AI-powered search: What you need to know [2025]

Artificial Intelligence (AI) powered search is expected to become the primary search modality for 90 million users in the US by 2027.

That’s because it brings machine learning (ML) and natural language processing (NLP) together to deliver faster, smarter, and more relevant results by decoding context, intent, and semantic meaning rather than relying on exact keyword matches.

What’s in it for you? Improved accuracy, higher conversion rates, and slicker user experiences. However, you may need to consider drawbacks like implementation complexity and resource costs.

Unlike traditional search, it handles conversational or ambiguous queries, learns user habits, and offers semantic precision, personalization, and typo tolerance. Think Meilisearch, Perplexity, Microsoft Copilot, You.com, and Brave Search.

Are you thinking of implementing AI-powered search? Budget for infrastructure, scaling, and optimization costs. Plus, measure its effectiveness using user engagement and conversion rates while future-proofing with flexible and scalable solutions.

What is AI-powered search?

AI-powered search uses ML and NLP to make search engines smarter, faster, and more intuitive. By integrating NLP, ML, and deep learning into the search activity, AI-powered search has enabled users to move from traditionally keyword-optimized results to more relevant and better-suited outputs.

With inputs extending beyond what the user types, it considers user habits, past searches, and contextually appropriate backgrounds to provide holistic results. Thus, continuous learning from and refining the search results is a hallmark of AI-powered search.

As AI continues to push the boundaries of what's possible, search technology is advancing at a rapid pace. To truly harness its potential, we need to deepen our understanding of how these systems work—and how they can empower us to build better, faster, and more intuitive experiences.

How does AI-powered search work?

AI-powered search works by applying ML and NLP techniques to understand the intent, context, and background behind a query, also known as semantic search. Then, using filters (including those for location and demographics) and advanced algorithms, the closest possible match to the query is produced.

The combination of ML, NLP, and vector search—which maps words, phrases, and concepts in multidimensional space—enables the AI-powered search engine to handle synonyms, contextually relevant words, and even typos. This allows it to deliver the best search engine result page (SERP), customized like a made-to-order bespoke suit.

It even remembers user interactions and continues to evolve based on behavior, past searches, and engagement patterns, thus providing better results with every search.

The more data the system processes, the better it becomes at predicting and displaying results that align with your preferences. Think: a personal tailor slowly adapting your measurements based on your changing fitness regime.

Understanding how AI-powered search works lays the foundation for appreciating its role in improving user experiences.

What are the key benefits of AI-powered site search?

AI-powered site search isn't just a fancy add-on—it's a complete game-changer for your website. Let’s look at some key benefits of AI-powered intelligent search that make it worth the switch:

1. Improved search relevance and accuracy

Separating the wheat from the chaff in your search results can be a considerable burden. The power of AI in search facilitates this process and makes SERPs a breeze to go through. Developers can also implement safeguards and “human-verified” datasets to maximize precision and accuracy.

For instance, in research, AI tools like Elicit help researchers fine-tune their research interests to ensure relevance, impact, and novelty, helping researchers refine ideas, spark creativity, and minimize stress.

2. Faster information retrieval

AI-powered search significantly reduces search time by predicting user queries and automatically suggesting relevant results. This includes the auto-fill feature we have grown to love in almost every conceivable search bar on the Internet.

For example, after implementing Meilisearch's AI-powered search, Bookshop.org dramatically improved user experience across its six-million-book inventory, resulting in considerably faster searches and a substantial 43% increase in sales-based purchases.

3. Enhanced personalization

AI-driven search tailors results based on individual user preferences, offering a more customized search experience than a traditional Google search.

For instance, using AI-powered search, platforms like Netflix and Spotify deliver highly personalized recommendations based on user habits. A Statista survey confirms this advantage, with over one-third of respondents finding AI search results more specific or trustworthy than traditional search engines.

4. Multimodal search capabilities

Modern AI search can process images, voice, and text queries, making search interactions more versatile. This makes navigating the world around you that much easier, whether it’s an unfamiliar signboard or laundry labels that need decoding.

For instance, the recently developed neural network CLIP from OpenAI uses the combined power of text and images on the internet to provide up to 88% accuracy for search results based on handwritten numbers.

While AI-powered site search offers significant advantages, it doesn’t come without its challenges.

What are the drawbacks of AI-driven search?

Understanding its pitfalls is key to successfully integrating AI-driven search into your website.

1. High development, Implementation, and maintenance costs

Setting up, sustaining, and improving an AI-driven search system requires significant technology, infrastructure, and talent investment, with much of this cost being transferred to the consumer.

While open-source solutions may cost nothing to license, they may yet have costs associated with implementation. However, the cost of tailored solutions may start at hundreds of dollars for the most basic features and go up to thousands of dollars.

2. Biased results

AI search engines rely on high-quality data to function effectively. Poorly structured or biased data can lead to inaccurate search results. In fact, over 50% of participants surveyed by Statista reported that they had minuscule or no trust at all in AI’s ability to make unbiased decisions.

3. Privacy and security concerns

By virtue of being constantly learning and evolving systems, AI search engines must process vast amounts of user data.

Because this may lead to a feeling of unease and distrust among users, ensuring data privacy and security is crucial. State-specific legislation, such as the Artificial Intelligence and Policy Act enacted by Utah in 2024, may need to be considered as well.

Despite the challenges it brings, AI-powered search is miles ahead of traditional search.

How does AI-powered search differ from traditional search?

Traditional search relies on exact keyword matching and predefined ranking rules like Google’s EEAT framework. In contrast, AI-powered search utilizes NLP, ML, and intelligent algorithms to understand user intent, context, and semantics. This leads to more precise and dynamic search results.

Imagine searching for "best budget smartphone with a good camera." A traditional search engine might return pages that contain the exact words "budget," "smartphone," and "camera," without necessarily understanding your intent.

In contrast, given the same input, an AI-powered search engine will analyze the query in context, recognizing that you're looking for an affordable phone with high camera quality, and then surface reviews, recommendations, and product listings that best match your needs.

The secret of AI-driven search’s supremacy lies in its defining features.

What are the core features of new generation AI-powered search?

As mentioned earlier, AI-powered search is itself powered by NLP and ML to interpret human-like and conversational queries. However, within its system lie more complex structures that are organized layer by layer.

AI-powered search consists of three key layers:

Storage layer

The first layer deals with information storage and retrieval.

Vector database

Vector databases allow complex semantic and lexical relationships between words to be stored as multidimensional numerical embeddings, thus allowing search systems to index and parse datasets efficiently. This lends lightning-fast speeds to modern AI-based similarity searches across millions of items.

Multiple DBs for full-text search

Even though more is not always better, having multiple specialized databases integrated into the architecture of modern search systems proves that, in this context, it does.

Prefix databases for immediate search suggestions, inverted indexes for quick full-text searches, and specialized stores designed to manage typos and variations are a few components that make the picture complete.

Capabilities layer

The second layer concerns multi-functional search capabilities, where the most relevant one is activated depending on the requirement.

Full-text search

Full-text search consists of indexing and searching that, when brought together, function like an efficient filing system with millions of data points. It provides precise lexical matching that allows search systems to tolerate queries with ambiguity and typos while enabling prefix searching.

Popular uses of full-text search include content management systems and e-commerce stores with large knowledge bases and product catalogs, respectively.

Vector search

Vector search, like its namesake vector databases, looks for and captures the contextual meanings of words as numerical representations. This allows for efficient and accurate information retrieval, even when the exact keywords are missing.

Imagine being suggested a vintage wool coat when you’re on the lookout for stylish winter outerwear—this is only possible with vector search.

Hybrid search

Put the sheer competence of full-text search with the multi-dimensional semantic space of vector search, and you’ll get hybrid search. The most powerful search capabilities, hybrid search, give users the confidence to present their queries however they wish and still get the correct output.

Application layer

The third and final layer of new-generation AI-powered search is what brings home the bacon, so to speak. It enables raw search capabilities to be implemented in business platforms, thus driving actual business value.

It also brings together recommendation engines for dynamic suggestions of related items, semantic search that understands natural language queries, and personalization systems that adapt results based on user behavior.

Hugging Face utilizes this layer to assist users in finding relevant AI models tailored to their specific use cases. HitPay uses it to allow merchants to follow their business rules and prioritize certain products in search results.

Let us see how popular AI-powered search engines have implemented these core features.

What are some common AI-powered search engines?

Many search engines increasingly integrate AI into their architecture, powering smart SERPs. Here are the 5 most popular ones:

1. Meilisearch

Meilisearch is an open-source, AI-powered search engine designed for lightning-fast, highly relevant, and scalable search experiences.

Built for developers, it offers typo tolerance, filtering, ranking customization, and a user-friendly API. These factors make it a top choice for businesses that require flexible search functionality and high search volumes.

Unlike traditional search engines, Meilisearch uses advanced vector search, NLP, and ML to understand context and intent, ensuring users receive the most relevant search results—even when queries are misspelled, ambiguous, or incomplete.

With ready-to-deploy features such as instant search-as-you-type, relevance scoring, and multilingual support, Meilisearch is particularly useful for e-commerce, SaaS platforms, and content-driven applications.

It integrates seamlessly with various frameworks and databases, allowing businesses to enhance their search functionality without complex setup processes.

Combining efficiency, ease of use, and AI-driven accuracy, Meilisearch offers businesses an affordable yet powerful alternative to proprietary search solutions, empowering them to deliver high-quality, intuitive search experiences.

Key features:

• Fully customizable semantic search
• Tailored SDKs for every framework and language (including React, Laravel, JavaScript, and LangChain)
• Multi-modal search capabilities
• Lightning-fast search speeds (≤50ms response time) even for high-volume applications
• Multiple region support for low-latency and multilingual results

2. Perplexity

Perhaps the most well-known ChatGPT alternative, Perplexity, is an AI-driven search engine that delivers conversational, contextual responses with every search. Using large language models, it generates precise and accurate answers with citations, making it ideal for research and knowledge discovery.

It also offers an interactive chatbot-like Q&A experience, allowing users to refine search queries and receive as relevant information as possible.

Key features:

• Saves and classifies search results for later conversations
• Well-rounded and well-researched answers with references
• Most features are available for free

3. Microsoft Copilot

Microsoft Copilot integrates LLMs into Microsoft’s suite of applications and enterprise solutions, specifically enhancing workplace productivity. It understands natural language queries, retrieves contextually relevant documents, and streamlines corporate workflows.

Backed by Microsoft’s AI infrastructure, Copilot simplifies data discovery, automates repetitive tasks, and delivers smarter search experiences across MS tools like Teams, Outlook, and SharePoint. It also has a chatbot-like interface for quick queries outside these tools.

Key features:

• Integrates with almost all MS apps for a holistic experience
• Generates both text- and image-based responses
• Answers questions and reworks content right in the file

4. You.com

You.com is one of the first search engines to integrate generative AI capabilities in its search system. It prioritizes user privacy while delivering hyper-personalized search experiences. It brings together AI-driven summaries, chat-based search, and multimodal capabilities, allowing users to interact through text, images, and code.

With customizable search preferences, You.com combines ease of use with control over information discovery while maintaining fast, relevant, and ad-free results.

Key features:

• Chat agents for tailored experiences
• Uses real-time data for accurate results
• Enterprise-grade security for full peace of mind

5. Brave Search

Brave Search is a hyper-privacy-focused AI search engine that operates independently of big tech search indexes. Interestingly, Brave's most popular offering is the Brave browser, which offers a built-in VPN, an AI assistant, and an unmatched user interface.

Brave delivers unbiased results, circumvents tracking, and offers AI-generated summaries for enhanced search understanding. With built-in ad-blocking, always-on security, and a focus on user-first instead of profit-first experiences, Brave Search is an excellent choice for privacy-conscious users.

Key features:

• Focus on user data protection and privacy
• Favors relevant results over search-engine-optimized ones
• Delivers results from its independent search index built from scratch

Once you’ve selected, you’ll need to see how to use your choice of AI-powered search engine.

What are some real-life use cases of AI-powered search?

AI-powered search transforms user interactions with digital platforms by truly understanding user intent and delivering the right results instantly.

Take Bookshop.org, for example. After upgrading to AI-powered search, their conversion rate jumped from 14% to 20%—a staggering 43% increase in search-to-purchase conversions.

The key? AI’s ability to handle complex book queries and efficiently navigate its six-million-item inventory ensures users find precisely what they’re looking for.

For Hugging Face, AI search was a game-changer. Discoverability was a challenge with 220,000 AI models, 38,000 datasets, and 60,000 demos. AI search now helps their developers find relevant models based on use cases, performance, and technical specs rather than just keywords, making searches far more effective.

HitPay took AI search a step further, bridging online and offline retail. Their AI-driven point-of-sale system allows sales assistants to instantly locate products across multiple locations while optimizing their e-commerce storefronts.

The result? A 50% increase in search API speed leads to a smoother in-store and online shopping experience.

AI-powered search isn’t just improving results—it’s transforming entire businesses. But how do you factor in the costs of implementation?

How to properly budget for AI search?

AI adoption is growing fast, with enterprises expected to dedicate 10% or more of their IT budgets to AI by 2025. Companies typically allocate 5-15% of IT budgets for AI-powered search, depending on factors like company size, operating niche, data complexity, and customization needs.

Determining AI budget allocation

When planning your AI search budget, consider:

• Search infrastructure scale: Larger, more complex systems require higher investment.
• Data complexity and search requirements: Unstructured or vast datasets need advanced AI processing.
• User base & expected growth: Scaling search for growing traffic adds long-term costs.
• Current AI expertise: In-house AI knowledge reduces third-party reliance.
• Customization level: pricing changes between off-the-shelf and highly tailored AI solutions.

Cost breakdown

While implementing AI-powered search in your systems, you must consider upfront and ongoing costs. Upfront costs include infrastructure setup, data preparation, and integration. Ongoing expenses cover maintenance, updates, and scalability.

For instance, Bookshop.org’s switch from Elasticsearch involved indexing and configuring a six-million-item catalog, requiring technology, time, and resource investments.

HitPay, however, minimized costs by starting with an open-source AI search before transitioning to a managed cloud service, balancing cost efficiency with reliability while keeping scalability at the helm.

Avoiding Hidden Costs

Many businesses underestimate scaling expenses. AI search demands:

• Computing power, especially during training. Consider cloud vs. on-premises and cost vs. performance trade-offs.
• Storage for handling vast data volumes efficiently. Consider upgrading storage for growing load needs.
• Network capacity for ensuring real-time query performance. Consider upgrading your network infrastructure to handle increased traffic.

ROI calculation framework

AI search implementation may have both direct and indirect returns on investment. Think about key performance metrics like:

• Conversion rate improvements (e.g., Bookshop.org’s 43% increase)
• Operational efficiency gains (e.g., HitPay’s 50% speed boost)
• Lower support costs
• Better customer retention

AI search enhances user engagement, reduces churn, and drives long-term growth for SaaS businesses, making it a smart and efficient investment.

How do you measure the efficiency of your AI-powered search?

The key to ensuring your AI-powered search solution is pulling its weight is defining the right efficiency metrics.

Quantifying real search effectiveness

Search volume and click-through rates only tell part of the story. The real measure of success lies in user behavior and business impact.

For instance, Bookshop.org didn’t just focus on search volume—they tracked search-to-purchase conversion. Its increase from 14% to 20% directly indicated boosted revenue.

Decoding search performance that matters

Take inspiration from Hitpay's 50% improvement in search API response speed, which, in turn, improves in-store customer service and online shopping experiences. Gauge search effectiveness by tracking KPIs that reflect real user success, like:

• Search-to-conversion rate: Measures how often searches lead to actions (e.g., Bookshop.org’s 43% boost).
• Query refinement rate: High rates may indicate users struggle to find what they need.
• Time-to-content: The faster users find relevant results, the better.
• Zero-result rate: High rates suggest missing content or poor search optimization.

For e-commerce, success may mean higher order values from search-driven purchases. Conversely, for knowledge bases, it could mean faster response times.

Warning signs: KPIs that signal AI search failure

Problems with AI search often show up as:

• High abandonment rates after search attempts.
• Rising support tickets for hard-to-find content.
• Users bypassing search in favor of manual navigation.
• Decreased engagement with search-discovered content.

When Hugging Face noticed users skipping search and browsing repositories, it signaled a need for better search relevance. Monitoring these red flags helps prevent both engagement and revenue loss.

Better ROIs are usually seen when your application has a good balance of features.

How to choose the right AI search features for your SaaS business?

Understanding your business needs and use cases is the first step to implementing the best-fit solution.

Focus on business needs

AI search should solve real user problems. Bookshop.org’s success came from prioritizing semantic search and typo tolerance, ensuring users could find books even with imperfect queries—leading to a 43% jump in conversions.

Matching features to use cases

Think about the features you need and why you need them. For instance,

• Semantic search helps with large content repositories.
  • Hugging Face’s AI search enables users to find relevant models even if search terms don’t match exactly.
• Similarity search boosts e-commerce.
  • HitPay connects customers with related products to enhance cross-selling opportunities.
• Personalization can tailor results for wide user bases.
  • HitPay lets merchants pin frequently accessed products to adapt search results to business needs.

More features ≠ better search

Adding every feature under the sky isn’t the goal—alignment with user needs is. For example, Hugging Face prioritized discoverability and user needs over implementing every available feature. Similarly, Bookshop.org’s success came from perfecting semantic understanding and typo tolerance, proving that simplicity can drive big results.

Ready to implement AI search into your infrastructure? Let’s see how.

What are the best practices for implementing AI-powered search?

Go all in with your implementation by following industry best practices for AI-driven search solutions.

Start small, scale smart

A phased approach reduces risk and ensures better results. HitPay began with an internal dashboard search, refining its system before expanding to customers, leading to a 50% faster search API while maintaining stability.

Bookshop.org tested its AI search in a sandbox environment before full deployment. This cautious rollout allowed it to make improvements without disrupting performance during peak seasons.

Key implementation decisions

While deploying AI search, think about:

• Prioritizing essential features based on immediate user needs.
• Optimizing data indexing for accuracy and speed.
• Continuously monitoring and refining performance.
• Incorporating user feedback to improve relevance.

Bookshop.org empowered non-developers to adjust ranking rules. Thus, they ensured that the search remained optimized without constant engineering input—a game-changer for business agility.

Now that you have AI-powered search, how do you secure its longevity?

How can you future-proof your SaaS with AI-powered search?

Ensure the longevity of your newfound innovative solution by being grounded in a strong foundation and prioritizing adaptability.

Beyond the AI hype

Not all AI innovations deliver real value. Hugging Face tested vector databases for semantic search before full adoption to ensure alignment with business needs. The key is to evaluate AI solutions based on impact, not trends.

Building for long-term success

AI search should scale with your business, not lock you into rigid systems. HitPay evolved from a basic search to a multi-channel solution, proving the value of adaptable infrastructure.

Bookshop.org structured its search to allow continuous improvements, thus refining ranking rules and adding features without disrupting core functionality.

AI search as a competitive edge

Companies like Bookshop.org, HitPay, and Hugging Face show that smart AI search implementation drives conversions and engagement. How? Choosing solutions that both deliver immediate value and remain flexible for future advancements.

Unlock the power of AI search with Meilisearch

AI-powered search is here, and it’s here to stay. A game-changer for SaaS businesses, it promises improved conversions, user engagement, and operational efficiency. Companies like Bookshop.org, HitPay, and Hugging Face have proven that the right search implementation delivers real results.