Naresh Kumar is a content and SEO specialist focused on understanding how AI is transforming the search landscape. His mission is to simplify AI Search by sharing accurate, research-backed information that explains the logic, mechanisms, and underlying processes behind AI-generated responses.
As AI becomes increasingly integrated into search platforms, Naresh believes that understanding how these systems work is essential for SEO professionals, marketers, and brands looking to remain visible in the evolving search landscape.
Understanding the Differences Between AI Search Platforms
One of the first things Naresh realized was that every AI search platform works differently. Although they may produce similar-looking answers, the technologies, information sources, and retrieval mechanisms behind those answers can vary significantly.
Therefore, optimizing for AI Search cannot be approached as a single, generalized process.
For example:
Google AI Overviews is an AI-powered search feature that generates summaries using information from web sources, often alongside Google’s traditional search results. It combines AI-generated responses with Google’s search capabilities.
ChatGPT can generate responses using knowledge acquired during training and, depending on the product, settings, and query, may also search the web or retrieve external information to provide more current answers.
Perplexity is built around AI-powered search, using retrieval from web sources to generate answers and provide citations that help users explore the original information.
Understanding these differences is an important part of developing an effective AI Search strategy.
What Makes Each AI Search Platform Different?
Each platform has its own strengths, capabilities, and approach to delivering information.
Gemini: The Connection Between Google Search and AI
Gemini is Google’s AI model family, integrated into Google’s broader ecosystem. Its connection with Google Search and Google’s search infrastructure can help it access and ground responses in relevant web information when search grounding is used.
For SEO professionals, this makes it important to understand the relationship between traditional search visibility, relevant web content, and AI-generated responses.
ChatGPT: Language Models and Conversational Assistance
ChatGPT is designed to understand user questions and generate conversational responses. Its capabilities extend beyond retrieving web pages, drawing on learned patterns and knowledge from training, reasoning capabilities, and external tools when available.
A brand’s visibility across the wider web may influence whether it is recognized, discussed, or recommended in certain contexts. Independent publications, expert opinions, community discussions, and other third-party sources can contribute to how a brand is represented online.
This makes it important to look beyond a brand’s own website and understand the broader information ecosystem surrounding it.
Perplexity: AI-Powered Answers With Web Citations
Perplexity combines large language models (LLMs) with web retrieval to answer questions using relevant information from external sources. It provides citations so users can examine the sources supporting its responses.
For marketers and SEO professionals, understanding how information is discovered, selected, synthesized, and cited is an important part of evaluating visibility on AI-powered search platforms.
Why Foundational SEO Still Matters in the AI Search Era
Naresh is a strong advocate of Foundational SEO because it provides many of the principles and practices needed to improve how websites are discovered, understood, and evaluated.
SEO, Answer Engine Optimization (AEO), and Generative Engine Optimization (GEO) are not entirely separate disciplines. They overlap in important areas, including content quality, technical accessibility, information architecture, relevance, and authority.
Foundational SEO provides structure for both machines and humans. A website needs accessible pages, clear information, meaningful content, and a well-organized structure before search systems can effectively process and use that information.
However, technical SEO alone does not guarantee that an AI platform will retrieve, cite, or recommend a website. Visibility also depends on the platform’s retrieval methods, relevance signals, source selection, and the context of the user’s question.
The goal is to build a strong foundation while understanding how AI systems discover and use information.
Two Years of Research Into AI Search and LLMs
Naresh has dedicated two years to researching the major AI search platforms and understanding the technologies that shape their responses.
Rather than relying solely on surface-level observations, he focuses on understanding the underlying mechanisms of large language models (LLMs), semantic search, and Retrieval-Augmented Generation (RAG).
He also follows the work and perspectives of AI researchers, engineers, and experts who develop these technologies. By attending AI summits and engaging with experts in relevant communities, he continues to explore how AI systems work and what their capabilities mean for SEO in practice.
His approach combines research, expert insights, and practical analysis to connect the technical concepts behind AI Search with actionable SEO strategies.
The objective is not simply to explain what AI platforms do, but to understand why they behave in certain ways and what SEO professionals can realistically do in response.
From Research to Practical AISEO Strategies
Naresh’s work goes beyond writing about SEO trends. He aims to help SEO professionals, marketers, and brands understand the changing search landscape and develop strategies for navigating it.
His approach focuses on translating complex technical concepts into practical knowledge that can inform content planning, website optimization, and long-term search visibility.
Through his research and educational content, he explores topics such as:
- Semantic Search: How search systems interpret meaning, context, and relationships between concepts.
- Large Language Models (LLMs): How language models process information and generate responses.
- Retrieval-Augmented Generation (RAG): How AI systems can retrieve external information to support generated answers.
- AI Search Visibility: How brands and websites can improve their chances of being discovered, referenced, and cited across AI-powered platforms.
- SEO, AEO, and GEO Strategy: How established SEO practices can be applied alongside strategies designed for AI-powered search experiences.
This research-led approach informs the educational resources available through AimforSEO.
Learn AISEO With AimforSEO
AimforSEO is an educational platform designed to help SEO professionals, marketers, and brands understand how AI and SEO work together.
Its mission is to bridge the AI knowledge gap in the SEO industry by making complex concepts easier to understand and apply.
The platform covers three key areas:
Beginners SEO Guide
Learn the foundational principles of SEO, from understanding search intent to building an accessible, well-structured website.
AI and SEO
Explore how artificial intelligence is changing search, content creation, information retrieval, and online visibility.
SEO Strategy for the AI Age
Understand how to adapt established SEO practices to an evolving search landscape shaped by AI-generated answers, LLMs, and retrieval-based systems.
Through blogs and YouTube videos, Naresh explains how different AI search platforms work and explores the cognitive and technical frameworks behind AI-generated responses.
His goal is to help people move beyond assumptions about AI Search and develop a clearer understanding of what happens behind the answers they see.
Naresh Kumar’s mission is simple: to simplify the AI Search landscape and help SEO professionals and brands make informed decisions as search continues to evolve.
Explore more at AimforSEO – Learn How AI and SEO Work Together.
