‍Why is Traditional Search Broken?

For over a decade, digital marketing teams have spent heavily to drive high-intent traffic to their websites, only to lose prospects at a pivotal moment: the search bar. The issue isn’t awareness or product-market fit, but the outdated search experiences users encounter once they arrive. Dropdowns, clunky filters, and static keyword logic fail to match how people actually search today. The result? Lower conversions, missed opportunities, and a quiet but persistent drain on acquisition ROI.

_{The Hidden Leak in the Funnel}

Despite rising acquisition costs, marketers continue investing in paid ads - yet conversion rates remain stubbornly low. The real problem lies in what happens after the click.

Poor search experiences are a key culprit. 41% of e-commerce sites fail to support essential query types, forcing users into frustrating trial-and-error loops that lead to abandonment. This weakens funnels and reduces returns on every dollar spent.

At the heart of the issue is structure: traditional site search expects users to conform to rigid taxonomies - the internal organization of product catalogs, rather than adapting to natural human language. Users are left guessing the “right” keyword or navigating logic designed for systems, not people.

When they can’t find what they need quickly, they bounce. In fact, 75% won’t scroll past the first page of results. This drop-off is especially costly during the exploration and consideration stages, when buyers are still forming intent. Static search may work for direct queries like “buy Nike shoes,” but fails with more nuanced needs like “kids’ hiking footwear for monsoon season in India.”

CMOs Are Reframing Search as a Growth Lever

As user expectations shift and competition intensifies, marketing leaders are rethinking a once-overlooked asset: site search. No longer just a functional tool, it now plays a critical role in shaping customer experience, influencing conversion rates, and defining brand perception, often from the very first interaction.

In 2025, the digital landscape is increasingly dominated by AI-generated summaries, zero-click results, and dwindling returns on paid ads. Amid these changes, on-site search remains one of the few experiences brands can fully control. But with rising acquisition costs, control alone isn’t enough. Search must do more than retrieve links - it must drive outcomes.

That’s why CMOs are embracing conversational AI agents: intelligent systems that replace static search with dynamic engagement. These agents are built to:

1. Understand complex and evolving user intent
   
   
2. Respond conversationally across multiple interactions
   
   
3. Offer contextual, personalized guidance
   
   
4. Reduce bounce by helping users find what they need faster
   
   
5. Create seamless experiences that deepen trust and encourage return visits

About Conversational AI Search

Since today’s users don’t want to adapt to outdated systems, they expect search to feel intuitive, responsive, and personalized. Increasingly, they want to express needs in natural language, refine their queries as they go, and receive helpful answers, just like they would when speaking to a knowledgeable salesperson. This is where Conversational AI Search changes the game.

But what exactly is Conversational AI Search?

At its core, Conversational AI Search builds on the foundation of natural language search (NLS), a system that allows users to type queries as if speaking aloud (e.g., “best shoes for hiking in summer”) instead of relying on rigid, optimized keyword strings. Powered by AI and natural language processing, NLS can interpret grammar, syntax, and user intent, returning results that better match meaning rather than literal phrasing. It’s what powers voice assistants like Siri and Google Assistant, as well as modern AI search interfaces on leading ecommerce platforms.

Yet, while NLS greatly improves single-query usability, it often falls short when users want to refine or expand on their search. That’s where Conversational AI Search takes over.

Conversational AI Search enables multi-turn interactions, letting users ask follow-up questions, adjust their preferences, or clarify needs, all while the system retains context from earlier queries. It mimics the natural back-and-forth of a real conversation, making it easier for users to reach the right outcome, especially in scenarios that require discovery or guidance.

Instead of treating each search as a standalone input, Conversational AI Search understands evolving intent, and that’s exactly what modern users, and marketers, have been missing.

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Understanding Conversational AI Search (and Why Natural Language Search Alone Isn’t Enough)

So, if natural language search has already changed how users interact, why isn’t it enough?

While NLS allows users to phrase queries conversationally (“best laptop for video editing”), it typically treats each question in isolation. That’s useful for a single search, but insufficient for journeys that unfold over time. The real power lies in turning language into dialogue, where each new input builds on the last and the system adapts dynamically.

Consider the journey of a customer browsing a product catalog. They might begin with:

“I need a laptop for graphic design.”

But they rarely stop there. Their needs evolve:

“I prefer a Mac.”
“It should be under $2,000.”
“I want it to be lightweight.”
“Will it fit in my backpavk?” (typos included)

Traditional systems would treat each input as a new query, discarding context and forcing users to start over. In contrast, conversational systems retain prior information, adjust filters in real time, and guide the user toward the most relevant option, without breaking flow.

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This kind of contextual memory is critical, especially in industries with complex decision paths. Whether the use case is selecting enterprise software, navigating insurance plans, or exploring educational programs, users rarely find what they need in one shot. Their intent becomes clearer through interaction, and only a conversational system can keep up.

In addition, Conversational AI Search takes natural language input a step further by enabling real-time, multi-turn dialogue. Users can ask follow-up questions, refine preferences, and clarify needs, all without losing context. The system remembers what’s been asked, interprets new inputs in relation to prior ones, and understands how each interaction fits into a broader conversation.

Powered by large language models (LLMs) like ChatGPT, Gemini, or Perplexity, Conversational AI Search transforms the static search bar into an intelligent, fluid assistant. Users no longer have to retype or repeat themselves. Instead, they explore and adjust naturally, with the system adapting in real time.

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Traditional Search vs. Conversational AI Search: A Clear Comparison

The Three Faces of Conversational AI Search: Search Bars, Chatbots, and AI Agents

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As Conversational AI Search gains traction, brands are rethinking not just how users search-but where those searches happen. Today, there are three main user interface (UI) models leading the shift toward natural, intent-driven experiences:

Search Bars – now upgraded with natural language input

Chatbots – scripted assistants for basic user queries

AI Agents – autonomous, outcome-oriented collaborators

At a glance, all three seem to support “conversational” experiences. However, how they interpret intent, guide users, and drive action varies dramatically. Understanding these differences is key to choosing the right UI-and investing in the one that moves the needle.

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 UI Comparison: How Each Interface Handles Conversational AI Search

Why AI Agents Are Replacing Chatbots and Smart Search Bars

As expectations for digital experiences rise, traditional tools like smart search bars and scripted chatbots are no longer enough. While they’ve helped users navigate sites and access basic information, they fail to deliver the dynamic, personalized, and outcome-driven experiences modern users demand-especially in industries with complex, high-consideration journeys like travel, real estate, and enterprise commerce.

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That’s where AI agents come in. Unlike static interfaces, AI agents don’t just respond-they act. Powered by large language models and orchestrated workflows, they retain context, adapt to evolving needs, and trigger real business actions. Whether it’s guiding a customer to the right vacation package, assisting a sales team during a deal cycle, or automating a lead nurture flow-AI agents handle it seamlessly.

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At Kleio, we’ve built a system of specialized agents designed to support the entire revenue journey:

• Lisa drives lead generation by engaging visitors, qualifying them, recommending the right product or service, and guiding them to booking or conversion. In the travel sector, Lisa helped Havas Voyages increase qualified leads by over 200%.
• Alex acts as a sales copilot-surfacing customer insights, identifying upsell opportunities, drafting proposals, and automating follow-ups. He’s tailored for teams in real estate and B2B commerce where timing and precision matter most.
• Kate supports operations and marketing by integrating with backend systems to adapt to an individual company’s needs.

These agents are deeply integrated into your systems-CRM, inventory, CMS, analytics-so they don’t just talk, they take action. They’re live on your site, working 24/7, scaling your ability to personalize and convert without added headcount.

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What Powers Conversational AI Search: The Architecture Behind the Experience

Delivering seamless Conversational AI Search isn’t just about having a good UI-it’s about building a sophisticated, multi-layered backend that can interpret messy human intent and return precise, actionable results in real time. The challenge is especially steep in industries like travel, real estate, and complex commerce, where product catalogs are vast, user queries are vague, and decisions are high-stakes.

To meet these demands, Kleio’s AI agents are powered by a stack of advanced technologies that work together to turn curiosity into conversion-at scale.

What Powers Conversational AI Search (Tech Deep Dive for CMOs)

Delivering a high-performing Conversational AI Search experience at scale requires powerful AI systems working behind the scenes. Here's a breakdown of the core technologies, and why they matter for customer experience and conversions:

1. Hybrid Search: Keyword + Semantic Understanding

Conversational AI Search blends traditional keyword matching with semantic AI. It can handle both precise inputs like “red running shoes size 10” and open-ended queries like “comfy shoes for walking,” delivering relevant results for both.

2. Direct Integration with Product Catalogs

In ecommerce, these systems plug into your product catalog and content libraries. They pull from structured data (price, color, size) and unstructured data (descriptions, reviews) to match queries with the most relevant products, using natural language input.

3. Knowledge Graphs: Mapping Intent to Products

AI agents use knowledge graphs to understand relationships between products, attributes, and user intent. This helps interpret context and connect related concepts, even when queries are incomplete or vague.

4. Semantic Filtering on the Fly

The system can auto-generate filters and recommendations based on query meaning. Ask for “eco-friendly office chairs,” and the engine applies filters even if those exact words don’t appear in your catalog.

5. Multi-Turn Dialogue with Memory

Unlike static search, conversational systems support back-and-forth refinement. A user might say, “What about something cheaper?” or “Show me the blue one.” The AI remembers the context and adapts dynamically to narrow down results.

6. The Hard Part: Interpreting Vague Queries Accurately

The biggest technical challenge is mapping conversational, often ambiguous queries to the right results. AI tackles this through contextual understanding, memory, and intent recognition, but it depends on strong training data and continuous model improvement.

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How to Evaluate a Conversational AI Search Platform

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Not all platforms are built to handle a high level of complexity. Here are six critical factors CMOs should assess:

• Catalog Depth & Flexibility: Can it sync with large, dynamic catalogs-pricing, inventory, and variants included?
• Multi-Database Querying: Can it query across SQL, graph, and vector databases simultaneously?
• Real Semantic Understanding: Does it combine keyword precision with deep intent recognition?
• Conversational UI & Personalization: Can it guide users iteratively through discovery, not just return static results?
• Memory & Cross-Session Context: Does it remember users across visits and tailor the journey accordingly?
• Data & System Integration: Can it connect with your CRM, analytics, APIs, and maintain factual accuracy with built-in safeguards?

Kleio checks all of the above-backed by fast implementation, enterprise-ready compliance, and unmatched performance across verticals.

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Enterprise Use Cases: Where Conversational AI Search Shines

Conversational AI is revolutionizing industries with complex buying journeys, vast product catalogs, and multi-step decision-making processes. Kleio's AI agents, Lisa and Alex, are at the forefront of this transformation, delivering personalized, efficient, and scalable solutions tailored to specific industry needs. You can read more about them here.

Real Estate

In the real estate sector, Lisa acts as a 24/7 digital sales representative, engaging website visitors, qualifying leads, and scheduling viewings by integrating with agents' calendars. She provides personalized property recommendations based on user preferences such as location, budget, and amenities. Alex complements this by offering sales teams real-time insights, identifying upsell opportunities, and generating client-ready proposals, thereby streamlining the sales process and enhancing client engagement. 

Read more about Kleio’s AI agents for real-estate here.

Travel and Hospitality

For travel and hospitality businesses, Lisa serves as a virtual travel advisor, assisting customers in planning itineraries, adjusting for budgets, and cross-referencing live availability across numerous options. She recreates the personalized experience of a human consultant at scale. Alex supports sales teams by providing data-driven insights and strategic guidance to accelerate deal closures and maximize revenue growth. 

Read more about Kleio’s AI agents for travel here.

Energy Efficiency and Renewables

In the energy efficiency and renewables industry, Lisa simplifies the discovery of subsidies and eligibility checking for services like solar panel installations or heat pump rebates. She guides users through technical criteria and qualification processes, reducing the need for manual intervention and enhancing user experience.

Read more about Kleio’s AI agents for energy efficiency here.

Financial Services and Insurance

Lisa assists customers in understanding complex financial products, simulating rates, and clarifying terms, thereby reducing the load on support teams and building trust with clients. Alex aids sales representatives by surfacing customer insights, identifying upsell opportunities, and automating follow-ups, which enhances the efficiency and effectiveness of the sales process.

Read more about Kleio’s AI agents for insurance here.

Beauty and Automotive

In industries where personal preference is paramount, such as beauty and automotive, Lisa provides personalized recommendations for products like skincare routines or vehicle models based on user behavior and input. Alex supports sales teams by offering real-time insights and generating tailored selling arguments, thereby creating a personalized retail experience that mirrors in-store consultations. 

Read more about Kleio’s AI agents for beauty, or the automotive industry. 

Conclusion: The Future of Search Is Conversational, Contextual, and Conversion-Driven

Traditional site search was never built for how people explore, decide, or buy in 2025. As marketing leaders face shrinking ad returns, rising acquisition costs, and more discerning digital buyers, search can no longer be treated as a backend function. It’s now a frontline growth lever.

Conversational AI Search transforms that lever into a performance engine, turning passive clicks into active conversations, static pages into guided journeys, and intent into outcomes. With memory, personalization, and real-time guidance, AI agents like Kleio’s Lisa and Alex are doing what no static search bar or scripted chatbot ever could: driving revenue, reducing bounce, and deepening trust at scale.

For brands navigating complex customer journeys - from real estate to retail, travel to fintech, the message is clear: the search bar is no longer just where users ask questions. It’s how you guide them through the buying journey that determines whether you win the next conversion, and with Conversational AI, we’ve made that possible for you.

Ready to turn search into your most strategic channel? Request a Kleio demo here.

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