Do you often spend hours looking for specific information, documents, or reports before every meeting, sales call, or project deadline? You either never find the document or waste hours looking for it.
We've all been there: searching a specific email, digging through shared drives, and frantically messaging colleagues.
All these happen because many organizations don’t have a centralized search system. As a result, employees struggle with
- fragmented data silos
- time-consuming manual searches
- missed opportunities due to a lack of the right data
A great way to address these challenges is by turning to an enterprise search software. According to 6Sense's report, over 5000 companies across multiple industries now use enterprise search software to access & organize data in seconds.
In this blog post, we’ll discuss everything about enterprise search software, its benefits, types and key features.
Let’s get started.
What is Enterprise Search Software?
An enterprise search software is designed to help organizations quickly locate and access information across various data sources. It provides a unified search experience by indexing and consolidating data from disparate systems such as documents, emails, databases, and intranet sites.
Consider it as a personalized Google for your workspace. You enter a query in the search bar and get the information within seconds.
Benefits of Enterprise Search Software: Employees and Customers
An enterprise search software has two different user groups: employees and employers. Both of these user groups have different perceived benefits from the software:
Benefits for employees
Boost Employee Productivity: The ability to access necessary data within seconds fuels your team’s creative energy in the workplace, allowing them to maintain a smooth workflow.
Seamless cross-departmental collaboration: Enterprise search software acts as a centralized data hub connecting various departments to a single source of information. It allows employees to stay up-to-date on product launches, campaigns, etc., without contacting colleagues.
Retains an organization’s database: Enterprise search software indexes knowledge and data in real-time, ensuring it’s always up-to-date. So even when employees move to new roles or leave the company, their work insights remain accessible to others. As a result, the company's operations will run more efficiently with minimal knowledge gaps.
Benefits for customers
Crafting personalized Service for customers: AI search software enables companies to deliver personalized service by instantly retrieving customer-specific information. Support teams can tailor their responses and services to meet individual needs using the customer's past insights. This personalized approach enhances customer experience and builds stronger, more loyal customer relationships.
Resolve repetitive customer queries faster: When your sales and support teams can access customer profiles at their fingertips, they can quickly identify and address their issues. AI search software captures insights from customer conversation histories and support tickets, summarizing the essential data in one go. This means solving repetitive queries faster based on past resolutions.
How Does an Enterprise Search Software Work?
An enterprise search software follows a structured process to deliver fast, accurate, and relevant search results.
Here's how it operates:
Data collections and integrations
First, The software must collect data from the company's intranet and various integrations to conduct searches across various platforms. The data is collected using two methods:
Connectors
The internal database uses connectors. Consider a connector as the bridge between the software and different data sources. It connects with the company's intranet, on-premise database, and internal file storage systems. Then, it pulls data to index it in the software.
APIs (Application Programming Interfaces)
The software uses APIs to integrate and fetch data in real-time for external applications. These APIs connect with CRM systems (like Salesforce and Hubspot), email servers (like Gmail and Outlook), and other platforms.
This combination of connectors and APIs organizes data into centralized, searchable indexes, ensuring quick and accurate retrieval. This integration allows seamless access to internal and external data sources, enhancing efficiency and productivity.
Indexing
Once the data is collected, it undergoes indexing. Indexing is the process of organizing data into a structured format that is easy to search. It is done using connectors and APIs. During this process:
- Documents are broken down into smaller pieces, such as paragraphs or sentences
- Then, the data is stored in indexes or rational bases
- Further, the search engine extracts and catalogs keywords and metadata
Overall, indexing organizes and manages data efficiently to enhance its findability. The process also uses advanced NLP techniques to accurately understand and categorize the content.
Query Processing
After the data has been indexed, end-users can use it. Employees can search for files, documents, reports, etc., to receive answers based on their authorizations or permissions. At the same time, the customers can ask questions about products and businesses or search for frequently asked questions.
When a user enters a query, the software processes it to understand the intent and context. This involves:
- Parsing the query using NLP to identify key terms and phrases.
- Applying search algorithms to interpret and match the query with relevant indexed data.
- To refine the search results, consider user context, such as role and previous search history.
Types of Enterprise Search
Here is a brief breakdown of four different types of enterprise search algorithms:
Siloed search
The siloed search uses a fragmented approach to data retrieval within an organization. It's mainly deployed in companies where data is isolated in separate systems or departments. With this enterprise search, employees must search for information independently across multiple platforms.
Top Features
- Enables departments to handle their data independently
- Minimizes the challenges involved in running a centralized system
- Simplifies implementation and maintenance within individual departments
- Restricts access across departments
Example: A manufacturing firm uses siloed search to maintain departmental data control. HR uses one tool for employee records, and finance uses another for invoices. This ensures independent data management and simplifies maintenance.
Federated search
Federated search allows users to search multiple databases or other data sources simultaneously. When a user enters a search query, federated search engines search across all data sources to combine the results into a single search result.
Top Features
- Reduces the time spent searching multiple systems individually
- Searches across multiple databases simultaneously
- Combines results into a single set
- Uses connectors to access different data sources
- Relies on metadata for search results
Example: A national library uses federated search to query multiple databases simultaneously. Users get a unified set of results from diverse sources. These include local archives, national repositories, and online collections.
Unified search
A unified search lets users find information across different data sources at once. It combines results into one index, making the process faster and simpler for a more complete search experience.
Top Features
- Uses advanced technologies and algorithms to provide the most relevant information to the search query
- Supports and produces answers in multiple languages
- Supports large volumes of data and user numbers without losing efficiency
- Updates data in real-time to provide the most up-to-date information
- Searches through different types of data: documents, spreadsheets, images, and other multimedia files
Example: A consulting firm employs a unified search to access various types of information. Employees can retrieve project reports, emails, and financial data from different platforms. All results are provided in a single search interface, ensuring easy and efficient retrieval.
📚Note: Users often confuse federated search with a unified search system. However, there's a significant difference in their data integration methods. Both search systems allow users to search through multiple data sources from a single interface. However, in federated search, the system scans multiple independent data sources that do not communicate with each other.
This means updates in one source might not be reflected immediately across others. Meanwhile, a unified search scans a single index that captures all the data from multiple sources. These data sources are integrated, ensuring real-time updates are reflected across the entire system. This provides a more consistent and up-to-date search experience.
AI-Powered search
An AI-powered search tool is designed to manage large volumes of data and its access in organizations. It retrieves information based on user intent, past searches, and contextual understanding of the query. AI search engines integrate with your existing applications, such as Gmail, OneDrive, Slack, Hubspot, and more, to deliver the most accurate results.
Top Features
- Provides contextually correct answers by using NLP, ML, indexing, and semantic search
- Identifies patterns and connections in data to improve search relevance over time
- Continuously adjusts and refines search results based on user behavior and interactions
- Offers insights that traditional search methods might miss, such as user intent, contextual relevance, and emerging trends
Example: A tech startup uses AI-powered search to streamline team collaboration. When an employee searches for a client's project details, the AI quickly finds relevant emails, documents, and chat messages from Gmail, OneDrive, and Slack. This speeds up access to crucial information and enhances productivity.
Key Features to Look For in an Enterprise Search Software
Looking for AI-powered search software for your organization? Look for the following features to get the best results.
Data security
Enterprise search software must offer robust security and compliance, as sensitive data is also exposed during indexing.
For data security, look for software that offers antivirus software, firewalls, data encryption, and access controls. To maintain legal standards, look for software that complies with industry standards such as GDPR and HIPAA.
Search capabilities
Consider natural language processing (NLP), semantic search, and relevance tuning powered by machine learning. These technologies ensure users can perform complex queries and receive highly relevant results, improving search accuracy and satisfaction.
Integration
Look for a tool that integrates with your existing workspace applications for a unified search experience. It should connect can with:
- CRM systems (Salesforce, HubSpot)
- ERP platforms(SAP ERP, NetSuite)
- Collaboration tools(Jira, Slack)
- Knowledge bases (Confluence, Google Drive).
It uses APIs and connectors to index information from different sources, such as Salesforce, Dropbox, and Gmail, into a single search interface. This interface will help you find relevant information without searching multiple apps individually.
Here’s a brief use case of Zendesk;
Consider this: Your organization's customer support team constantly struggles with scattered data, slowed response time, and low ticket resolution rates. Your support agents are overwhelmed with fragmented data across multiple platforms—ticket management systems, knowledge bases, and communication channels. They spend hours manually looking for customer insights.
Here’s how you can overcome all those challenges by integrating Zendesk with an AI-powered search software like Findr AI;
- It can organize Zendesk data into searchable indexes to provide the customer’s history, past interactions, and related issues in a single search
- You can search across Zendesk tickets, knowledge bases, and even connected platforms like Slack or Confluence to get cross-platform insights
- Gain deep insights into resolution times, frequently used resources and agent efficiency
Reporting and analytics
Reporting and analytics are essential for monitoring search performance, user behavior, and trends. These tools provide actionable insights to optimize search functionalities, improve content, and better understand user needs, driving continuous improvement in the search experience.
Smart recommendations and predictive search
Intelligent recommendations and predictive search enhance the user experience by suggesting relevant content based on previous searches and the query context. For example, if a user frequently searches for customer support documents, the system may suggest related resources or FAQs. This feature helps users find information faster and more efficiently, reducing search time and improving productivity.
###Ability to build a centralized knowledge base
A centralized knowledge base collects information from connected applications and internal databases into one accessible location. It is an intelligent repository where users can search for documents, files, reports, FAQs, and policies. Using this intelligent repository, you can:
- Automatically organize content through smart tagging and categorization for easier retrieval and navigation
- Enhance search accuracy by understanding user queries in natural language using NLP
- Enable multiple users to work on documents simultaneously with real-time collaboration and live updates
- Handle various file types, including text, video, and images, in one repository with multi-format support
Role-based access for different team members
Role-based access control (RBAC) is a security feature that manages user permissions in enterprise software. It helps employers control access to sensitive data and functionality by assigning users specific privileges. For example, an "admin" role can modify system settings, while a "viewer" role can only view data.
RBAC enhances security, simplifies administration, and ensures compliance. Employers should prioritize this feature when selecting enterprise software to protect their organization's assets.
Use cases of enterprise search software
Here are some use cases of enterprise search software for different company departments.
Sales
Sales teams often struggle with productivity obstacles such as inadequate data access, customer-specific knowledge gaps, and disrupted workflows caused by fragmented tools. And do you know what is the biggest obstacle to sales?
Finding information!
According to Forresterr’s sales activities study, “studies have consistently shown that finding content and information is a significant productivity obstacle for sales teams”.
However, you can eliminate this consistent barrier and compress your sales cycle by integrating enterprise AI search software. Sales teams can use the software to:
- Efficiently gather and summarize client information for better call preparation and quicker deal closures
- Develop tailored follow-ups, outbound campaigns, and sales decks to engage prospects more effectively
- Provide the right content at the right time to consistently close more deals and enhance customer satisfaction
- Use AI to stay updated on team performance, new features, and updates, ensuring strong customer relationships and quick adaptation
Use Case: Tailored follow-ups with clients
Sales representatives can use search software to quickly access clients’ historical data, such as emails, support tickets, and other communication files. This data can then be used for personalized follow-ups and client interactions.
Advanced AI features, such as content summarization and insightful reporting, have helped them build strong relationships with clients and customers, identify opportunities for upselling and cross-selling, and close deals faster.
Marketing
Marketing teams manage multiple documents and files simultaneously. Whether developing a new campaign, drafting SEO strategies, or responding to customer inquiries, the need to access and manage vast information is constant.
Without a proper data management system, marketers face many challenges. Important documents can get lost in email threads, hindering project timelines. Searching through disconnected tools for the latest campaign data wastes valuable time. Collaboration suffers as team members struggle to find the most recent shared file version.
However, you can overcome these challenges by integrating enterprise AI search software into your existing workflow. You can
- Leverage AI to transform your existing knowledge into actionable insights, campaign ideas, and marketing strategies
- Quickly draft SEO strategies, brainstorm campaign concepts, and address customer queries with ease using your workplace data
- Generate emails, newsletters, and blog posts informed by real-time updates from product, engineering, and sales teams
- Streamline content creation by letting your AI assistant draft documents, retrieve information, and handle queries so you can focus on creativity
Use Case: Centralized reports and data
Finding and analyzing past data has never been easier.
Email marketers could retrieve loads of templates, segment lists, and customer studies in a second using AI. And based on these insights, they could write hundreds of personalized emails in one go.
Further, they used it to generate actionable insights from past campaigns and strategies in under minutes. And this time, they didn’t have to spend days sifting through last year’s performance analyzing “what went wrong here?” It was all performed by the AI search software.
Customer Support
If you work in customer success, you know how much difference AI search software can make in your daily work. From customer inquiries to troubleshooting issues, everything needs quick data access. But, when the information is spread across various tools and emails, the work becomes frustrating.
This leads to low self-esteem, delayed responses, inconsistent information, and longer customer waiting periods. However, enterprise software can streamline searches for you. Not just that AI can also
- Quickly update customers with accurate information without searching through knowledge documents
- Utilize expert knowledge to provide up-to-date solutions and confidently resolve customer issues
- Find precise answers swiftly to resolve cases efficiently and keep customers satisfied
- Generate FAQs directly from Zendesk tickets, Slack conversations, Google Docs, and more
Use Case: Quick and efficient customer support
Customer support teams use AI-powered search to access customer interactions, support tickets, and internal documentation. With tailored search engines like Findr AI, support agents quickly find relevant information by simply putting in a query. AI’s integration feature also lets users search through emails and chat histories to understand the context of the customary query.
It also summarizes the search results to quickly overview the customer profile. This feature is handy for quick preparations before a sales call.
Engineering
Engineering teams often face challenges with fragmented knowledge, slow issue resolution, and inefficient onboarding. Enterprise search software provides unified access to technical documents, error codes, and historical work across multiple tools, such as GitHub, Jira, and Slack.
This enables faster sprint planning, efficient debugging, and quicker onboarding, accelerating time to market for new products. Those are the primary uses of AI. Let's look at the more advanced uses below
- Enhance workflows with AI that answer questions about bugs, code practices, and more using internal knowledge
- Access documents, specifications, and processes instantly without switching between systems
- Reduce repetitive tasks to free up time for engineers to focus on creative problem-solving and innovation
- Resolve code issues quickly by accessing solutions and technical support within a unified platform
Use Case: Enhanced Project Collaboration
Engineering teams often use search AI’s filtering and faceted search features to access multi-format data easily. Using this feature. Users can add customizable filters and facets to narrow search results by criteria such as date, file type, or project phase.
Filtering and faceted search ensure a smoother workflow during high-focus tasks like debugging, code review, and project documentation. For example, advanced filtering allows engineers to quickly locate specific error logs or issue reports by severity or component when debugging. This feature has majorly improved work productivity and accuracy for engineering teams.
Role of LLMs in enterprise search
Large Language Models (LLMs) are AI programs that understand, process, and generate human-like content. They use deep learning models pre-trained on extensive datasets. LLMs are integrated into enterprise search software to understand word patterns, context, and meanings in queries.
Here’s how LLMs play a key role in the execution of enterprise search features:
Improved Data Accuracy: LLMs enhance the user's search experience by understanding the underlying emotions behind the queries.
Offers Semantic Search: Unlike traditional keyword-based searches, LLMs use semantic search to comprehend human texts and provide accurate results.
Conducts Multimodal Search: LLMs process various data types, enabling the development of multimodal search applications. The program can retrieve information from different sources and formats, including text, images, videos, audio files, and structured data.
Insightful Data Retrieval: LLMs utilize advanced NLP to extract detailed information and generate insight from large amounts of text.
Tailored Search: LLMs use machine learning algorithms to analyze user behavior and preferences. Hence, they help deliver tailored search results and enhance user satisfaction.
Continuous Refinement: LLMs continuously adapt and improve their search accuracy and relevance based on user interactions.
Conclusion
Are you looking for enterprise search software that creates a connected experience for your team?
We recommend using Findr.
Findr integrates with all your workplace applications, enables universal search across all your apps, and helps you extract specific answers to your queries.
Here’s what the existing users of Findr say about this tool:
Want to experience Findr first-hand?
