The future of website navigation: from keyword to semantic search

The humble search bar has, since the very early days of the internet, been every site visitor’s first port of call for finding answers fast. But the way people interact online has changed dramatically.  Rather than guessing keywords and trawling results, visitors now want natural language, intuitive search experiences.

To meet these expectations, businesses and public institutions are moving beyond keyword-based tools. They’re adopting semantic search: a smarter way to deliver results that are more relevant, personalized, and aligned to how people actually speak.

In this blog, we’ll break down the difference between semantic vs keyword search. We’ll explore how conversational AI search is helping organizations deliver more meaningful results that understand context and reduce friction.

Skip ahead:

• The differences between keyword search and semantic search
  • Why semantic search matters now
• Semantic search technology and how Squiz does it differently
  • 1. Powered by Squiz Funnelback
  • 2. Understanding beyond keywords
  • 3. Data control that prevents hallucinations
  • 4. Built-in attribution and transparency
  • 5. Optimized for answerability and structure
  • 6. Continuous learning and tuning
  • 7. Implementation support that goes beyond the tech
• Semantic search use cases
• Your next steps

Keyword search works by matching user queries to exact or partial word matches in your content. If someone types "student visa", it looks for those specific words on your site. It’s fast and effective, but only if users type the “right” keywords and your content includes them.

The issue? People don’t always know the terminology your site uses. They might search for "study visa" and miss results tagged as "student permit". And when content is complex, keyword search can either flood users with too many results or none at all.

Semantic search, on the other hand, goes further. It interprets the meaning behind a query, using AI to understand natural language, synonyms, and user intent.

This ability to handle incomplete, vague, or imprecise queries is one of semantic search’s biggest strengths. Users often type questions like “fees for applying” or “getting help with login” without full context. Semantic search uses natural language processing (NLP) to understand synonyms, implied intent, and contextual clues, allowing it to connect these fragments to the right answers, even when keywords don’t match directly.

So instead of matching words, semantic search can connect the question “How do I apply to study in Australia?” to content about international student applications, even if none of those exact words are used.

It’s the difference between searching with rigid rules and searching with understanding.

Semantic search isn't just more accurate, it’s also more user-friendly and inclusive. By interpreting meaning rather than relying on exact terms, it supports users who may not know the right terminology or who use varied phrasing. This reduces dead ends, frustration, and cognitive load, especially for people with lower digital literacy or using assistive technologies. It also returns more relevant results, improving user experience and satisfaction across the board.

At a glance: keyword search vs semantic search

• Matching logic:
  • Keyword search: uses literal word matching
  • Semantic search: matches based on intent and context.
• Language flexibility:
  • Keyword search: is limited to exact or partial matches
  • Semantic search: supports synonyms and varied phrasing.
• Result relevance:
  • Keyword search: results can be too broad or too narrow
  • Semantic search: delivers results tailored to the meaning of the query.
• User experience:
  • Keyword search: relies on users “guessing” the right words
  • Semantic search: feels natural, conversational, and more intuitive.
• Use case alignment:
  • Keyword search: is best for exact-match scenarios
  • Semantic search: excels at Q&A, long-tail, and vague queries.

See the table below for a visual comparison.

Users today expect more from search. They want:

• ChatGPT-like experiences embedded into websites
• Natural language input, not keyword guessing
• Direct answers, not 20 links to dig through

Keyword search can’t meet these needs on its own. Conversational AI search tools provide this context-aware, semantic search experience that understands user intent and delivers relevant answers in real time.

Not all semantic search tools are created equal. Many rely solely on natural language processing (NLP), a form of AI that helps machines interpret human language. While NLP can help match queries to content more flexibly than keyword search, it’s only part of the equation.

Without a powerful retrieval engine, natural language context alone won’t guarantee accurate, useful results. That’s where Squiz stands apart.

Squiz Conversational Search combines the linguistic understanding of NLP with enterprise-grade retrieval, customization, and guardrails. It understands intent and delivers accurate answers, not just surface-level summaries.

Here’s what sets it apart:

Our Conversational AI Search is built into Squiz Funnelback, a high-performance enterprise search engine. That gives your semantic search a foundation of fast, reliable, and scoped retrieval, ensuring that AI-generated answers are grounded in your most accurate and relevant content.

With advanced NLP, Squiz understands context, synonyms, intent, and phrasing, delivering natural responses that reflect how people actually speak.

It doesn’t just match text, it interprets user goals. For example, if someone types “get new photo ID,” semantic AI can infer they’re looking for a step-by-step guide, even if those words don’t appear explicitly. This makes the search experience more intuitive, responsive, and effective for real-world use.

The result? Answers that feel personalized, not robotic.

3. Data control and two-step verification process that prevent hallucinations

Unlike generic AI tools that pull from the open web, Squiz search is confined to your trusted content. Only approved pages are indexed, so users get accurate, brand-safe answers, while you stay in control of what the AI can see and say.

Squiz Conversational Search also uses a Retrieval-Augmented Generation (RAG) framework. It retrieves content using Squiz Funnelback’s enterprise-grade search engine that ensures only the content you want to crawl is included in the response. It then generates an answer using AI. Finally, it verifies the answer using a two-step verification process that checks responses for accuracy against your content and the question asked.

Here’s how it works, step by step:

1. Smart retrieval with Funnelback: When a user submits a query, Funnelback retrieves the most relevant content from your authorized data. This includes structured and unstructured content across your site, intranet, or any other integrated systems.
2. Prompt pairing and customization: The retrieved content is then paired with a customizable prompt template, controlling the tone, format, and fallback behavior of the AI response (e.g., what to say when no answer is found), all of which can be edited through the DXP interface.
3. Answer generation within your environment: A Large Language Model (LLM), hosted within Squiz’s secure DXP environment, generates an answer using your content only - never general internet data.
4. Built-in accuracy checks: Once the system has generated an answer, it automatically runs that answer through a built-in validation step – a “faithfulness check” that verifies the generated response draws only from your approved content sources and doesn’t include any external information.
5. Response delivery with full visibility: The user receives a direct answer with source attribution and can continue the conversation naturally. These interactions tracked and monitored, giving you insights needed to improve the user experience.

Example of this in practice:

The user asks: “How do I apply for a postgraduate scholarship if I’m an international student?”

Behind the scenes, Squiz Conversational Search uses the RAG process:

1. It first retrieves relevant, verified content from your approved data sources (e.g. a postgraduate scholarships page).
2. It then augments that content and generates a plain-language answer using that content only, never from general internet data.
3. It then automatically runs the answer through a built-in validation step to check it for accuracy before it’s delivered to the user.

Squiz Conversational Search then responds:

“To apply for a postgraduate scholarship as an international student, you’ll need to complete the online application form by July 31. You must have an offer of admission and meet the eligibility requirements outlined here [link].”

And when a high-confidence answer isn’t possible, fallback behaviors kick in, like showing traditional search results or prompting users to rephrase their query. This ensures transparency and protects against AI overreach.

All AI responses include clear source attributions with links, helping users verify information and giving content teams full visibility into what’s being returned.

Squiz supports structured content markup (like schema.org tags such as ‘FAQPage’ and ‘HowTo’), semantic metadata, and clean formatting. This makes it easier for AI to summarize complex content accurately.

With analytics and conversation logs, Squiz gives teams visibility into what people are asking, how answers are performing, and where improvements are needed. You can refine content, adjust indexing rules, or update tuning - all with full admin control.

Squiz doesn't just provide the tool. We partner with you through a proven consulting framework that includes:

• Conversational readiness assessments to evaluate if your content and architecture are AI-friendly
• Content audits to identify gaps, structure issues, or outdated materials
• Pilot scoping and crawl strategy to limit risk and start small
• Ongoing monitoring and optimization to ensure long-term success

This guided approach helps government, higher education, and enterprise clients avoid common pitfalls and deliver conversational AI search experiences that are both safe and impactful.

Semantic search can transform digital experience across different sectors. Here are some examples of how organizations can put it to work:

• Higher education: Prospective students can ask natural questions like “When do classes start?” or “How do I apply?” and get instant, accurate answers about courses, dates, and admissions; without having to navigate a labyrinth of dropdowns and PDFs.
• Government: Citizens asking “What forms do I need to renew my license?” receive clear, AI-powered responses drawn from verified content. This reduces support center volume, improves accessibility, and strengthens public trust in digital services.
• Professional services: Clients or consultants looking for “risk compliance policy” are taken straight to the relevant clause, not buried in a 40-page PDF or lost in outdated intranet structures. The result? Faster access, better service, and lower internal friction.

For more details on the benefits of this technology for different industries, check out the blogs here.

With semantic search, your site doesn’t just return results; it delivers answers. Squiz Conversational Search (powered by Squiz Funnelback) brings this capability to your organization.

And to help you maximize the success of this AI implementation, we offer a dedicated consultancy framework that supports content readiness, smooth implementation of conversational tools, and continuous improvement over time.

Want advice on how to get started with conversational AI search? Book a 30-minute chat with a Squiz strategy consultant here.