IDC

Regions Focus: Worldwide

Worldwide Knowledge Discovery Software for Internal-Facing Use Cases 2023–2024 Vendor Assessment

January 2024 | us51175723e
Hayley Sutherland

Hayley Sutherland

Senior Research Analyst, Conversational AI & Intelligent Knowledge Discovery

Product Type:
IDC: MarketScape
This Excerpt Features: Mindbreeze

IDC MarketScape: Worldwide Knowledge Discovery Software for Internal-Facing Use Cases Vendor Assessment

Capabilities Strategies Participants Contenders Major Players Leaders

Leaders

MindbreezeFeatured Vendor

Elastic

Coveo

Lucidworks

Glean

Major Players

Yext

Squirro

OpenText

IntraFind

EPAM

SearchBlox

Sinequa

Lucy

IDC MarketScape Methodology

IDC Opinion

Knowledge is the foundation for strong decision-making. Finding information, synthesizing that information into knowledge, and then making that knowledge findable and usable are things humans have been working on since we first gained the ability to communicate. In most of today’s enterprises and other organizations, these processes have been supplemented with knowledge management and information retrieval technologies for decades. However, the manual and time-consuming nature of the work needed to keep such technologies up to date with the latest information, connect disparate pieces of information into useful knowledge, and serve that to users in a way that is consistently accurate, relevant, complete, and timely has continued to be a challenge to achieve at scale. This has been particularly difficult for large, complex organizations with decades of historical information, held in myriad siloed content sources — often with their own built-in search features — scattered across various business units and functional areas. Such organizations struggle with challenges like manually tagging content, creating and maintaining taxonomies and ontologies, and ensuring that such central representations of knowledge are consistently useful for all users. For years, despite improvements in some search technologies, knowledge workers continued to lose time trying to find the knowledge they needed.

Over the past three to five years, however, IDC has been observing a fundamental shift as machine learning (ML)– and deep learning–driven advances in artificial intelligence (AI) have made their way into most commercial search systems. Capabilities such as ML-based relevancy tuning, auto-tagging of content, auto-building of knowledge models, enhanced document understanding, and vector search have helped improve key measures of the accuracy and completeness of search (i.e., precision and recall). According to a February 2023 IDC survey, the benefits of these advances have been wide reaching: for example, 41% of respondents saw improvements in employee productivity because of AI-powered search systems, helping scale operations; 33.9% of respondents saw increased cost savings; and 31% saw increased revenue. Areas such as decision-making, employee and customer satisfaction, and innovation also saw positive improvements. Nearly half of those respondents (40.8%) had invested in new AI-powered enterprise search and knowledge discovery software in the past three years, and 79.4% were using a system that was less than five years old (see Search and Knowledge Discovery Buying Decisions and ROI, 2023, IDC #US50160723, May 2023).

Increasingly, search and knowledge discovery systems are also leveraging the latest innovations in generative large language models (LLMs). Emerging into the mainstream in late 2022, the ability of LLMs to generate nuanced, human-like language shows great promise for search, from improving intent understanding and results relevancy to providing personalized and conversational search to synthesizing insights, answers, and summaries. At the same time, practitioners are finding that search technologies, particularly vector search, are critical for helping mitigate the problem of hallucination, in which LLMs provide false or inaccurate answers, via techniques such as retrieval-augmented generation (RAG). As a result, IDC is seeing an accelerated move among search and knowledge discovery vendors to provide vector-based search (if they were not already doing so), as well as to enhance and upgrade vector databases. Some are turning to third-party providers such as Redis, Pinecone.io, and Weaviate, while other vendors are moving to provide their own native vector database.

Definitions

We define some important terms that are key to understanding IDC’s assessment and characterization of the knowledge discovery software for internal-facing use cases market. For a definition of the market itself, including the term knowledge discovery, refer to Market Definition in the Appendix. Furthermore:

  • Keyword search: Also known as lexical search, keyword search describes information retrieval performed by searching for exact matches to search terms.
  • Semantic search: Semantic search goes beyond keyword matching to incorporate semantic meaning such as context, resulting in better intent understanding and enabling related or similar items, knowledge, and so forth to be found.
  • Vector search: Typically used in semantic search engines, replacing or (more often) in combination with traditional keyword search, vector search uses ML models to transform unstructured data into numeric representations or “vectors.” These vectors represent the semantic meaning and context of that data, allowing ML models to find related or similar concepts by using numerical distance as a proxy for semantic distance.
  • Generative AI: Generative AI is a branch of computer science that involves unsupervised and semi-supervised machine learning algorithms that enable computers to create new content using previously created content, such as text, audio, video, images, and code.
  • Large language models: One example of generative AI, LLMs are language-generation models with vast numbers of parameters. LLMs began at Google Brain in 2017, initially used for translation of words while preserving context, and have since proliferated at tech firms like Google (BERT and LaMDA), Facebook (OPT-175B and BlenderBot), and OpenAI (GPT-3.5). In addition, some organizations have developed their own LLMs or customized models based on OS and commercial versions for business use.
  • Retrieval-augmented generation: RAG is an approach to answer generation that leverages the strengths of both search and generative AI technologies. In the RAG machine learning pattern, relevant information (typically determined via vector search) is first retrieved by the search system and then passed to the LLM in the form of best-fit snippets or documents, which the LLM then summarizes into an answer or insight.

Tech Buyer Advice

  • Upgrade your organization to AI-powered knowledge discovery software if you have not already done so. While the search market was somewhat stagnant for some time, advances in AI in the past few years have resulted in significant improvements that are providing many organizations with competitive advantage.
  • Consider up front whether you will want to expand your search and knowledge discovery system into a centralized system for an entire function or department or even an organizationwide knowledge network. Centralizing search globally across a specific function, such as market insights or competitive intelligence, can be a good starting point, and many of the vendors in this evaluation have proven experience in doing this on both a departmental and companywide level. However, depending on your specific needs, some vendors will be better suited than others to work with the systems and file types most important to your business.
  • Work with internal business and IT leaders to develop KPIs and metrics that help measure the success of new or enhanced knowledge discovery software, including but, ideally, not limited to productivity. Consider aspects such as increased innovation, better business decision-making, faster time to decision, faster time to market, increased employee satisfaction, and even financial benefits such as profit increases and cost savings.
  • Frame your generative AI strategy in terms of outcomes. Leverage internal, vendor, and partner expertise to understand what use cases are valuable, feasible, and safe to start with while ensuring that success can be meaningfully measured. Ask your vendors detailed questions about how they are dealing with concerns such as hallucinations, data privacy issues, legal/IP challenges, pace of innovation, and open versus closed systems. Work with IT and knowledge management teams to gain a clear picture of the state of organizational data.
  • Remember that LLMs are essentially very good language generators. They were not built to be fact retrievers and must be grounded in real-world data via techniques like RAG and prompt engineering as well as bounded within organizational values and ethics to ensure that classic AI issues such as bias, discrimination, or inappropriate language are not produced.

Featured Vendor

This section briefly explains IDC’s key observations resulting in a vendor’s position in the IDC MarketScape. While every vendor is evaluated against each of the criteria outlined in the Appendix, the description here provides a summary of each vendor’s strengths and challenges.

Mindbreeze

After a thorough evaluation of Mindbreeze’s strategies and capabilities, IDC has positioned the company in the Leaders category in this 2023–2024 IDC MarketScape for worldwide knowledge discovery software for internal-facing use cases.

Mindbreeze is a provider of applications and cloud services for enterprise search, applied artificial intelligence, and knowledge management. Its primary search and knowledge discovery offering, Mindbreeze InSpire, allows users to search across structured and unstructured information from across the enterprise and is supported by a unified graph index. Mindbreeze InSpire supports a variety of deployment types, including on premises, public and private cloud, and SaaS, and comes with hundreds of native connectors and supported file format types. It provides drag-and-drop tools for customizing the functionality and display of metadata, search filters, and search results, including tailoring results display based on user role, department, or even behavior and expertise. Mindbreeze has dual headquarters in Chicago, Illinois (the United States) and Linz, Upper Austria, Austria (EU), with four other offices and customer deployments worldwide, and is privately held.

Quick facts about Mindbreeze include:

  • Year founded: 2005
  • Total number of employees: 450+
  • Total number of clients: 2,900+
  • Industry focus: Mindbreeze works across a variety of industries, with strong experience in manufacturing, retail, transportation, utilities, telco, pharma/life sciences, and government institutions.
  • Deployment options: Mindbreeze provides a full range of deployment options including on premises, private cloud, public cloud, and hybrid cloud. Public cloud deployments are available for AWS, Azure, and Google or using on-premises Mindbreeze InSpire appliances hosted in customer datacenters.
  • Pricing model: Standard end-user pricing is charged via annual subscription license with pricing based on the total number of documents or data objects indexed, with app telemetry included to assist admins with tracking and budgeting. This license also includes all connectors and features. For OEM usage, pricing is based on a combination of partner’s pricing schemas and Mindbreeze’s own market prices. 
  • Related products/services: Mindbreeze Business Decision Insights (BDI) provides an insights-as-a-service ecosystem with targeted solutions for specific business processes and functional areas. Mindbreeze also provides professional services to assist with customization as needed.
  • Prebuilt integrations, connectors, and content types: Mindbreeze comes with over 450 prebuilt native connectors to a wide variety of data sources. It also supports over 500 file formats out of the box.

Generative AI Approach and Features

  • Overall approach: Mindbreeze’s approach to incorporating the latest advances in generative AI and large language models is to leverage LLMs along with RAG to power out-of-the-box features such as summarization within Mindbreeze InSpire as well as providing the ability and guidance for customers to leverage their own licenses with Hugging Face and other LLM services.
  • LLM of choice: Mindbreeze’s out-of-the-box generative AI features are powered by Hugging Face’s LLM, while its BYO-license model supports both Hugging Face and other WinX models. Mindbreeze also allows customers to integrate existing services from other vendors, such as OpenAI, Azure, Google, and AWS, via its extension points.
  • Currently GA features: Mindbreeze InSpire offers AI-generated summaries and recommended related answers, supported with RAG to mitigate the possibility of hallucinations. This is combined with personalization models that learn from user signals to ensure suggested content and answers are personalized, relevant, and respect user access. Mindbreeze InSpire also now has a chat feature that supports conversational search and knowledge discovery.
  • Publicly announced road map features: Mindbreeze plans to continue to expand and enhance its LLM/generative AI–based features and capabilities.

Strengths

  • Knowledge networking capabilities: Mindbreeze’s customers praised its ability to perform “semantic networking,” or the semantic linking of enterprise data into a network of knowledge that goes beyond simple data or information retrieval to provide relevant insights. In particular, they noted that Mindbreeze InSpire was able to do this while firmly respecting existing access permissions.
  • Customization: Mindbreeze provides a variety of drag-and-drop tools for customization of search applications and search results, including the ability to use widgets to easily tailor by role or use case. Customers choose Mindbreeze for this flexibility in tuning features, functions, and filters.

Challenges

  • Custom deployment timelines: While Mindbreeze states that its average time frame for a standard Mindbreeze InSpire deployment is four weeks, in some cases, custom deployments can take much longer and even exceed original/target timelines. This market is growing rapidly, and as Mindbreeze faces more demand for custom deployments, it should ensure it is working to attract adequate talent for its professional services division to support this growth.
  • Upgrading client base: Mindbreeze supports a significant number of on-premises customers, and this may prove challenging in terms of ensuring all customers can access advanced and future features such as those based on generative LLMs. Mindbreeze should ensure it is working closely with such customers as they consider a move to private cloud or hybrid environments, in order to mitigate the risk of being supplanted by major cloud providers that also offer search and knowledge discovery capabilities.

Consider Mindbreeze When

Consider Mindbreeze if you are looking for an internal-facing search and knowledge discovery vendor with native support for a wide range of data sources and format types and the ability to semantically link data from across these sources with unified graph and vector capabilities. In addition to providing a full spectrum of cloud deployment options, Mindbreeze has a history of supporting customers in highly regulated industries that still require on-premises deployments and can also support mobile deployments of Mindbreeze InSpire.

Conclusion

While the search market was somewhat stagnant for some time, advances in AI have led to significant improvements, resulting in knowledge discovery software systems that are providing many organizations with competitive advantage. The rapidly evolving business and global landscape has shifted focus to resiliency and flexibility, and organizations of every kind are recognizing the importance of a strong foundation of enterprise intelligence. As leading organizations look to operationalize the latest advances in generative AI and LLMs, having this foundation will be critical to providing business leaders and employees with insights and recommendations that are accurate, relevant, and useful.

Methodology

IDC MarketScape Vendor Inclusion Criteria

Participating vendors must meet the following inclusion criteria:

  • The offering should be commercially available for use as a single product family or a suite of services and purchased by customers for at least one year. IDC also considers and includes new product features and capabilities introduced through the calendar year 2023 as part of vendor strategy evaluation. In addition, IDC considers these features as part of its capabilities evaluation if there is sufficient customer adoption and use for IDC to properly evaluate them and as long as these features are generally available at the time this document was published.
  • The product must offer knowledge discovery software that organizations can utilize, customize, deploy, and/or also include in their enterprise applications.
  • The product must support a variety of internal-facing use cases.
  • The product must have at least 25 customers that have used this solution/service in production in the calendar year 2022.
  • The product must have achieved at least $1 million in revenue in the calendar year 2022.
  • The product must be offered and available on a worldwide basis.
  • The product must be all or mostly the vendor’s own intellectual property (IP).

Reading an IDC MarketScape Graph

For the purposes of this analysis, IDC divided potential key measures for success into two primary categories: capabilities and strategies.

Positioning on the y-axis reflects the vendor’s current capabilities and menu of services and how well aligned the vendor is to customer needs. The capabilities category focuses on the capabilities of the company and product today, here and now. Under this category, IDC analysts will look at how well a vendor is building/delivering capabilities that enable it to execute its chosen strategy in the market.

Positioning on the x-axis, or strategies axis, indicates how well the vendor’s future strategy aligns with what customers will require in three to five years. The strategies category focuses on high-level decisions and underlying assumptions about offerings, customer segments, and business and go-to-market plans for the next three to five years.

The size of the individual vendor markers in the IDC MarketScape represents the market share of each individual vendor within the specific market segment being assessed.

IDC MarketScape Methodology

IDC MarketScape criteria selection, weightings, and vendor scores represent well-researched IDC judgment about the market and specific vendors. IDC analysts tailor the range of standard characteristics by which vendors are measured through structured discussions, surveys, and interviews with market leaders, participants, and end users. Market weightings are based on user interviews, buyer surveys, and the input of IDC experts in each market. IDC analysts base individual vendor scores, and ultimately vendor positions on the IDC MarketScape, on detailed surveys and interviews with the vendors, publicly available information, and end-user experiences in an effort to provide an accurate and consistent assessment of each vendor’s characteristics, behavior, and capability.

Market Definition

For this research, IDC defines knowledge discovery software for internal-facing use cases as software that can find, locate, and provide answers, information, and knowledge discovery capabilities for a variety of internal-facing use cases, such as enterprise search, departmental search, executive decision-making, research and discovery, or competitive/market intelligence. As traditional search and information retrieval systems evolve, this software increasingly uses artificial intelligence technologies, including machine learning, deep learning, LLMs, and NLP, to facilitate natural language search and knowledge or product discovery across various structured and unstructured forms of data.

Related Research

  • IDC MarketScape: Worldwide General-Purpose Knowledge Discovery Software 2023 Vendor Assessment (IDC #US49988523, October 2023)
  • Market Analysis Perspective: Worldwide Search and Knowledge Discovery, 2023 (IDC #US49696923, September 2023)
  • Worldwide Search and Knowledge Discovery Software Forecast, 2023–2027 (IDC #US49651823, July 2023)
  • Worldwide Search and Knowledge Discovery Software Market Shares, 2022: Artificial Intelligence Accelerates Growth (IDC #US49651923, July 2023)
  • Search and Knowledge Discovery Buying Decisions and ROI, 2023 (IDC #US50160723, May 2023)
  • The Implications of Generative Large Language Models for Search and Knowledge Discovery (IDC #US50160923, March 2023)

IDC MarketScape: Worldwide Knowledge Discovery Software for Internal-Facing Use Cases 2023–2024 Vendor Assessment