Search is no longer a single channel. In 2025 and 2026, when someone wants information, they do not only open Google and click on blue links. They ask ChatGPT. They search on Perplexity. They speak to voice assistants. They read AI-generated summaries inside Google’s AI Overviews. The way people discover content has fundamentally changed, and most websites are not ready for it.
Muhammad Naqash is an SEO and digital marketing strategist who works at the intersection of traditional search optimization and the newer disciplines of Generative Engine Optimization (GEO) and Answer Engine Optimization (AEO). Over the past several years, his work has evolved from building standard keyword-driven content strategies to developing a unified framework called Hybrid Engine Optimization (HEO) — a methodology designed to make websites visible not just in Google, but across every major discovery channel that modern users rely on.
This case study documents a real HEO engagement: the challenge, the strategy, the execution, and the measurable results. It is written for SEO professionals, content marketers, and business owners who want to understand what it takes to achieve strong search visibility in the age of AI.
Who Is Muhammad Naqash?
Muhammad Naqash has worked in digital marketing and SEO for several years, building expertise across technical SEO, content strategy, link building, and AI-driven search visibility. His work spans multiple industries and client types, including SaaS companies, service businesses, and personal brands looking to establish authority in competitive niches.
His approach to search has always been data-driven and entity-focused. Rather than chasing keyword rankings alone, Naqash builds content ecosystems — interconnected clusters of pages that signal topical authority to both search engines and AI systems. Over time, this approach led him to recognize a critical gap in how most SEO professionals were thinking about modern search.
Most websites were being optimized for Google’s traditional ranking algorithm. Almost none were being optimized for large language models (LLMs) or the AI-powered search systems that use those models to generate answers. Naqash developed the HEO framework to close that gap.
What Is Hybrid Engine Optimization (HEO)?
Hybrid Engine Optimization is a search strategy that targets three distinct discovery channels within a single, unified approach. These three channels are traditional search engines, generative AI engines, and answer engines or voice systems.
Traditional search engines — primarily Google — rank pages based on relevance, authority, backlinks, and technical performance. Generative AI engines like ChatGPT Search, Perplexity AI, and Google’s AI Overviews generate answers by synthesizing and citing web content. Answer engines and voice systems like featured snippets and Siri pull direct answers from structured web content.
Most SEO strategies in 2024 and 2025 addressed only the first channel. HEO addresses all three simultaneously. The core insight behind HEO is that each of these channels consumes content differently. Google’s algorithm weighs signals like PageRank and E-E-A-T. LLMs look for semantic clarity, entity consistency, and citation-worthy prose. Answer engines prefer short, direct, factual responses followed by supporting detail.
A piece of content written only for keywords will rank on Google but get ignored by AI engines. A piece written only for AI citations may lack the depth and backlinks needed to rank in traditional search. HEO builds content that satisfies all three simultaneously — and that is what makes it powerful.
The Client: A B2B SaaS Company in HR Technology
The client for this case study was a B2B SaaS company offering HR management software to small and medium-sized businesses. The company had a professionally built website, a blog with around 40 published articles, and a domain rating of 21 at the start of the engagement. They were ranking on page two of Google for several target keywords but generating minimal organic traffic and had zero presence in AI-generated search results.
Their primary goals were to increase organic traffic, capture featured snippets for high-intent informational queries, and establish brand visibility in AI-powered search channels where their competitors were beginning to appear.
The engagement ran for approximately 90 days with a structured execution plan divided into three phases.
Phase One: Audit and Foundation (Weeks 1–4)
The first phase focused entirely on understanding the current state of the website and laying the groundwork for everything that followed. This included a full technical SEO audit, an entity mapping exercise, and a competitor analysis focused not just on Google rankings but on AI citation patterns.
Technical SEO Audit
The technical audit identified several issues limiting the website’s crawlability and indexation. These included slow page load speeds due to unoptimized images, missing canonical tags on several blog pages, broken internal links, and inconsistent URL structures. These issues were resolved in the first two weeks.
Core Web Vitals — specifically Largest Contentful Paint (LCP) and Cumulative Layout Shift (CLS) — were also below Google’s recommended thresholds. Improving these metrics required image compression, font preloading, and JavaScript deferral, all of which were implemented during this phase.
Entity Mapping
Entity mapping is one of the most important and most overlooked steps in modern SEO. An entity is any named concept — a person, company, product, or topic — that a knowledge system like Google’s Knowledge Graph or an LLM can recognize and associate with related concepts.
For this client, the entity mapping exercise identified the core concepts the website needed to be associated with in order to appear in AI-generated answers. These included HR software, employee onboarding, payroll management, leave tracking, and performance reviews. The audit found that the website had content covering these topics, but it did not establish clear semantic relationships between them — meaning Google and LLMs could not easily understand what the website was fundamentally about.
Schema Markup Implementation
Structured data was deployed across all key pages during this phase. The schema types implemented included Organization, SoftwareApplication, FAQPage, HowTo, and BreadcrumbList. Each schema block was validated using Google’s Rich Results Test before deployment.
An author page was also created with Person schema, establishing a named human entity associated with the content — a signal that both Google and LLMs use to assess credibility and expertise.
Phase Two: Semantic Content Build-Out (Weeks 5–8)
Phase two was the most content-intensive part of the engagement. The goal was to publish a cluster of semantically rich articles that would establish the website as a topical authority — not just for Google’s algorithm, but for the retrieval systems inside AI engines.
The LLM-Readable Content Framework
Before writing a single article, a content framework was developed based on patterns that LLMs prefer when selecting sources to cite. Research into how ChatGPT, Perplexity, and similar systems choose their sources reveals a consistent preference for content that follows an encyclopedic structure.
The framework structured every article around five elements in the following order. First, a Definition — a clear, direct answer to the core question the article addresses, written in the first 50 to 70 words. Second, Context — background information that explains why the topic matters. Third, Evidence — data or referenced claims that support the article’s main argument. Fourth, an Example — a concrete, specific illustration of the concept in practice. Fifth, Implication — a closing section explaining what the reader should understand or do differently.
This structure serves two purposes simultaneously. For Google, it creates a well-organized, intent-matching article. For LLMs, it creates citation-worthy content that a generative system can reliably extract and reference when answering related questions.
Content Published in Phase Two
Fourteen articles were published during weeks five through eight. Each article targeted a specific informational query related to HR software, employee management, or workplace processes. Topics included employee onboarding checklists, payroll compliance guides, remote team management, performance review templates, and leave policy best practices.
Every article included an Answer Block — a concise 40 to 60 word direct answer placed at the very top of the page, before any other content. This Answer Block was written specifically to be captured as a featured snippet by Google and as a cited answer by AI engines. Internal linking was structured as a topic cluster, with each article linking back to a central pillar page on HR software.
Phase Three: Authority and Citation Building (Weeks 9–12)
Phase three focused on building the external signals that tell both Google and LLMs that this website is a trusted, authoritative source in its niche.
Guest Posting on High-Authority Platforms
A targeted guest posting campaign was executed across platforms with high domain authority and strong indexation by AI training datasets. Platforms used included dev.to, Hackernoon, Medium, Hashnode, and Mirror.xyz. Each guest post included entity mentions — references to the client’s brand name, product name, and key topics — along with contextual backlinks to the most important pages on the client’s website.
The selection of these platforms was deliberate. These are not just high-DA sites for Google backlinks. They are platforms whose content is frequently included in LLM training data and referenced by AI engines when generating answers. Building entity mentions on these platforms increases the probability that an AI system will recognize the client’s brand as an authoritative entity in its domain.
Brand Mention Tracking
Beyond guest posting, a brand mention tracking strategy was implemented across Reddit, Quora, LinkedIn, and relevant industry forums. Existing conversations about HR software problems were identified and answered with helpful, non-promotional responses that naturally referenced the client’s expertise and content. These unlinked brand mentions contribute to what SEO researchers call topical authority signals — patterns of consistent, contextual association between a brand and its subject area.
Results After 90 Days
The results of the HEO engagement after 90 days were measurable across all three channels.
Google organic impressions increased from 12,400 per month to 38,900 per month, representing a 214 percent increase. The website went from capturing 3 featured snippets to capturing 21, a 600 percent increase. Average keyword position improved from 34.2 to 18.7. Domain rating increased from 21 to 29 through the guest posting and citation building campaign.
In terms of AI visibility, the client went from zero tracked AI engine citations to 47 citations across ChatGPT Search, Perplexity, and Google AI Overviews within the 90-day period. This was tracked using a custom AI Visibility and LLM Rank Analyzer tool that tests how specific AI engines respond to queries related to the client’s topics and whether the client’s content or brand is cited in the response.
The most significant finding was that AI citation frequency was strongly correlated with the content structure changes made in phase two — particularly the Answer Block format and the Definition-Context-Evidence-Example-Implication framework. Articles that followed this structure were cited at a rate approximately 3.2 times higher than articles that did not.
Key Lessons from This HEO Engagement
Several important lessons emerged from this engagement that apply broadly to any website attempting to achieve visibility in modern search.
The first lesson is that entity clarity matters more than keyword density. Google and LLMs do not just match keywords — they map entities. A website that clearly establishes what it is, who it is associated with, and what topics it covers at a conceptual level will consistently outperform a website that simply repeats keywords in its content.
The second lesson is that structure is a ranking signal for AI engines. LLMs are trained on enormous amounts of text, and they have learned to recognize authoritative content by its structure. Content that opens with a direct answer, supports that answer with evidence, and closes with a clear implication consistently performs better in generative search than content written in a conversational or narrative style without clear logical organization.
The third lesson is that traditional SEO and AI optimization are not in conflict — they are complementary. The same practices that make content clear, well-structured, and authoritative for LLMs also tend to improve user experience, reduce bounce rates, and increase dwell time — all signals that Google’s algorithm values positively.
The fourth lesson is that external citations on the right platforms matter significantly for AI visibility. Building entity mentions on platforms that are known to be part of LLM training datasets — such as dev.to, Hackernoon, and Medium — creates a pattern of association that AI engines recognize when deciding which sources to cite.
Frequently Asked Questions
What is Hybrid Engine Optimization (HEO)?
HEO is a unified search strategy that combines traditional SEO, Generative Engine Optimization (GEO), and Answer Engine Optimization (AEO) to make content visible across Google, AI-powered search engines, and voice or featured-snippet systems simultaneously. Instead of optimizing for one discovery channel, HEO ensures a brand or website is findable wherever modern users search for information.
How is GEO different from traditional SEO?
Traditional SEO focuses on ranking pages in Google’s search results through keyword targeting, backlink building, and technical optimization. GEO focuses on making content citation-worthy for AI-powered systems like ChatGPT Search, Perplexity AI, and Google AI Overviews. GEO requires semantic depth, entity clarity, and an LLM-readable content structure that goes beyond what traditional keyword SEO provides.
Can a small website get cited in AI-generated answers?
Yes. LLMs do not exclusively cite high-authority domains. They prioritize content that is clear, well-structured, semantically accurate, and entity-rich. A niche website with strong topical authority and properly implemented schema markup can consistently appear in AI-generated answers even when competing against much larger websites.
What tools does Muhammad Naqash use for HEO tracking?
The primary tools used include Google Search Console for traditional SEO performance tracking, Ahrefs for backlink analysis and domain rating monitoring, and a custom-built AI Visibility and LLM Rank Analyzer tool for measuring citation frequency across ChatGPT, Perplexity, and Gemini. Manual prompt testing is also used to verify brand and entity citations in real AI engine responses.
How long does HEO take to show results?
Traditional SEO improvements typically become visible within 4 to 8 weeks. GEO and AEO results — including featured snippet captures and AI engine citations — generally appear within 6 to 12 weeks when content is properly structured and schema markup is correctly implemented across the site.
Is HEO suitable for small businesses and freelancers?
Yes. HEO principles apply at any scale. A freelancer or small business owner can implement the core elements of HEO — semantic content structure, schema markup, entity clarity, and Answer Blocks — without a large budget. The fundamental requirement is not spending power but content quality and structural discipline.
Conclusion: Search Visibility in 2026 Requires a Hybrid Approach
The evidence from this case study, and from broader trends in search behavior, points clearly in one direction. Standalone SEO — optimizing only for Google’s traditional ranking algorithm — is no longer sufficient for maximum organic visibility. AI-powered search engines are capturing a growing share of information discovery, and that share is increasing every month.
Brands and content creators who want to remain visible in this environment must adopt a strategy that addresses all three modern discovery channels: algorithmic search, generative AI search, and answer engine search. That is precisely what Hybrid Engine Optimization provides.
Muhammad Naqash’s HEO framework offers a structured, reproducible methodology for achieving this kind of comprehensive visibility. By combining technical SEO excellence with semantic content architecture and entity-focused authority building, it is possible to create a web presence that is not just ranked by Google — but cited by AI, featured in snippets, and trusted by the knowledge systems that increasingly mediate how people find information online.
For anyone serious about search visibility in 2026 and beyond, HEO is not an optional upgrade. It is the new baseline.
