For employees, finding the right information needed to perform their jobs has always been a challenge. 

This is because traditional search methods rely on static keywords and basic indexing. So, even with the results that appear from the traditional search, employees would still have to manually sift through each output manually to find contextually relevant information. 

Another problem is that, sometimes, the results that appear could be incomplete, irrelevant, or outdated, stemming from poorly structured data and isolated information.

This becomes even more challenging when enterprise data grows in volume. Without a centralized search system, employees would dwell in the vortex of information search and compromise their productivity.

To save employees from this, enterprise search software emerged as a solution. 

The software addresses these challenges by breaking down information silos, improving search accuracy, and providing  ChatGPT-like experiences for enterprise searches. 

In this blog, we’ve explained enterprise search software, its use cases, benefits, and future prospects and compiled the top 10 enterprise search software you should look for to drive operational efficiency in your enterprise.

Enterprise search software serves as a centralized search tool, enabling all employees to quickly find important information needed to do their jobs effectively.

The software utilizes the power of generative AI and large language models to analyze structured and unstructured data, understand search intent, and deliver accurate information. 

Let’s say the HR team is working on creating a diversity hiring metrics report. The data required by the HR is spread across spreadsheets, emails, and application tracking systems (ATS). 

Without an enterprise search system, the HR person would have to spend hours manually collecting this data from multiple platforms. 

With enterprise search, the HR professional only needs to input a relevant query like “Previous year diversity hiring metrics,” and the search system will understand the intent, find all the information related to this, and surface relevant results in one place.

Enterprise search can be broadly classified into 4 different types. Each type has its own way of retrieving and displaying information within an organization. 

Let’s understand each type of search with a scenario.

Scenario: A customer reported an issue with one of your products, and the customer support agent wants to find as much information as possible to resolve this quickly. The data required is spread across multiple platforms and formats, such as email, documents, PDFs, videos, Slack messages, customer support databases, and a project management tool.

Here’s how each type of enterprise search will function in this situation: 

Siloed search

With siloed search, each data source operates and shows results independently. This requires the customer support agent to search for information manually on each platform. 

So, the agent will have first to search emails for conversations related to the issue. Then, move to Slack to check internal discussions, then support logs to locate the original ticket, and so on.

Results appear only from one platform at a time, which limits visibility and understanding of the issue. To gain full context, the support agent will have to connect information together manually, which is challenging when dealing with complex issues. 

Federated search

In federated search, the agent can send the query once, and the search system will connect with multiple data sources, such as email, chats, documents, and support logs, to look for information simultaneously. 

The results will appear on the same screen and be grouped into original data sources, like email, support logs, and chat, without indexing. 

This saves time compared to a siloed search, but as the results are still grouped by data source, support agents will still have to navigate multiple sections to better understand the issue.

Unified search

Unified search combines all your company data into a centralized index based on relevancy. When the agent performs a search query, it will source information from the combined index instead of searching multiple sources separately. 

The results from this search will be displayed based on relevance.

So, the information from a search query of a product issue reported by a customer will be displayed by:

• Relevant emails discussing the potential fix for the issue

• Support logs with all the customer tickets

• Product troubleshooting documents

Gen AI search

Generative AI, powered by LLMs, takes the enterprise search to the next level. It learns from past user interactions, understands user intent, and provides personalized and context-aware answers to queries. 

Here’s how AI-powered enterprise search will surface information needed to resolve the product issue:

• Surface customer support tickets based on urgency/priority

• Summarize Slack messages from conversations where the issue was first flagged

• Internal documents detailing the ongoing measures being taken to resolve the issue 

• Troubleshooting guide with specific steps to resolve the issue

• Proactive suggestion of past bug reports with similar characteristics to the current issue.

Since the 1950s, artificial intelligence has contributed to major technological advancements, enabling businesses to analyze data, improve operations, and drive growth.

With these advancements in AI technology, generative AI—its subset—has shown immense potential in redefining how organizations search, understand, and use information. 

So, what is the difference between traditional AI search and generative AI search? The key difference lies in their definition. 

Traditional AI is designed to perform specific tasks by following a set of guidelines and patterns. In contrast, generative AI models are trained on a large data set and constantly learn from it to create something new, mirroring their training data.

 Let’s further understand the key differences through a comparison table:

Like how you use Google and Bing for your personal queries, you can integrate the enterprise search software and use it as an enterprise search engine to quickly locate company information across all your departments. 

Here are 7 use cases for enterprise search: 

IT support

IT support agents handle high volumes of queries every day. Enterprise search enables faster query resolution by simplifying access to past tickets, technical documents, system logs, developer wikis, and other relevant information. 

This implementation helps you improve metrics like average resolution time and first contact resolution rate. 

What else? 

Many enterprises are also improving self-service by enabling chatbots with enterprise search features for employees to resolve repetitive, non-complex queries independently. This helps improve employee satisfaction and frees support agents to focus on high-value tasks.

For example, if an employee has a problem with the internet connection and VPN and types “Why is my internet very slow when connected to a VPN?” the system understands the intent behind the query and suggests relevant steps for the employee to resolve it independently.  

HR support

The human resource department is bombarded with redundant queries on employee benefits policy, leave balances, pay slips, and onboarding material. With enterprise search in place, your employees can access such details directly. 

For example, if your employee searches “I would like to know my available leaves,” the enterprise search tool will understand the intent behind this query, look up past leaves the employee has taken, and inform him of the remaining balance. 

Customer support

Customer support agents need a complete history of customers raising support requests. This information includes past chat transcripts, initiatives to resolve customer tickets, product configuration, and the customer’s use case. 

With enterprise search, support agents have immediate access to all this information, which helps them pull relevant information quickly without switching platforms. This helps provide personalized solutions and reduces the time to resolve issues, increasing customer satisfaction.   

Sales enablement 

The sales team can access annual or quarterly sales reports to assess sales performance. Going beyond static reports with chunks of text, the sales team can perform advanced searches to extract more insights with queries like “Give me data on the best-selling product and region-wise sales revenue.”

Website search

You can incorporate enterprise search into your website’s search bar for quick information retrieval. This will help you improve engagement with existing customers or prospects who landed on your website for more details.

For example, a prospect might visit your website to learn more about a certain feature. Instead of having to navigate each webpage on your site exhaustively, they can just search for the particular product feature, which will improve their overall experience. Chances are they might turn into a loyal customer, as you’ve struck the iron when it’s hot.  

Finance and Accounting

Enterprise search enables smoother finance and auditing operations by simplifying access to finance documents.

• Employees can easily locate information like tax filing guidelines and expense reimbursements. This saves time from the back-and-forth between employees and the finance department.

• Employees in the finance department can access financial reports from previous years and other relevant reports while auditing and preparing budgets for upcoming years.

Legal Departments

Enterprise search allows legal departments to manage and access compliance documents, employee contracts, and vendor contracts.

The legal department can search for and organize important organizational information such as trademarks, copyright filings, and patents. 

Enterprise search also enables legal professionals to quickly locate different versions of a contract or policy, compare clauses, and identify changes without the hassle of reading each file.

Enterprise search software eliminates the need to spend hours searching through large piles of documents for information. It helps with quick information retrieval, enhanced self-service, personalized knowledge delivery, and improved decision-making. 

Let’s take a closer look at the benefits of enterprise search software: 

Quick information retrieval

One of the most significant benefits of incorporating enterprise search software is the quick and easy access to information. 

Thanks to generative AI, employees no longer have to waste time hunting for information scattered across the company. It interprets the context and intent behind each user query and delivers accurate results even if the user query contains colloquial terms. 

What’s more? You can search for information and prompt the AI model to produce it in different easy-to-read formats, such as graphs, pie charts, and summaries.

Enhanced self-service

To find information, employees had to constantly exchange information with subject matter experts or employees from other departments. This not only disrupted their productivity but also affected others in the organization.

Enterprise search has eliminated this need by allowing employees to resolve issues seamlessly and independently. For example, if an employee wants to connect to the company network, they can look for this on the enterprise search bar, follow relevant steps, and connect to the office internet instead of waiting for an IT professional to attend to their query. 

Personalized knowledge delivery

An HBR research shows:

• 47% of employees receive irrelevant information from their organization

• 57% of employees and managers receive multiple information on the same topic 

These statistics highlight the need for personalized information, which is precisely what enterprise search enables. 

AI-driven enterprise search tailors the information for each employee based on their job roles and past interactions. Employees can also customize their preferences, enabling the AI to learn and improve search results over time. 

Improved decision making

AI-powered knowledge search can help uncover insights from both structured and unstructured data. This enables leaders to identify potential opportunities better and generate more revenue.

For example, if you’re a product manager and want to improve a certain product. You can use enterprise search to retrieve customer feedback and leverage AI to extract key insights from the feedback to uncover areas for improvement. 

Improved MTTR

Mean Time to Resolution is a key metric that directly impacts customer satisfaction, operational efficiency, and costs. High MTTR means hampered productivity and increased downtime. 

With enterprise search, support agents can access information in a jiffy. This enables them to find solutions to issues and restore operations before it’s too late.

In this section we’ve compiled the 10 best enterprise search software available in the market to help your employees find information without any hassle.

Need a glimpse of all the solutions before the deep dive? Here’s a ready reckoner of the top enterprise search tools for you to evaluate:

Workativ

Workativ is a platform that offers tools to build chatbots with advanced AI capabilities to help you automate IT and HR support, simplify workflows, and improve knowledge management. Workativ’s knowledge AI enables enterprises to harness the power of LLMs and generative AI technology to build an AI-powered enterprise search solution to improve employee support and customer experience. 

 Key features of Workativ’s Knowledge AI:

• Knowledge base integration: Knowledge AI allows you to seamlessly integrate your generative AI bot with your internal, external, and Workativ KB, enabling employees to find all information in one place.  

• Workflow automation: Enterprises can automate a wide range of employee queries by integrating Knowledge AI into conversation dialogue flows to fetch instant answers to simple and complex queries.

• Live agent handover: Knowledge AI bot can smoothly escalate queries to live support agents, providing them with the full context of the query and chat history for faster resolution. 

• Easy deployment: Enterprises can customize the bot’s appearance to match their branding and deploy it in their preferred channels, such as MS Teams, Slack, and website portals. 

• Search analytics: The platform offers you an intuitive dashboard to maximize the use of the Knowledge AI bot. Users can get insights into number of tickets raised, number of queries answered using Knowledge AI, track unresolved queries and more. 

Workativ streamlined information search for GoTo, a company that provides a suite of SaaS tools for remote work, collaboration, IT management, and customer support. Our platform helped the GoTo team retrieve information quickly, resolve queries faster, improve metrics like FCR and MTTR, and save additional costs. 

Pros:

• Users report that Workativ provides an intuitive interface. 

• Users state that the platform’s pre-built templates and workflows enable quick deployment, going live within hours.

• Users mention that the platform provides extensive features to build chatbots and automate app workflows, offering flexibility to meet diverse business needs.

Cons:

• Training the chatbot with advanced configurations will take some time. 

• Users report minor issues on the platform but say they can be resolved quickly by the customer support team.  

Glean

Glean is a platform designed for knowledge management and information retrieval. It is used in large enterprises to simplify access to knowledge spread across multiple systems in multiple formats, such as texts, audio, and video.  

Key features of Glean:

• Personalized search results: Glean uses artificial intelligence to understand user patterns and interactions to produce highly personalized results.

• Prompt library: The platform offers a prompt library that contains various pre-built prompts, enabling you to use Glean chat more efficiently.

• Integrations: Glean connects with 100+ popular apps, including GitHub, MS Teams, Bamboo HR, Gong, and Jira.   

Pros:

• Efficient in handling search queries

• The platform enables seamless collaboration across teams

Cons:

• Users state built-in analytics features are inflexible, with room for date filtering and reporting improvements.

• Users say the Glean search lacks a search-by-date filter to sort search results, reducing user control and clarity in search results.

Guru

Guru is a leading knowledge management platform that helps businesses easily capture, organize, and access organizational information. It offers enterprise search solutions to various industries, including technology, IT services, finances, sales, and marketing. 

Key features of Guru:

• AI-search across all platforms: Guru connects with your workplace apps, such as Slack, Salesforce, and Google Workspace. This enables the built-in AI model to retrieve the right information at the right time. 

• Browser extension: The platform offers a browser extension so that users can find information without the hassle of switching multiple systems.  

• In-app knowledge creation: Guru allows users to create and verify content within the platform, keeping knowledge updated and easily accessible.

Pros:

• The platform offers highly effective search functionality that delivers accurate results.

• Users report that the platform is intuitive, visually appealing, and has a simple design that is easy to learn and navigate.

Cons:

• Users state high time and effort are required for initial setup and ongoing maintenance.

• The platform offers insufficient visualization or accessibility of bookmarks for frequent users.

Algolia

Algolia is a widely known software that offers AI-powered solutions, such as AI search, AI browse, AI recommendations, and more, for enterprise websites that primarily serve customers rather than internal employees.

Key features of Algolia: 

• NeuralSearch: Algolia combines semantic search with keyword search. This enables users to find precisely what they’re looking for. 

• Dynamic Re-ranking: This feature uses AI to identify trending content based on user interactions and boosts the ranking for more engagement.  

• AI Synonyms: Agolia analyzes user inputs and automatically detects potential new synonyms to match user queries, making the search easier. 

Pros:

• Users state that the platform integrates effortlessly with other composable technologies, facilitating collaboration across product, marketing, and engineering teams.

• Users report that Algolia provides in-depth user search analytics, offering insights into click-through rates (CTR), product views, and search performance over specific periods. These analytics help them refine their search strategies using data-driven decisions.

Cons:

• Users lack call support, even with premium plans. 

• Users report that Algolia’s pricing and cost scalability can be challenging to understand. This lack of transparency makes it difficult for them to forecast expenses as projects expand.

Elasticsearch

Elastic is an open-source search and analytics engine that helps developers create customizable search applications. It is designed to handle large volumes of data, indexing and querying.

Key features of Elastic: 

• Search capabilities: It supports full-text, semantic, vector, and hybrid searches and integrates features like geospatial search and retrieval-augmented generation (RAG) for advanced use cases.

• Generative AI integration: Enterprises can integrate Elastic with Gen-AI to build mobile and website portals, slackbot integrations, and other employee experiences. 

• Customizable search UI: Elastic offers pre-built user interfaces that can be modified to integrate with your company’s branding. 

Pros:

• The platform efficiently unifies data across the organization and improves the search experience. 

• Users state that the platform has a user-friendly interface and increases productivity to a great extent. 

Cons:

• Users report that the platform takes significant resources on more minor applications.

• Users state the platform can be cumbersome to install and configure, especially for those who are not familiar with Elastic.

Coveo

Coveo is an enterprise search software with AI capabilities that helps businesses improve internal knowledge management and simplify workflows. 

Key features of Coveo:

• Semantic search: Coveo’s search engine uses AI to understand the intent behind user queries and provides context-aware results.

• AI recommendations: The platform uses user behavior analytics to provide personalized content and product recommendations. 

• Generative answering: Coveo reduces the time spent searching for information by leveraging Gen AI, which extracts information from various repositories and documents.

Pros:

• Users report that Coveo simplifies content indexing, including associated permissions, through its out-of-the-box and generic connectors.

• Users state that Coveo's machine learning models and built-in relevance tuning significantly improve content visibility, particularly for extensive technical documentation across large product suites.

Cons:

• Users report that many configurations or changes in Coveo require Salesforce admin access, which creates a dependency on IT teams. This can slow down the process and hinder teams that manage Coveo from making timely updates or adjustments.

• Users state that the platform’s user experience has room for improvement.

Moveworks

Moveworks is a conversational AI platform that focuses on improving employee and customer experience by automating employee support and repetitive tasks and offering intuitive enterprise search capabilities. 

Key features of Moveworks:

• Employee self-service: Moveworks helps employees resolve queries independently by providing a centralized knowledge repository and quick information access using AI. 

• AI-chatbots: Moveworks extends enterprise search by integrating search capabilities in AI chatbots. This helps in offering real-time assistance to employees by answering repetitive queries. 

• Search analytics: The platform provides granular insights into search patterns to understand gaps, trends, and content quality.

Pros:

• Users state that Moveworks’ AI-powered prompts and suggestions provide relevant and actionable answers, enabling employees to address queries independently.

• The platform provides an intuitive user-interface.

Cons:

• Users express concerns about controlling data access levels and restricting queries that may expose sensitive data. 

• Potential challenges in extracting information from knowledge base articles. 

Pinecone

Pinecone is a platform that offers developers a scalable search application solution through a vector database and search service. It offers various features like metadata filtering, hybrid search, and live index updates and integrates with popular data sources, frameworks, models, and more. 

Key features of Pinecone:

• Filter by metadata: Users can combine vector search with familiar metadata filters to get more relevant results. 

• Live index updates: The platform provides real-time index updates for vector databases. As your data changes, it automatically updates to maintain accuracy and relevancy.

Pros:

• Users report that Pinecone offers a simple API and lean management interface, making integrating, deploying, and scaling easy.

• Users report that Pinecone’s support team is highly responsive and effective, resolving issues quickly and courteously.

Cons:

• Users state that the platform lacks robust security features, such as Multi-Factor Authentication (MFA), for critical actions like deleting indexes or projects, increasing the risk of accidental data loss.

• Users mention that Pinecone has a steep learning curve and lacks integrations with broader ecosystems, making it harder to use alongside other tools for more complex workflows.

IBM Watson Discovery

IBM Watson Discovery is a search solution that uses natural language processing to search and analyze large amounts of structured and unstructured data. Its search capabilities enable enterprises to find information quickly, extract key insights, and make improved decisions. 

 Key features of IBM Watson Discovery:

• Smart document understanding: This feature helps users label texts so that machine learning algorithms can understand critical components from tables, headers, and more. This helps in retrieving more relevant answers.    

• Optical Character Recognition (OCR): This feature accurately recognizes and extracts texts from images. The platform can give you key insights from sub-optimal documents such as images with irregular fonts or poor resolution. 

• Domain-specific entities: Enterprises can define custom entities, which the platform's ML algorithms use to extract data more effectively. 

Pros:

• Users say that the platform makes it simple to connect with and analyze data from various sources, including HTML pages, PDFs, and documents, without needing complex setups.

• Users report the platform’s NLP capabilities are excellent for extracting insights from unstructured data like text documents, emails, and social media posts.

Cons:

• Users report the service can be expensive due to its cloud-based subscription model.

• Users state that the initial setup can be complex and requires technical expertise. Users also experience a learning curve to fully understand and utilize the platform’s capabilities.

Aisera

Aisera is an industry leader in Generative AI solutions that help enterprises increase employee productivity, improve business operations, and generate more revenue. Its products include AI Copilot, Agent Assist, AI voice bot, and AI ops. 

Key features of Aisera

• Personalized search: The platform provides personalized search results information based on the user’s role, context, past interactions, and search history. 

• Autocomplete search suggestions: Aisera offers predictive search query suggestions that allow users to search faster. This eliminates the need to type every character manually. 

• Access and control: The enterprise can manage access and controls for viewing specific documents. This ensures that the right people have access to confidential documents and secures enterprise searches. 

Pros:

• Users report that Aisera integrates smoothly with other platforms, enabling faster and more accurate solutions for employees and customers. 

• The platform helps users reduce reliance on support agents by efficiently handling a wide range of queries.

Cons:

• User reports that the UI of admin control can be overwhelming for IT teams

• Potential for misinterpreting queries and generating wrong answers

There are plenty of options available in the market for enterprise search software, but you need to be careful when investing in any software. You need to evaluate whether they’re the right fit for your business requirements. You need to know if they have all the functionalities to make information search easier for your employees.

Here, we’ve put together 7 factors you should consider before deciding on an enterprise search software:

Built-in Generative AI

The enterprise search software you choose must go beyond simple keyword matching. Look for platforms that incorporate generative AI capabilities to:

• Synthesize information from multiple sources into easy-to-read summaries

• Interpret natural language queries and provide accurate answers

• Generate insights and recommendations based on search patterns

• Learn from user interactions to improve relevance over time

Speed and accuracy

The goal of enterprise search is to provide the right information quickly. So, evaluate the software based on:

• Accuracy of search results across different content types and languages

• Time taken to respond to user queries across varied data volumes

• Ability to handle the surge in queries during peak times

• Precision of search results for industry-specific terms. 

Ease of implementation

The platform you choose should not be complex to implement. Look for platforms that offer no-code implementation, easy data migration, and scalability.

Another vital aspect is exceptional customer support. The software you choose should provide complete assistance, from initial onboarding to successful deployment. 

User-friendly interface

A powerful search engine is hardly beneficial if your employees find it difficult to use.

Look for platforms with:

• Clean, intuitive user interface that requires minimal training

• Customizable dashboards for different user roles

• Advanced filters to refine the search 

Integrations with existing tools

The enterprise search software you choose should smoothly integrate with your current tech stack for efficient business operations.

Look for platforms that:

• Natively integrate with popular enterprise applications

• Support custom integrations through APIs

Analytics from search data

Today, enterprises are trying to understand what is missing in their knowledge base, what their employees are looking for, and how they can keep information relevant and updated.

So, invest in a search platform that offers:

• Detailed search analytics and user patterns to understand trends 

• Content gap identification to update knowledge

• Search analytics dashboard for a holistic view 

Robust security

While choosing an enterprise search software, you must ensure your company’s data is handled safely, as security can’t be an afterthought in this case.

The software you choose should allow you to manage and control access to important documents. 

Example: For a particular document, you should be able to provide view, edit, and export access to a manager and only view access to a newly joined employee. 

Today, employees expect the same search experience as when they use search engines like Google and Bing to search for their personal queries with minimal effort. This has set the bar high for enterprises to provide seamless search experiences to their employees.

With the immense potential of artificial intelligence and its building blocks, the future of enterprise holds the key to meeting these expectations and capabilities to reshape how enterprises organize, access, and use their data. Here’s how:

Large language models (LLMs)

LLMs have enhanced the search experience for employees and customers by allowing them to engage in natural language conversation. In the future, large language models will become more advanced in question-answering capabilities by providing users with precise and context-aware answers in multiple formats. 

Voice search capability

A study reveals that by the end of 2024, 8.4 billion voice assistants are anticipated to be used globally. This number is higher than the global population. 

Automatic Speech Recognition models like OpenAI’s Whisper and Facebook’s Wav2Vec 2.0 have greatly improved transcription accuracy and voice search capabilities across industries providing transcription services, call centers, and more. 

In the enterprise context, integrating voice search with an enterprise search system would improve employee productivity and satisfaction. Your employees can perform hands-free searches, access important data on the go, and focus more on high-value tasks. 

Visual search capability

According to a study, the image recognition market is expected to grow at an annual rate of 8.71%, resulting in a market volume of US$22.64bn by 2030.

Implementing a visual search function in enterprise search systems will enable employees to look for information with visual cues. This is especially helpful when employees want to discover new information but don’t know its search terms.

Another application of image recognition in enterprises is inventory management. Enterprises can use visuals to classify and segment objects in their inventory by assigning specific tags, locations, and other relevant attributes. So, when an employee wants to know the inventory status or identify a product, they can use image recognition to quickly get the information.

Today, just like how popular search engines such as Google or Bing make web search easier for people, employees expect an equally capable enterprise search experience. They expect fast, accurate, and personalized information in one place. 

And Workativ makes this possible for your employees.

With Workativ’s Knowledge AI, you can utilize the properties of large language models and generative AI, upload your enterprise knowledge from multiple data repositories, and build your own Gen AI-powered enterprise search system without any coding.

Want to see how Workativ helps your employees retrieve information faster with minimal effort? Book a demo now.

What is enterprise search software, and how is it used?

Enterprise search software is designed to search for information within an enterprise organization. By integrating the software into the organization database, employees can easily retrieve information from structured and unstructured data like images, videos, documents, emails, troubleshooting guides, and more.

What is the difference between Google search and enterprise search?

Google search is used worldwide by users to find information on the Internet. Enterprise search is designed explicitly to index private enterprise data for internal employees.

In simple terms, we use Google search for our personal queries, and employees use enterprise search engine to quickly find company information such as documents, reports, troubleshooting guides, and more. 

What are the use cases for enterprise search?

Enterprise search can be used by organizations to improve IT and HR support, customer support, sales and marketing functions, website navigation, finance and accounting, and legal department functions. 

What is SearchGPT?

Search GPT is a search engine with AI capabilities developed by OpenAI. It responds directly to the user's questions by providing the latest information from the web with relevant links to the sources. 

You can ask follow-up questions and get more explanations on the topic with citations from relevant sources.

What is AI enterprise search? 

The term “AI enterprise search” refers to using artificial intelligence in enterprise search that helps interpret user queries and searches for information from multiple sources. 

The use of AI in enterprise search enables the faster generation of personalized and accurate answers to user queries.