AI-driven enterprise search uses semantic understanding, hybrid retrieval, and retrieval-augmented generation (RAG) to return cited, permission-aware answers from across a company's apps. Employees get the answer, with sources, instead of a page of links. You'll also see the category called enterprise AI search or AI-powered enterprise search.
Keyword search struggles inside a company for a few reasons. Slack messages are short and have no titles. Documents use internal jargon, project code names, and acronyms that a keyword match can't interpret.
RAG is where search and generative AI meet. A large language model (LLM) writes the answer, but search decides which company knowledge the LLM sees first.
In this article, we'll explore the significance of enterprise AI search, how it works, and its impact on business efficiency.
What is AI-driven enterprise search?
AI-driven enterprise search is a search system that indexes content, activity, and identity data from tools like Google Drive, Confluence, Jira, and Salesforce. It then uses language models to interpret the intent behind each query. The result is relevant, permission-aware answers drawn from your company's own knowledge.
Enterprises grapple with the management of extensive datasets encompassing HR records, financial transactions, customer interactions, and project details. The efficiency of information retrieval becomes paramount, allowing employees to access relevant data promptly and eliminating delays associated with navigating complex structures or manually searching through vast repositories. This enables:
- Enhanced productivity
Swift information retrieval directly correlates with increased productivity, as employees spend less time searching for data and more time on value-added tasks. - Informed decision-making
Quick access to relevant information empowers organizations to make informed decisions promptly, leading to a more agile and competitive business environment.
Glean Search applies this approach across more than 250 connected apps. Glean builds a custom language model for each company that keeps learning its dialect, including project names, team terms, and acronyms. In a company's first six months with Glean, search quality typically improves by 20%.
How AI-driven enterprise search works
AI-driven enterprise search works in four layers. Connectors index data, hybrid search ranks it, RAG turns results into answers, and permissions control who sees what.
Connectors index content, activity, and permissions
Enterprise search tools take one of two approaches to company data. Federated search sends each query to every app's search API at runtime and stitches the results together. That method is often incomplete, and it misses relationships between people, content, and activity.
Indexed search copies and normalizes data ahead of time, so the system can rank everything together. Glean Search ingests content, activity, and identity data through more than 250 connectors. Each connector also brings in the source app's permissions.
Hybrid search combines semantic, lexical, and graph signals
Hybrid search blends several retrieval methods, because no single method handles every query. Semantic search matches the meaning of a query rather than its exact words. Vector search is a common way to do it: text becomes embeddings, numeric representations that place similar ideas close together.
Lexical search still matters in hybrid search. Exact document titles, ticket IDs, and error codes need precise keyword matching that embeddings can blur. Glean redesigned lexical search for enterprise data, such as short Slack comments without titles.
The third signal comes from the Enterprise Graph. Glean's Enterprise Graph links people, content, and activity across apps. Those relationships help Glean Search tell HR's authoritative "benefits" page from a stale copy.
Importance of Enterprise AI Search
Traditional search methods often fall short when confronted with the intricacies of modern enterprise data landscapes. The adoption of AI-powered workplace search solutions becomes imperative due to the following challenges:
1. Complex data structures
Enterprises typically manage multifaceted intranets, intricate websites, and diverse applications, making manual navigation time-consuming and prone to errors. AI-driven search solutions simplify this process by providing a unified interface for swift and accurate information retrieval.
2. Diverse content repositories
Organizations store data across various platforms, from databases to knowledge management systems. AI-powered search seamlessly integrates and queries these disparate repositories, ensuring comprehensive access to information.
3. Enhanced user experience
In the contemporary workplace, employees expect a search experience similar to popular online search engines. AI-driven search solutions not only meet these expectations but also offer intelligent suggestions, natural language processing, and personalized results, enhancing overall user satisfaction.
4. Data analysis and insights
Beyond retrieval, AI-powered search facilitates advanced data analysis by identifying patterns, trends, and relationships within enterprise data, unlocking valuable insights for strategic decision-making.
Examples of Enterprise AI search
Effective search functionality is indispensable in navigating the complexities of large datasets and intricate systems within enterprises. The following examples illustrate AI enterprise search applications:
1. Knowledge management:
Content examples:
- Proposals, pitch books, RFPs, RFIs, contracts, legal documents
- Internal and external research, sales, and marketing materials
- Documentation, guides, training resources
Search queries in knowledge management help users access specific insights within the vast sea of enterprise content, leveraging automated information extraction for precise answers
2. Corporate portal/intranet
Content examples:
- Corporate functions (timecards, expenses, payroll, IT, CRM, etc.)
- Policies, procedures, FAQs, support documentation, ethics, and forms
- Organizational details, personnel information, office locations
- Products, parts, projects, customer details
- HR benefits
Search queries in this context aim to answer practical employee questions, simplifying information retrieval and eliminating the need for users to navigate complex structures.
3. Data catalog
Content examples:
- Data sources from data lakes and data warehouses
- Business system APIs, MasterData, etc.
Search queries for data catalogs aim to facilitate the identification of datasets for analysis, efficiently pinpointing relevant tables and subsets within the enterprise data infrastructure.
Case study – How Confluent built a complete company knowledge base
AI search seamlessly integrates with enterprise conversation platforms such as Slack, Microsoft Teams, and Cisco Webex to provide a more efficient search experience. For instance, Glean offers integrations with these platforms, enabling users to search for information directly within the conversation interface. This integration saves time, increases productivity, and eliminates the need to switch between different applications.
The challenge
Confluent grew from a team of 250 to 2,000+ employees in a matter of years and had an influx of distributed/hybrid teams. As they grew, it became increasingly difficult for employees to find the information they needed to do their jobs quickly. When an annual employee survey produced low scores in response to the statement, “The information needed to do my job is readily available”, it was clear that information sprawl was a challenge.
Solution
As an early adopter of Glean, an advanced search solution that would act as a connective tissue between information sources, Confluent quickly worked to solve the challenge expressed. They promptly launched Glean to search across their 20+ existing systems and unlock enterprise-wide information access. Implementing required minimal lift yet had a significant impact.
Read the full case study here – The Confluent Customer Story
Conclusion
By centralizing information, streamlining access, and providing powerful search capabilities, Glean empowers companies to make the most of their internal knowledge. It ultimately leads to higher productivity and enhanced corporate culture. Embrace the future of knowledge management with Glean and unlock your organization's full potential. Get a demo today.
