is no keyword-only matching system.
There is no simple equation where higher ranking means more AI mentions.
AI systems work with meaning, context, entity relationships, and source patterns. They evaluate how a brand is represented across many signals, not just whether one landing page ranks for one keyword.
This creates four common SEO failure points in ChatGPT.
1. SEO optimizes pages, but AI often selects brands
A page can rank well, but ChatGPT may still not understand the brand behind it clearly.
For AI visibility, your brand needs to be recognized as an entity.
That means the system should understand:
Who you are
What category you belong to
What problem you solve
Who you serve
How you compare to alternatives
Why you are relevant to a specific prompt
If that entity layer is weak, page-level SEO may not be enough.
2. SEO targets keywords, but AI interprets intent
Traditional SEO often starts with keywords.
AI search starts with prompts.
A user may not ask:
“best GEO analytics platform”
They may ask:
“Why does ChatGPT recommend my competitor instead of my company?”
That is a different search behavior.
The user is not typing a keyword. They are expressing a business problem.
This is why AI visibility requires prompt-level thinking, not only keyword-level thinking.
3. SEO measures traffic, but AI shapes decisions before the click
In AI search, the user may receive a complete answer before visiting any website.
That means brand perception can be shaped without a click.
If ChatGPT says your competitor is a leading option, the user may trust that framing. If your brand is missing, the user may never know you exist.
This changes the role of visibility.
The question is no longer only:
“How many users visited our website?”
The better question is:
“Did AI include us when buyers asked for recommendations?”
4. SEO focuses on owned content, but AI relies heavily on broader signals
Your website matters, but it is not the only source of truth.
AI systems may be influenced by:
Review platforms
Third-party articles
Comparison pages
SaaS directories
Public reports
Documentation
Forum discussions
News coverage
Analyst content
Brand mentions across the open web
This is why a competitor with stronger third-party presence can appear more often in AI answers, even if your website is technically optimized.
IV. The New Layer: AI Visibility
To understand ChatGPT visibility, brands need a new concept:
AI visibility.
AI visibility is the degree to which your brand is recognized, understood, selected, mentioned, and accurately represented in AI-generated answers.
It is different from SEO visibility.
SEO visibility asks:
“Where does my page rank?”
AI visibility asks:
“How does AI understand and present my brand?”
This distinction matters because AI visibility is not only about being found. It is about being selected.
A brand with strong AI visibility is more likely to appear when users ask:
What is the best tool for this problem?
Which companies are leaders in this category?
What are the best alternatives to this product?
Which service should I use for my business?
What are the pros and cons of this brand?
Which brand is most trusted in this market?
The attached draft already identifies this shift correctly: SEO is still important, but it is no longer sufficient because ChatGPT does not simply rank websites. It decides whether a brand should be included in an answer.
That is the right foundation.
But the stronger version is this:
SEO gets your content into the ecosystem. AI visibility determines whether your brand enters the answer.
V. GEO vs SEO: What Actually Changes?
Generative Engine Optimization, or GEO, is the practice of improving how generative AI systems understand, cite, mention, and represent your brand or content.
The academic paper “GEO: Generative Engine Optimization” describes GEO as a creator-centric framework for optimizing content visibility in generative engine responses. The paper also reports that GEO methods improved visibility by up to 40% in their tested generative engine settings.
This does not mean GEO replaces SEO.
It means GEO expands SEO into a new visibility environment.
Here is the practical difference:
Traditional SEO
AI Visibility / GEO
Rankings
Mentions
Keywords
Entities
Pages
Brands
SERPs
Generated answers
Clicks
Consideration
Backlinks
Source authority
Search intent
Prompt intent
Organic traffic
AI recommendation presence
SEO asks:
“How do we rank higher?”
GEO asks:
“How do we become a trusted answer?”
SEO optimizes for search engines.
GEO optimizes for generative engines.
SEO improves discoverability.
GEO improves inclusion, interpretation, and recommendation.
Both matter.
But they solve different layers of the modern search journey.
VI. A Realistic Example
Imagine a SaaS company that sells project management software.
The company has:
Good blog content
Strong technical SEO
Several pages ranking on Google
A healthy backlink profile
Decent organic traffic
From a traditional SEO perspective, the brand looks healthy.
But when users ask ChatGPT:
“What are the best project management tools for remote teams?”
The brand does not appear.
Instead, ChatGPT mentions competitors.
Why?
Possible reasons include:
Competitors are mentioned more often in third-party lists
Competitors have stronger review coverage
The brand category is unclear
The website does not explain use cases clearly
The brand lacks comparison content
There are weak public associations between the brand and the target problem
AI systems do not have enough confidence to include the brand
This is not an SEO failure in the old sense.
It is an AI visibility gap.
The company is visible to Google but not visible enough to AI decision systems.
That is the new problem.
VII. What Companies Should Do Instead
The wrong response is to say:
“We just need more SEO.”
More blog posts may help.
More backlinks may help.
Better technical SEO may help.
But if the underlying problem is weak AI interpretation, then traditional SEO alone will not fix it.
Companies need to add a GEO layer on top of SEO.
1. Keep the SEO foundation strong
Do not abandon SEO.
Make sure your website is:
Crawlable
Indexable
Fast
Structured
Internally linked
Clear in its category
Supported by strong content
Built around real user intent
Google’s own guidance for AI features emphasizes that site owners should continue focusing on helpful, unique, satisfying content as Search evolves into AI experiences.
That means SEO best practices still matter.
But they are the foundation, not the whole strategy.
2. Strengthen entity clarity
Your brand should be easy for AI systems to understand.
Make your website clearly answer:
What is your company?
What category are you in?
What problems do you solve?
Who is your product for?
What makes you different?
What alternatives are you compared against?
What proof supports your claims?
Vague positioning weakens AI visibility.
Clear entity structure strengthens it.
3. Build prompt-based content
Do not only optimize for keywords.
Optimize for the questions buyers actually ask AI tools.
Examples:
“Why is my brand not mentioned in ChatGPT?”
“How do I get my company recommended by AI?”
“What are the best tools for AI brand monitoring?”
“How do LLMs choose which brands to mention?”
“How do I track brand mentions in ChatGPT?”
“What is the difference between SEO and GEO?”
These themes match high-intent GEO and AI visibility keyword groups such as “why ChatGPT not mentioning my brand,” “how to appear in AI search results,” “LLM visibility tracking tool,” and “AI brand mention tracking.”
4. Improve third-party validation
AI systems do not rely only on your own claims.
You need credible external signals.
That can include:
Review platforms
Industry directories
Expert mentions
Comparison articles
Case studies
Product documentation
Public reports
Interviews
Thought leadership
Community discussions
The more consistent your brand is across reliable sources, the easier it becomes for AI systems to understand and trust your positioning.
5. Track AI mentions directly
This is the step most companies still miss.
They track rankings.
They track backlinks.
They track traffic.
But they do not track whether ChatGPT, Gemini, Claude, Perplexity, Grok, or Copilot actually mention their brand.
That creates a blind spot.
You cannot optimize what you cannot observe.
VIII. Where SpyderBot Fits
SpyderBot is built for this new visibility layer.
It helps brands understand how AI systems interpret, mention, compare, and represent them across major LLMs and AI search platforms.
SpyderBot tracks AI visibility across systems such as ChatGPT, Grok, Gemini, Copilot, Perplexity, Llama, Claude, and other LLMs. Its platform focuses on mention visibility, sentiment analysis, ranking performance, competitor comparison, prompt insights, ecommerce mentions, founder and investment signals, bot traffic, and LLM referrals.
That matters because AI visibility is not something teams should measure manually with one or two prompts.
Manual testing is inconsistent.
One prompt is not a strategy.
One screenshot is not a report.
One ChatGPT answer is not enough evidence.
A brand needs to know:
When it appears
When it disappears
Which competitors are mentioned instead
Which prompts trigger visibility
Which AI systems understand the brand correctly
Which systems misclassify the brand
Which sources may influence the answer
Whether brand sentiment is positive, neutral, or negative
This is where SpyderBot helps shift AI visibility from guessing to measurement.
It gives brands a practical way to answer a question that traditional SEO tools were not designed to answer:
How do AI systems see us compared with our competitors?
IX. The Future: From Search Rankings to AI Representation
The search journey is changing.
Users are moving from keywords to prompts.
Search engines are moving from links to answers.
Visibility is moving from rankings to mentions.
Competition is moving from page-level SEO to brand-level representation.
This does not make SEO irrelevant.
It makes SEO incomplete.
The future of digital visibility will likely require both:
SEO for discoverability.
GEO for AI inclusion.
SEO helps your content become available.
GEO helps your brand become selectable.
SEO helps search engines find your pages.
GEO helps AI systems understand why your brand belongs in the answer.
This is the strategic shift every brand needs to understand.
Final Conclusion
So, is SEO relevant for ChatGPT?
Yes.
But SEO is no longer enough.
SEO helps your content enter the digital ecosystem, but ChatGPT visibility depends on whether AI systems understand, trust, and select your brand.
The old game was:
Search engine optimization → rankings → traffic
The new game is:
SEO → data layer → AI interpretation → generated answers → brand consideration
That is why brands need to move beyond only asking:
“Are we ranking?”
They need to ask:
“Are we being mentioned?”
“Are we being recommended?”
“Are we being represented correctly?”
“Are competitors appearing where we should be?”
SEO still gets you into the system.
But GEO determines whether you are selected.
And in AI-powered search, selection is the new visibility.
