The Forrester Wave™: Cognitive Search Platforms, Q4 2023

Cognitive Search Platforms Vendor Product Comparisons 2023

Key features and pros and cons of top Cognitive Search Platforms products:

Coveo - Leaders

Pros:

Strong capabilities in understanding user intent at query time and following through with highly relevant search results
Platform can ingest and understand a wide range of content, including analyzing the content of images and video
Superior product roadmap will attack key issues that prevent successful use of LLMs for search — namely sourcing and citing of the information provided
Analytics and machine learning capabilities
GenAI roadmap

Cons:

Focus on so many service- and commerce-related cognitive search use cases can come at the cost of appealing to more specialized markets
Must continue to focus on expanding its capabilities for parsing and snippeting content to support additional future retrieval-augmented generation (RAG) use cases
Hybrid search approach needs to include additional methods of submitting queries (e.g., images) and other approaches to indexing beyond vector databases
Challenges around vendor support of certain data connectors

Sinequa - Leaders

Pros:

Variety of data connectors for many enterprise systems, including specialized data types, models, and industries
Platform can ingest a variety of content and natively leverages LLMs to snippet and chunk content for vector search
Strong NLP capabilities
Variety of prebuilt connectors
Data ingestion capabilities
Customer-centric approach to innovation
Early entrant in the neural search market, being one of the first to include LLMs in search

Cons:

Must keep an eye on other methods of indexing to maintain a competitive edge, e.g., knowledge graphs
Must continue to build upon query and results delivery capabilities to keep pace with the market

Elastic - Strong Performers

Pros:

Strong security, including many different certifications and data protection standards
Solid native capabilities for data ingestions and content understanding capabilities
Powerful tooling to extend and build upon the platform's core offering
Open and flexible nature
Platform's ability to extend beyond search into other operational areas like security and observability

Cons:

Needs continued focus on building better business administration capabilities and abstractions in its platform to make its flexibility and power more accessible to less technical users
Must continue to invest heavily in R&D to keep pace with competitors
Buyers should be attentive to managing infrastructure cost when self-hosting

IntraFind - Strong Performers

Pros:

Solid platform across many key areas including data ingestion, connectors, intent, and relevancy tuning
Extensive security capabilities with many certifications for different data types and environments
Connectors and ability to integrate different data sources into search
More than two decades in enterprise search with wealth of experience in government and manufacturing

Cons:

Must expand its capabilities to help enterprises take advantage of multiple indexing and search methods as well as the methods by which users can submit a query (beyond text)
Needs to increase its innovation in delivering native search capabilities in more data formats
Must deliver a smoother search builder experience for non-technical users to keep up with competitors

Kore.ai - Strong Performers

Pros:

Strong capabilities to understand user intent using a variety of data and context inputs to help drive the best query and to deliver results
Solid capabilities for ingesting content and snippeting it down into usable chunks for advanced vector search
LLM capabilities as part of its broader enterprise ecosystem

Cons:

Needs to continue to build more connectors for specialized domains
Must broaden its capabilities for multimedia content ingestion and search in order to differentiate itself further in this market

Lucidworks - Strong Performers

Pros:

Wide array of data connectors
Enterprise security capabilities
Scalability
Search analytics
Fosters a close partnership with customers

Cons:

Needs to focus on expanding its array of search methods — the lexical-vector hybrid approach is solid today but Lucidworks will need to fold in additional capabilities such as knowledge graphs and additional localization
Needs to aggressively move forward with its integration of LLM-supported capabilities
Need for internal resources to maximize success of the platform

Mindbreeze - Strong Performers

Pros:

Powerful capabilities for deploying search in hybrid environments, ensuring secure and reliable delivery of the most relevant results
Range of applications to help enterprises spin up search projects quickly
Vendor updates the product consistently
Ability to deploy search across many data sources
US federal agency certifications position Mindbreeze to be attractive to government buyers

Cons:

Capabilities for snippeting and RAG searches must continue to improve to keep pace with a quickly changing market
Professional services or training will be needed to get the most out of the platform
Investment in Business Decision Insights ecosystem may distract from the primary focus of enhancing InSpire

OpenText - Strong Performers

Pros:

Powerful capabilities for ingesting and understanding a wide array of different content and data types, including multimedia content types
Heavily customizable by developers who can leverage its powerful query understanding and results delivery capabilities
Wide set of data connectors
Flexibility of platform customization
Decades of customer loyalty from buyers

Cons:

Must invest in making the platform more abstracted and usable for less technical users to take full advantage of the platform's capabilities
Struggling to grow significantly in the market
Formal training in the platform is important for success
Vision to bring IDOL capabilities to all OpenText offerings remains to be seen whether it will successfully manifest

Squirro - Strong Performers

Pros:

Strong capabilities in its core search method approach by automatically bringing customer data into all relevant indexes
Solid features for understanding user intent and automatically tuning relevancy
High-touch engagement from the vendor to ensure customer success

Cons:

Has significant work to do in expanding its features around its manual tuning and content ingestion capabilities
Relatively slow growth over recent years
Remains to be seen whether it stays an important player in this market
Long way to go to become an AI/ML platform with a search focus

Algolia - Contenders

Pros:

Powerful scalability to high volumes of queries and responses
API-oriented platform for enterprises to build and customize their search experiences
Strong capabilities to deliver the most relevant content to the search user
Vendor's approach to hybrid keyword/vector search

Cons:

Needs to improve its localization capabilities
Must increase its application offers in order to address more cognitive search use cases
Neural hashing approach won't continue to significantly differentiate it from many of its peers
Will need to reach beyond its private cloud roots and into the public cloud world

Amazon Web Services - Contenders

Pros:

Strong capabilities in its data connectors and security
Delivering more context-relevant search with the Retrieval API
Moved quickly to add generative AI capabilities to the Kendra platform

Cons:

Kendra's ability to ingest and parse data is below par compared to competitors
Must expand data ingestion and processing
Declined to participate in the full Forrester Wave evaluation process

Glean Technologies - Contenders

Pros:

Strong capabilities in understanding user intent and delivering automated tuning of search results
Good set of data connectors and analytics for understanding and refining search behavior
Speed with which customers were able to deploy Glean
Speedy integration of an LLM-supported chat function into the product

Cons:

Manual tuning capabilities aren't comprehensive compared to others in the market
Must significantly expand its localization capabilities
Must expand capability to snippet and summarize content
Without clearer differentiation, Glean risks being overlooked in an increasingly active and crowded market

Microsoft - Contenders

Pros:

Solid capabilities in ingesting, chunking, and snippeting content
Leveraging multiple different search methods — lexical, vector, and knowledge graph
Close partnership with OpenAI to provide embeddings and support RAG-based search applications

Cons:

Current offering lacks a wide variety of data connectors, particularly for more specialized enterprise data sources
Doesn't offer a wide range of modalities for displaying search results
Needs to continue to expand on first-party connectors for use cases with esoteric data needs
Will need to help customers understand how to best bring together the Cognitive Search API with OpenAI services
Declined to participate in the full Forrester Wave evaluation process

Yext - Contenders

Pros:

Strong capabilities for delivering search through multiple modalities, using a knowledge graph in addition to its vector and lexical search offerings
Easily deploy and manage a variety of distinct search experiences with a relatively low level of technical expertise
Good features for snippeting and summarizing content

Cons:

Must expand the number of formats by which it can deliver search results to end users
Must enhance its native security capabilities
Will need to show success with its new partner program in order to ensure scalable growth of the platform
Needs to expand its ingestion capabilities for multimedia content

FAQs

Q: Which vendors are Leaders in 2023?

A: Coveo, Sinequa

Q: What does "Leader" mean in Forrester Wave terms?

A: Leaders in the Forrester Wave evaluation demonstrate the strongest current offering and strategy in the cognitive search platforms market. They offer comprehensive platforms with robust capabilities across data ingestion, intelligence, development, end user experience, scope, operations, and architecture. Leaders have superior product roadmaps, strong vision for the future of cognitive search (particularly around generative AI integration), and demonstrate market leadership through innovation, customer success, and strategic partnerships. They excel at understanding user intent, delivering relevant results, and providing advanced capabilities such as hybrid search approaches combining lexical, vector, and semantic search methods.

Q: How do I interpret a Forrester Wave?

A: https://www.forrester.com/policies/forrester-wave-methodology/

Report:

Top Vendor Products for Cognitive Search Platforms in 2023

Cognitive Search Platforms Vendor Product Comparisons 2023

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FAQs

Q: Which vendors are Leaders in 2023?

Q: What does "Leader" mean in Forrester Wave terms?

Q: How do I interpret a Forrester Wave?

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