What is
Intelligent Search?

Intelligent search refers to the process of locating and accessing information as quickly as possible, no matter where it is stored. Intelligent search is facilitated using cognitive technologies such as artificial intelligence (AI) and machine learning (ML) to provide the user with customized and accurate search results.

Intelligent search helps with the elimination of data silos. It enables employees and consumers to access information quickly and easily. End users can utilize intelligent search to retrieve content from wherever it is, inside or outside the organization, and in any format, document management systems, database, or web page.

Why Do Companies Need Intelligent Search?

People are bombarded with a massive amount of digital information in their daily lives. Imagine how much data an enterprise with thousands of customers has, and the demand for it. Unless organized in some form, customers would need to sift through an astronomical amount of content on the website to find what they are looking for. We’re living in an information overload reality, which means intelligent search is a necessary tool that makes life easier for users, as well as the business.

Intelligent search is designed to understand the user’s goals and provide results that are based on the inference of those goals. This means that intelligent search goes way beyond providing matches for direct queries. It harnesses some elements of semantic vector search, plus the power of ML technology to build a picture of the goal a user may have when conducting a search.

A user might type “peach cobbler” into a search bar. The questions that come to mind are: Is the user looking to order peach cobbler from a restaurant? Are they looking for the recipe? Maybe they want to know the history of the dish. If lexical search methods were relied on (a search method that looks for exact matches in a database), the results would be varied, and probably won’t be precisely what the user wanted.

This is where intelligent search comes into play. It takes a range of various signals provided by the customer to deeply understand the actual intent of the search. Was the customer just searching for another dessert recipe? It’s likely this will be a recipe search too.

How Does Intelligent Search Work?

Step 1: Ingest data, connect data sources.

Before answers or insights can be obtained, the data needs to be crawled and connected. This includes both structured and unstructured data. The need to use a connector to plug-in to a source of content, which could be an internal dataset, program, or external source.

Step 2: Index the content to create a single unified search index.

The system intelligently indexes all the content to make it searchable. This must be done in a manner that demonstrates understanding of the content and how the search was performed.

Step 3: Enrich the content.

By using entity extraction and sentiment analysis, the AI orders, categorizes, and identifies the key content.

Step 4: Analyze content and create semantic correlations.

The AI looks at the content, its context, and then forms connections between the individual content pieces.

Step 5: Deliver accurate and relevant outcomes.

Answers and/or insights are delivered quickly. They are the answers the user was looking for, even if they did not ask in the most logical fashion. Even better, the answers are ranked from most appropriate or relevant to least.

The Evolution of Intelligent Search

Enterprise information retrieval systems are not a recent phenomenon. They came into existence long before the public started using the internet. Back when multi-user mainframe computer systems were implemented, one of the most significant advantages was that they facilitated the discovery of information by precisely matching search queries to text strings housed in large repositories of documents.

This was followed by the era of corporate intranets, ubiquitous desktop computing, and commercial enterprise solutions for search. In quick succession came the rise of web search engines such as AltaVista and Google, which were free and publicly accessible. These search engines revolutionized information retrieval and content discovery, transforming what users expected from search engines. They also transformed enterprise search platforms.

Enterprises were already facing rapid growth in not just the volume of data but also the variety of content that search tools were required to examine. The result retrieval speed became a key performance indicator of cognitive search algorithms.

The volume of modern information is absolutely immense. It is necessary for the intelligent search solutions of today to be built on architectures that can easily handle the demands of big data workloads. This is why cloud-driven infrastructures that have extensive API-driven integrations, as well as automation, are the top choice for enterprise search solutions. They deliver the necessary scalability, and speed, in our increasingly big data-driven world.

The Mechanics of Intelligent Search

The three key processes involved in how intelligent search understands the goal and intent behind a search query are ML and natural language processing (NLP), and semantic vector search. NLP and ML both work to enable the prediction ability and provide tailored search results for every individual user. Semantic vector search, on the other hand, gathers knowledge from the behavior of the users and encodes products and queries in a shared vector space. In simpler terms, think of it as the way groceries are organized on shelves in a supermarket or grocery store.

These methods connect diverse data sources and index them for easy discovery and querying. The built-in algorithms then provide the right answer when queried. The steps to these include:

1. Connecting various data sources, for example, CRMs, document management systems, or cloud-sharing solutions
2. Indexing the content through a unified search index that allows the ranking of search results no matter their source
3. Intelligent understanding of documents by extracting information to identify and classify them

These three technologies work in tandem to create the intelligent search experience that a user gets.

Why is Intelligent Search Necessary for Creating a Meaningful Digital Experiences?

People expect the answer they want when they want it, which is generally instantly. The sheer amount of information impacts customer service agents, because they now have more customer information to navigate, along with an increasing number of tickets. Maintaining the quality of customer service requires an ability to wade through a ton of information quickly.

Intelligent search solutions help with these challenges in two ways:

1. It enhances the speed at which customers can find the information they need, and the speed and quality of help the customer service agent is able to provide.
2. It provides an additional layer of understanding and intent to the search, which means the experience of both the customer and support agent is augmented by intelligent search.

The Benefits of Intelligent Search Within an Organization

There are significant benefits of intelligent search within an organization. With intelligent enterprise search in place, the workplace can be transformed into something that enables team members to work with improved accuracy and efficiency.

Faster information retrieval

Enterprise search software facilitates the retrieval of both structured and unstructured data in businesses. It gives employees access to information that is stored in almost all systems and data sets used in the business. This allows for faster information retrieval and a more productive way for them to consume and engage with information.

Improvement in efficiency and enhanced data access

According to the statistics, about 60% of knowledge workers use at least four or more systems to gather the information they need to execute their daily tasks. Another 14% use 11 or more systems.

With a unified search experience, all information is brought together in one place and made accessible. Employees will not need to bounce between separate systems to search for the documents they need. This means intelligent enterprise search allows for improved efficiency in the organization by locating documents in any repository using one tool.

Improved accuracy

Having a single source of truth is the only way to ensure accuracy. Failing that, using intelligent search to collect all relevant information and present it to the user allows them to find the correct solution. Intelligent search enables data silos to be bridged, which can uncover answers not previously found.

Better insights

Employees are empowered and given access to the data they require to accelerate research and design processes, make data-driven business choices, and boost employee productivity. Also by removing data silos, it ensures all information is collected, with no missing or error-prone information.

Better decision-making

Employees will feel empowered with easy access to information. They will learn more quickly how to make sound decisions for the benefit of the organizations.

For example, when customer service teams are encouraged to use intelligent document search. agents will have more confidence speaking with customers because they will have crucial knowledge of how to resolve their queries.

Business data security

Because company data is so critical to organizational success, businesses must take extra care to protect it.

Document security involves the protection of data across its entire lifecycle, from creation to disposal, including secure archive storage.

Intelligent search tools improve the security of company data by leveraging a secure platform that is easily customizable. They have complete control over who has access to the sensitive data that is important to the business. Information is managed so that only the people eligible to see it have access. For instance, customers can’t see internal information, and employees can’t access HR information.

Traditional search vs. intelligent search: A clear winner

For almost everyone, Google is the most relied upon source of information on the internet. Regardless of the website that provides the answer to our search queries, Google is trusted as the most accessible source of information readily available. Organizations need a similar kind of trust in their enterprise search systems.

Traditional search was useful for a long time. But data has increasingly become unwieldy and will continue to grow exponentially. Traditional search simply cannot keep pace with this growth of data complexity. Specifically, the need to integrate immense volumes of information systems across organizations is where legacy search methods fail.

Intelligent search, on the other hand, can connect almost every data source and index it, offering users the most relevant results to their search queries. Traditional search uses search queries to find a match, but intelligent search tries to determine what the user is looking for and then ranks the search results based on the user’s implicit preferences.

Organizations cannot use traditional search engines to seek answers to business-specific queries. For example, where is the new product shipment? What were the top customer-related challenges last week? This is where intelligent search comes in. Unlike web search engines, intelligent search can answer queries that are specific to the business.

AI is the difference between general search results and specific, helpful, and targeted answers. AI uses a variety of tools to enable the most useful results.

Understanding of human language

Business data is updated on a regular basis and generally provided in a domain or industry-specific terminology. Intelligent search software can analyze and query digital content from numerous data sources thanks to NLP capabilities. Intelligent search also makes use of both semantic search and contextual knowledge to identify and index linguistic nuances or synonyms or any other relationships present in everyday language as well as within complicated documents.

Comprehension of document structure

AI can understand that there are a range of data sources and types of files. Intelligent search can use ML to learn the type and layout of documents particular to the company, industry, or domain space. Intelligent search then quickly learns and identifies items such as headers, footers, charts, and tables using this understanding. It can recognize document types such as contracts, purchase orders, and invoices right out of the box.

Embracing machine learning (ML) for better results

ML and deep learning generate seamless, instant query suggestions and continuously enhances search query result relevancy over time. This allows intelligent search to predict what information will be most useful to the users.

Filtering the results of the search for clarity

Faceted and filtered searches narrow the scope of data collection and uncover specific information which could have otherwise remained hidden.

Classification and categorization of content

Entity extraction finds and categorizes text data items such as the names of people, products, object types, or organizations into predefined categories.

Advantages of Intelligent Search

Enable users to discover insights and make well-informed decisions.

Even unstructured text data has insights waiting to be discovered. Intelligent search applications use NLP to extract meaning and find correlations across various data sources such as social media, customer feedback, e-commerce reports, and maintenance records.

This gives access to real-time insights with speed and precision. The result? Better decision-making.

Empower employees with the right knowledge.

To give a simple and team-wide user experience, deploying intelligent search helps to construct an enterprise search platform. This encompasses knowledge management, content management system, and question-answering systems and helps to ensure the right answer, every time.

Deliver customer service on a large scale.

When organizations give consumers the correct answer every time, they will have a better experience. Customers expect more than just FAQs. Now more than ever, they want to be able to fully self-serve on websites and mobile applications. Including virtual agents and intelligent search enables the consumers to attain independence. Customers who are self-sufficient result in lower support costs and increased customer satisfaction.

Challenges with Intelligent Search

Like any technology, intelligent search is not perfect. While the advantages outweigh the negatives, there are factors organizations must consider when implementing their new system. Businesses must manage these weaknesses and problems, developing strategies that minimize or negate them.

High cost to entry

AI-powered search engines are more expensive to develop and maintain than their traditional counterparts.

Can be biased

If an intelligent search engine was trained on data that was biased, it can lead to inaccurate or misleading search results.

Privacy concerns

Personal data is collected and used by AI-powered search engines, and this could lead to privacy concerns.

Intelligent Search in the Future

AI-driven search systems are constantly learning from past searches. This means organizations can expect to see intelligent search constantly evolving as it continues to learn from the natural speech patterns of users and trying to determine the meanings of each inquiry.

Organizations and individuals have already witnessed the leap from mere text search to voice and graphical intelligent search. It’s also expected that there will be an increase in the use of voice with intelligent search to enhance the accessibility of information.

As voice assistants and smart speakers become more common, voice-activated search capabilities will be the future of intelligent search. AI and NLP technology will continue to advance, interpret, and respond to voice searches, making intelligent search more accessible and frictionless.

Finally, visual search, which allows users to search for information using images rather than text, is gaining popularity. Intelligent search systems will be able to evaluate and interpret visual content, offering appropriate results based on picture recognition and understanding.