Search is more than a utility — it’s a business enabler.
For enterprises, search is a critical layer of digital infrastructure. Yet, it’s often treated as an afterthought. A poor search experience doesn’t just frustrate users — it increases IT costs, wastes employee time, and drives customers away.
CIOs and CTOs are under constant pressure to reduce operational costs, drive efficiency, and secure enterprise data — all while improving customer and employee experiences. But traditional search methods, whether simple keyword-based search or even modern AI-enhanced approaches, fail to unify data, personalize experiences, and scale across an enterprise.
Enter hybrid search, supported by unified indexing.
Hybrid search combines lexical, vector-based, and AI-powered techniques to deliver fast, relevant, and secure results — no matter where the data lives. It ensures that employees find the right information, customers get the right answers, and IT teams reduce the complexity of disconnected search systems.
In this article, we’ll break down:
- How search has evolved from simple keyword-matching to AI-powered retrieval (and why strong keyword search is still important, rather than vector methods alone)
- Why hybrid search is the future for enterprises looking to cut costs and improve search efficiency
- How leading enterprises are using hybrid search to drive business results
Let’s dive in.
Search’s Evolution from Keywords to AI
The origin of the internet stretches as far back as the 1960s, but the first search engine? That came much later. It was called Archie. Launched in 1990, Archie was a simple tool that allowed users to find ftp sites by searching for filenames and descriptions.
Other internet directories and text search engines began to emerge a few years later, using a combination of human curation and algorithmic ranking to create a searchable catalog of websites. Here’s a (very high level) history lesson that speaks to how search evolved from keyword-focused to vector-based to hybrid.
Early 1990s: Keyword Search Meets Human Curation
In 1994, two Stanford grad students, Jerry Yang and David Filo, created the online directory that would eventually be named Yahoo! Credited as the first human-edited directory, Yahoo’s human-curated results were augmented by various crawler-based search engines including AltaVista, Inktomi, Google, and eventually its own crawler, Yahoo Slurp. These early search engines relied on keywords (aka lexical search) to determine relevancy. Results were ranked based on the frequency and location of keywords within web pages.
Late 1990s – Early 2000s: Google Comes to The Party
Google entered the fray with a new approach to search, launching in 1998 with its PageRank ranking algorithm. PageRank made it difficult to spam your way into a top search position with keyword stuffing since the algorithm considered the number and quality of links to a page (e.g., backlinks) when determining relevancy. This improved the relevance of search results for Google users, making the search experience much better. It’s a big reason Google became the leader in search.
2010s: Google Goes Semantic and Adds Machine Learning
Google is credited for bringing semantic search to the masses via their Knowledge Graph, a feature launched in 2012. The knowledge graph aimed to improve search result accuracy by understanding the context and semantic relationships behind queries, rather than just matching keywords.
Google’s 2013 Hummingbird update marked another significant shift towards semantic search. It allowed Google to better understand conversational queries and user intent. Two years later, Google introduced RankBrain, which used machine learning (ML) to process search results, further enhancing the relevance by applying factors like searcher location, and “true intent.”
2020s: AI-Powered Search
By the 2020s, the integration of AI models and technologies was baked into top search engines like Google and Bing. AI including natural language processing, large language models, and deep learning were incorporated into existing keyword-focused and semantic technologies. In late 2022, ChatGPT introduced users to the concept of conversational search interfaces and the marvels of generative AI (GenAI). GenAI features were quickly adopted by major players in search and tech including Bing, Google, Meta, and Baidu.
What Is a Hybrid Search Engine?
As the above timeline shows, hybrid search didn’t instantly appear. It’s taken decades for what started as a simple utility to become a critical piece of infrastructure. Here’s a breakdown of each of the pieces of the hybrid search puzzle including how they work.
Lexical/Keyword Search
Lexical or keyword search works by splitting user queries into individual words and matching them against the keywords on a web page using Boolean logic (and, or, not, and parenthetical groups). This makes lexical search good at finding exact matches, but not-so-great at interpreting the nuance of language and user intent. Lexical search looks at strings of words versus analyzing concepts, meaning and the relationships between words.
Lexical search gets tripped up by synonyms and complex queries. While this can be mitigated somewhat with stemming and thesauri, it’s not a substitute for the kind of contextual understanding enabled by semantic search and AI.
When looking at a common ecommerce use case – a shopper searching for something vague that’s not 100% represented in your product catalog – lexical search tends to miss the mark. A search for “comfortable walking shoes” may return no results even when your store sells dozens of sneakers with the term “all-day wearability” in the description. That’s because lexical search can’t bridge the gap between what that shopper really means (I want to be able to wear my sneakers all day) and the keywords used to describe the products in your catalog.
Semantic Search
Semantic (vector-based) search is much better at connecting that shopper with the comfy shoes they seek because it uses ML to understand related concepts behind a user’s query. It uses a process called embedding to convert words, phrases, or entire documents into lists of numbers (vectors). These vectors represent the semantic meaning of the content in a multi-dimensional space – the “vector space”.
Similar concepts are positioned closer together in this space. So “comfortable” and “sneaker” might be situated closer together than “comfortable” and “pointy toe pumps”. In the above image, “cat” is closer to “kitten” than “dog”, but “cat” and “kitten” are much closer to each other than they are to “king” and “queen”.
The semantic search system uses these vector relationships to understand the context of words – the meaning, intent, and connections that are the foundation of how we understand language.
Vector Search
In vector search systems, both sparse and dense vectors are used together to leverage the strengths of exact keyword matching and semantic understanding.
- Sparse Vectors are long lists of numbers with mostly zero values, representing specific keywords in a document or search query. They’re used for exact keyword matching, allowing for precise retrieval of documents containing specific words.
- Dense Vectors use distance metrics to interpret the semantic meaning of words, phrases, or entire documents. They use numerical representations of words to help the search engine understand context and intent. This allows them to find relevant results without exact keywords being present.
The short of it is: semantic search can understand synonyms and handle ambiguous queries. That makes it much more likely for a vector search result to contain sneakers with “all-day wearability” when your search for “comfy walking shoes” doesn’t contain any of those keywords.
Search engines use vector and semantic techniques to retrieve results based on conceptual similarity. That is, the complex relationships within words, versus attempting to find content with exact keyword matches.
Hybrid Search: The Best of Search
Hybrid search combines the precision of lexical search with the contextual understanding of semantic search and sprinkles AI into the pot. AI enhances traditional keyword-focused approaches, particularly in the areas of personalization and predictive search, by allowing the system to understand user behavior patterns and create “digital twins.” It can learn from one user’s successful interactions to improve results for similar users.
If two users – Marge and Homer – exhibit similar search behaviors, the system can leverage Marge’s successful past interactions to improve Homer’s future search results. By continuously learning and adapting from collective user search behavior, hybrid search systems provide increasingly relevant results.
The AI component also allows for more sophisticated handling of natural language queries. Hybrid search technology uses AI to interpret these queries more accurately and, in the case of conversational interfaces, use GenAI to provide more natural human-language results.
What are the Security Considerations for Hybrid Search?
Hybrid search systems protect sensitive information by inheriting and enforcing existing access controls and permission settings across various platforms and data sources. Users only see the information they’re authorized to access, even when searching across multiple systems with different permission structures.
In an example of an HR use case, the search system can differentiate between long-term employees and new hires, providing personalized results for questions about benefits or vacation time based on the user’s specific entitlements.
In the context of customer support, the system can distinguish between different tiers of customers or support agents, ensuring that premium features or sensitive troubleshooting information is only displayed to authorized users. This granular control allows organizations to maintain data security while still providing highly relevant and personalized search results.
What are the Benefits of Using Hybrid Search?
Keyword search was enough, for a while. Then semantic search, with its sparse and dense vectors, expanded the capabilities, making search results much more relevant and intuitive. But as the enterprises – and searchers – continue to evolve, search must evolve too. New devices, touchpoints, and an unfathomable amount of information require the help of AI. The way we search is different too.
Voice search and voice assistants have taught us to use more natural language when searching for information online. These conversational queries are often more ambiguous and complex than one-or-two-word searches, posing a challenge for both lexical and vector-based systems.
Hybrid search models address this complexity by combining the following approaches to better understand user intent and context:
- Keyword-based search for precise matching
- NLP to understand semantic meaning and context
- Vector search that goes beyond exact keyword matches to find related concepts
- ML to improve relevance over time based on user behavior
6 Benefits of Hybrid Search
Hybrid search offers significant business benefits as well, including:
- Increased Self-Service: Hybrid search improves self-service resolution because it makes it much easier for customers to find answers on their own. Coveo client Xero boosted self-service resolution by 20% in the first three months of implementation.
- Faster Resolution Times: Coveo’s clients have observed up to 40% reduction in time to resolution. Customers find answers more quickly with hybrid search either on their own or because agents can get the answers they need much more efficiently.
- Cost Reduction: Hybrid search reduces costs by deflecting cases from reaching human agents. This can save millions of dollars on support costs since even a 5% increase in self-service can translate to substantial savings.
- Improved Customer Confidence: GenAI has some famous limitations, particularly when it comes to making up facts and statistics (e.g., hallucinations). But hybrid search can instill confidence in GenAI-powered search tools by reducing or eliminating hallucinations, citing sources for generated answers, and connecting to your approved and vetted company knowledge base.
- Efficient Employee Onboarding: Since information is contextually relevant based not only on the context of the search, but the context of the user, hybrid search is an incredibly powerful training tool. It makes the lives of new hires easier by making it far easier for them to self-serve from the company’s internal knowledge base, accelerating their onboarding and confidence.
- Adaptability Across Channels: The same hybrid search functionality can be implemented across various customer touchpoints, making self-service experiences consistent, coherent, and relevant. And the same unified index can be used to power search, discovery and generative experiences across the entire enterprise.
Making the Case for Hybrid Search
Every time a customer contacts your organization, you’re likely paying for someone to answer their question or resolve their issue. If your internal staff struggles to find the right information, it takes even longer. But if that question can be answered through a self-service channel, you’re saving everyone time, providing a much-needed service, and making use of your company’s expansive knowledge base.
Eric Immerman, Practice Director of Search and Content at Perficient, notes,
“What we’ve seen for many business leaders is that, from a financial perspective, this is about making the contact center more efficient. It’s about having better-informed agents who require less training. Fundamentally, it’s about reducing costs while improving service quality.”
What we’ve seen with our own clients is that organizations implementing hybrid search are typically using 10-20% of their content to generate answers, successfully addressing 30-40% of incoming queries with automated responses. So, while hybrid search is not a wholesale replacement for existing systems, it’s clearly a powerful tool that complements and enhances current capabilities.
Learn more about how Coveo’s hybrid search platform can unify and enhance your existing tech stack:
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