Online discovery is changing faster than many brands expected. For years, companies focused on search rankings, social media reach, paid ads, marketplaces, and the design of their own websites. Those channels still matter, but a new layer is quickly forming between brands and customers. That layer is made of AI agents, chatbots, large language models, answer engines, and shopping assistants that can read the web, compare options, summarize choices, and recommend products before a customer ever lands on a website.
This is the shift Aviv Shamny is building for with Limy AI.
As Co-Founder and CEO of Limy AI, Aviv Shamny is focused on a problem that is becoming more urgent for brands: how do companies stay visible when AI systems begin acting as the new gatekeepers of online discovery? In the traditional web, brands could track clicks, pageviews, sessions, referral traffic, and search rankings. In the agentic web, an AI agent may visit a product page, collect information, compare it with competitors, and influence a buying decision without behaving like a normal human visitor.
Limy AI is being built to make that hidden activity visible. Its goal is to help brands understand how AI agents interact with their websites, what information they collect, which prompts lead to their products, and how AI-driven discovery connects to real business outcomes. For Aviv Shamny, this is not just a technical analytics problem. It is part of a much larger change in how brands will compete online.
Who is Aviv Shamny
Aviv Shamny is the Co-Founder and CEO of Limy AI, a company working at the intersection of AI search, agentic commerce, brand visibility, and marketing attribution. His role places him in the middle of one of the most important questions facing digital businesses today: what happens when people stop browsing the internet in the old way and start asking AI systems to do more of the discovery for them?
Shamny’s background as a founder and builder gives him a practical angle on the problem. Limy AI is not positioned as a simple dashboard for checking whether a brand appears in AI answers. It is aiming to go deeper by looking at how agents behave, what they fetch, and how those interactions can be tied back to prompts, influence, and revenue.
That focus matters because AI visibility is becoming harder to understand with old marketing tools. A brand may have strong SEO, good product pages, and a familiar name, yet still struggle to know whether AI tools are reading the right content or recommending the right products. Aviv Shamny is building Limy AI around that gap.
What Limy AI is building
Limy AI helps brands understand and improve their visibility in the agentic web. In simple terms, it gives companies a clearer view of how AI agents interact with their online presence.
AI agents do not browse like people. They may not scroll, compare product photos, read reviews in the same order, or follow the path a normal customer would take. Instead, they can fetch content, process structured and unstructured information, evaluate product details, and use that information to answer a user’s prompt. For brands, that creates a new challenge. If an AI agent is visiting a site and using its information to shape a recommendation, the brand needs to know what the agent saw and how that information was interpreted.
Limy AI is building infrastructure for that moment. The platform is designed to help brands detect agent activity, understand what content AI systems are using, and connect that activity to business performance. This can include learning which prompts bring a product into view, which pages AI agents are pulling from, and whether that activity contributes to conversions.
This is a different way of thinking about digital visibility. Traditional SEO focuses heavily on ranking for keywords. Paid media focuses on audiences, targeting, and cost per acquisition. Web analytics focuses on human sessions and traffic sources. Limy AI is focused on the layer where AI agents become part of the customer journey.
Why the agentic web matters for brands
The agentic web describes an internet where AI systems do more than answer questions. They can act on behalf of users. They may compare brands, narrow down product options, summarize reviews, recommend vendors, book services, or guide purchasing decisions.
For customers, this can feel convenient. Instead of opening ten tabs and reading product pages one by one, a person can ask an AI assistant for the best running shoes for flat feet, the most reliable family SUV, the safest baby monitor, or the best software for a small business. The AI system then decides which sources to trust, what products to mention, and which details deserve attention.
For brands, this changes the rules of discovery.
A company may no longer be competing only for a blue link on Google or a placement on a marketplace page. It may be competing for a spot inside an AI-generated answer. If the AI system recommends three products and a brand is not one of them, the customer may never know that brand exists. If the AI misunderstands the product, uses outdated information, or pulls details from a weak source, the brand may lose visibility without seeing the full reason.
That is why Aviv Shamny’s work with Limy AI is timely. Brands need to understand not only how people see them online, but also how machines interpret them.
The problem Aviv Shamny is trying to solve
Most marketing teams are used to measuring activity that comes from people. They can see when someone clicks an ad, visits a landing page, opens an email, adds a product to cart, or completes a purchase. But AI agents introduce a new type of traffic and a new type of influence.
An agent may visit a website because a user asked a chatbot for a recommendation. The agent may collect product descriptions, pricing information, specifications, reviews, availability, shipping details, or brand signals. Then it may return to the user with a short answer that includes only a few options.
The brand might never see the full journey. It may not know which prompt triggered the visit. It may not know which content the agent used. It may not know whether the AI system compared the brand with competitors. It may not know why it was recommended in one answer but ignored in another.
This is the visibility gap Limy AI is trying to close.
Aviv Shamny’s approach starts with a simple but powerful idea: brands cannot improve what they cannot see. If AI agents are becoming a new audience for online content, then brands need a way to understand that audience. They need to know what agents are reading, what agents are missing, and how agent-driven discovery affects commercial results.
How Limy AI helps brands win AI visibility
Limy AI’s value is rooted in making AI agent activity measurable. That means moving beyond guesses and giving brands clearer signals about what is happening when AI systems interact with their sites.
Measuring AI agent activity
The first step is helping brands identify when AI agents are visiting their websites. This matters because agent traffic can look different from normal user traffic. Without the right infrastructure, brands may miss those visits or misunderstand what they represent.
For ecommerce teams, this can become especially important. A product may be seen by an AI agent before it is ever seen by a human customer. If that agent later recommends the product, the brand needs a way to connect the dots.
Understanding what AI agents fetch
It is not enough to know that an agent visited a website. Brands also need to understand what the agent collected. Did it pull from a product page, a comparison article, a help center, a review section, or a category page? Did it see updated pricing? Did it find the product’s strongest selling points? Did it understand the difference between similar products?
These details can shape whether a brand is recommended, ignored, or described incorrectly. Limy AI is built around the idea that brands need visibility into these agent-level interactions.
Connecting prompts to outcomes
One of the more interesting parts of Limy AI’s positioning is the connection between prompts and business outcomes. In the AI search era, a prompt can work like a new kind of query. A customer might ask, “What is the best skincare brand for sensitive skin?” or “Which project management tool is best for a remote startup?” Those prompts can influence discovery in the same way keywords once did, but they are more conversational and more intent-driven.
If brands can understand which prompts surface their products, they can learn where they are strong and where they are missing. They can also see how AI-driven recommendations may connect to sales, signups, leads, or other conversions.
Improving content for AI recommendations
AI visibility is not only about being mentioned. It is about being understood correctly. Brands may need clearer product descriptions, better structured content, stronger comparison pages, cleaner technical signals, updated information, and more trustworthy supporting content.
Limy AI gives brands a way to see where their content may be helping or hurting them in AI-driven discovery. This can help marketing teams update product pages, improve content structure, clarify use cases, and make their strongest proof points easier for AI systems to interpret.
Moving from SEO to AI visibility
SEO is not disappearing, but it is expanding. Brands still need useful content, strong authority, technical health, and clear information architecture. What is changing is the audience. Content now needs to serve human readers, search engines, and AI agents.
This is where Limy AI fits into the market. It gives brands a new layer of intelligence for a web where recommendations may be shaped by large language models and autonomous agents.
Aviv Shamny’s bigger vision for agentic commerce
The rise of AI agents could reshape ecommerce, travel, financial services, software buying, healthcare discovery, and many other categories. When users rely on AI assistants to compare options, brands will need to compete for visibility inside those AI-led journeys.
Aviv Shamny’s bigger vision for Limy AI is tied to this new form of commerce. Instead of thinking only in terms of business-to-consumer or business-to-business, brands may also need to think about business-to-agent. In that model, AI agents become an important audience because they help decide what customers see, compare, and trust.
This does not mean brands should write only for machines. It means they need to make their product information accurate, accessible, structured, and credible enough for AI systems to use properly. The brands that do this early may have an advantage as the agentic web becomes more common.
Limy AI is trying to build the infrastructure that makes this possible. It helps brands see where they stand, understand how agents interact with their content, and improve the signals that influence AI recommendations.
Why Limy AI’s funding matters
Limy AI’s $10 million seed funding is an important milestone because it shows investor confidence in the agentic web category. The round was led by Flybridge, with participation from a16z speedrun and other investors. For a young company working in a new market, that funding gives Limy AI room to expand the platform, grow its team, and work with more brands trying to understand AI-driven discovery.
The timing also matters. AI adoption is no longer limited to early adopters. Consumers are using AI tools to search, compare, plan, write, shop, and make decisions. Businesses are also adopting AI agents for research, procurement, support, and workflow automation. As these habits grow, brands will need better tools to understand how AI systems see them.
Funding also helps Limy AI educate the market. Many brands know AI search is important, but they may not yet know what to measure or how to act. Limy AI’s challenge is not only to build the technology, but also to help marketing leaders understand why agent visibility belongs in the modern growth stack.
How Aviv Shamny is positioning Limy AI differently
The AI visibility space is becoming crowded. Many companies are building tools around generative engine optimization, AI search tracking, brand mentions, and prompt monitoring. Limy AI stands out by focusing more directly on agent behavior and the infrastructure layer behind AI-driven discovery.
That difference is important. A dashboard that shows whether a brand appears in an AI answer can be useful, but it may not explain how the answer was formed. Limy AI’s deeper value is in helping brands understand the behavior behind the recommendation. Which agents visited the site? What did they fetch? Which prompts led to that activity? How did it connect to revenue?
This moves the conversation from visibility as a vanity metric to visibility as a business channel.
For marketing teams, that distinction matters. It is one thing to know that a brand is mentioned by an AI tool. It is far more useful to know what caused that mention, how the agent reached the answer, and whether the recommendation had commercial impact.
What brands can learn from Aviv Shamny’s approach
Aviv Shamny’s work with Limy AI offers several lessons for brands preparing for the next stage of online discovery.
First, brands should stop thinking of AI search as a distant trend. AI agents are already changing how people research products and services. Even if the market is still early, the shift is moving quickly enough that brands need to pay attention now.
Second, product content needs to be clearer than ever. AI systems rely on available information. If a product page is vague, outdated, thin, or poorly structured, an AI agent may struggle to understand it. Strong content is no longer only about persuasion. It is also about machine readability and trust.
Third, brands need to measure AI visibility instead of guessing. Marketing teams are used to data. They should bring the same discipline to AI-driven discovery. That includes tracking prompts, agent visits, product mentions, recommendation patterns, and conversion signals.
Fourth, AI visibility should connect to revenue. Mentions are useful, but brands ultimately care about business outcomes. Limy AI’s focus on prompt-to-conversion insight reflects a bigger need in the market: companies want to know whether AI discovery is creating real value.
Fifth, the brands that adapt early may learn faster. The agentic web is still forming, which means the playbook is not fixed yet. Early movers can test content, study agent behavior, improve product data, and build internal knowledge before the space becomes more competitive.
The future of brand visibility in the agentic web
The future of online visibility will likely be a mix of traditional SEO, AI search optimization, LLM visibility, structured product data, agent analytics, prompt-level attribution, and AI advertising. Brands will still need great websites and strong content, but they will also need to understand how AI systems interpret that content.
This is where Aviv Shamny and Limy AI are building with a clear sense of timing. The web is moving from a place where users search and click toward a place where AI systems help users decide and act. That shift creates uncertainty, but it also creates opportunity.
For brands, the message is simple. Being online is no longer enough. Being indexed is no longer enough. In the agentic web, brands need to be understood, trusted, and recommended by AI systems that may shape the customer journey before a human ever reaches the site.
Aviv Shamny’s achievement with Limy AI is rooted in recognizing this shift early and building infrastructure for it. As AI agents become more involved in discovery and commerce, Limy AI is positioning itself as a company that helps brands see what is happening, understand why it matters, and compete with more confidence in an AI-first internet.
