The Forrester Wave™: Cognitive Search Platforms, Q4 2025

Cognitive Search Platforms Vendor Product Comparisons 2025

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

Elastic - Leaders

Pros:

Outstanding capabilities in enabling deep and comprehensive refinement of relevancy and results delivery
Flexibility in deploying across any environment and scaling well
Outstanding capabilities for scale and efficiency with varied hosting options
Returned to open-source model which has potential to speed innovation via deeper developer community relationship

Cons:

Using Elastic to its full extent requires significant expertise in the platform
Capabilities for richly understanding user intent directly within the platform are less extensive than peers
May require some external context engineering
Only one reference customer responded to Forrester's outreach

Kore.ai - Leaders

Pros:

True multiturn conversational search experiences shine
Deep and rich set of tools to understand user intent and shape experience accordingly
Strong capabilities to understand user behavior and fine-tune retrieval and results for optimal relevancy
Excellent set of development and evaluation tools for traditional search and for developing RAG and hybrid search pipelines
Huge R&D investment relative to size
Superior roadmap with vision to be the agentic operating system

Cons:

Establishing itself as a generalized agentic platform is a challenge in a crowded, evolving space
Deep experience in conversation may give advantage but broad agentic platform positioning needs validation

Mindbreeze - Leaders

Pros:

Robust pipeline for ingesting, understanding, and contextually indexing data
Rich set of data connectors with in-platform code assist tool for creating customized connectors
Foundations for building a rich agentic platform in the future
Only vendor offering a prebuilt search appliance
Comprehensive end-to-end cognitive search platform meeting needs of different enterprise client types
Customers report very positive experiences with the Mindbreeze team

Cons:

Current appliances don't come preconfigured with GPUs for AI workloads
Today's abilities to integrate more complex actions are largely limited to Mindbreeze's prebuilt application

Coveo - Strong Performers

Pros:

Strong models for relevance and reranking results across lexical and vector searches
Varied set of data connectors with detailed customizations for important enterprise data sources
Shines at understanding users' complex intents and needs
Strong implementation support and partnership in realizing business value
Good developer tooling and ease of use

Cons:

Vision lacks differentiation in a crowded market
Does not provide a knowledge graph natively
Has some catching up to do in innovating toward agentic AI
Could improve platform update communications

Glean Technologies - Strong Performers

Pros:

Exceptional knowledge graph
Superior capabilities for understanding user intent and determining relevancy
Most advanced capabilities for building an agentic flow within the platform
Powerful workplace search solution popular with end users
Ambitious vision for open platform transforming how customers work

Cons:

Connector set is narrower than many competitors
Fewer capabilities for enriching indexed data with further context
More limited set of prebuilt apps and off-the-shelf solutions
Needs to improve capabilities in scale and efficiency for broader use cases

Google Cloud - Strong Performers

Pros:

Rich capabilities to add context, metadata, and enrichment to incoming data
Long history with knowledge graphs
Third-party data service connections integrated directly into Agentspace
Rich developer-centric experience for constructing search capabilities
Tight collaboration loop with Google's DeepMind research group
Customers report high adoption rates in scaled enterprise deployments

Cons:

Must look more closely at enabling more industry and horizontal use cases
Agentspace only on market since fall 2024
Only one reference customer responded to Forrester's outreach

Lucidworks - Strong Performers

Pros:

Accuracy and tunability of search relevancy and results
Users can balance weight of keyword and vector search through simple interface
Rich usage analytics
Highly scalable and resilient global search
Responds proactively and flexibly to customer needs

Cons:

Vision lacks clarity on how to distinguish itself in agentic space
Specific focus on commerce-related agentic capabilities, an already crowded area
Must enhance native governance and orchestration capabilities for agentic market
Pushing beyond native capabilities can be challenging

Moveworks - Strong Performers

Pros:

Functionality to enable multiturn conversational search and take action stands out
Core search capabilities on par with peers
Strong set of data connectors for ingesting and parsing textual, structured, and document data
Positive experiences with platform tooling
Handles infrastructure and deployment complexities

Cons:

Roadmap lacks clarity and requires more focus on implementers skilled in additional use cases
Must expand breadth and depth to understand contents of indexed images, charts, or graphs
Some customers express concern over post-acquisition future by ServiceNow
Only one reference customer responded to Forrester's outreach

Sinequa - Strong Performers

Pros:

Rich set of data connectors
Powerful indexing and searching
Strong data pipeline with extensive capabilities for data enrichment, security, and privacy
Robust sets of prebuilt enterprise apps
Highly mature tooling enabling actions to be taken directly within search platform
Ability to scale search securely
Deep levels of customization for search experience

Cons:

Future is unclear under new ChapsVision owner
Some challenges with roadmap visibility and communication since acquisition
Integration with ChapsVision platform showing promise but not yet part of core offering

Algolia - Contenders

Pros:

Strong at delivering search results and scaling indexing and query needs
API enables easy integration into application capabilities
Can process a broad set of formats essential to enterprise use cases
Speed of implementation
Good communications with vendor

Cons:

Must adopt clearer functional roadmap for customers
Need to develop go-to-market strategy for differentiating in cognitive search market
Platform lags in depth of capabilities for understanding broad-based user context and intent
Lacks flexible controls to refine and focus relevancy and precision
Customers express concern at lack of clarity in roadmap
Did not provide reference customers

Amazon Web Services - Contenders

Pros:

Broad and deep range of data connectors
Enrichment capabilities for contextual search across data
Excels at managing security and governance of platform and data
Flexibility of using Bedrock's Knowledge Bases with wide array of LLMs
Flexibility of Kendra and Retriever APIs in search solutions

Cons:

Needs to look beyond agentic RAG to robust, action-taking agents
Must support increasing array of less-technical users developing knowledge apps
Customizations take skilled developers
Did not provide reference customers

Microsoft - Contenders

Pros:

Rich knowledge-graph-powered platform
Strong capabilities for hybrid search experience
Ability to build simple search experiences quickly in Copilot Studio
Ability to build customized and robust applications using Azure AI Search APIs
Positive feedback for workplace search capabilities

Cons:

Vision lacks clarity on how enterprises should use capabilities in concert
Must push harder on innovating around flexibility and breadth of platform
Must clearly stitch together technical and use-case stories
Customers report struggles understanding and fully leveraging Copilot Studio
Did not provide reference customers

OpenText - Contenders

Pros:

Ability to ingest widest set of indexable data types using semantic information (video and audio)
More significant capabilities for querying and analyzing structured data than peers
Broad set of prebuilt solutions and tools to integrate insights into other apps
Customers enjoy flexibility of platform
Automated governance capabilities for content

Cons:

Vision must expand to articulate how IDOL fits with rest of OpenText offerings and agentic world
Innovation must address positioning in action-taking agentic world
Did not provide reference customers

SearchUnify - Contenders

Pros:

One of the most robust and flexible knowledge graphs in evaluation
Rich core connector for variety of popular enterprise data sources
Robust and customizable preparation pipeline
Significant data collection and high-quality search analytics
Excellent roadmap focusing on agentic actions and making advanced RAG more transparent

Cons:

More limited set of prebuilt data connectors than many peers
Not all analytics directly available to customers and require vendor to provide them
Innovation strategy is more incremental with less proportional investment than competitors
Reference customers did not respond to Forrester's outreach

FAQs

Q: Which vendors are Leaders in 2025?

A: Elastic, Kore.ai, Mindbreeze

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

A: Leaders in the Forrester Wave are vendors that offer the strongest current product offerings and have the most robust strategies for the market. They demonstrate excellence across both current offering capabilities and strategic vision, with strong customer feedback. Leaders typically excel in key areas such as data connectivity, search capabilities, scalability, and have clear roadmaps for addressing the emerging agentic AI revolution. They are well-positioned to help enterprises implement, innovate, and increase their capabilities around AI-powered cognitive search.

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 2025

Cognitive Search Platforms Vendor Product Comparisons 2025

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FAQs

Q: Which vendors are Leaders in 2025?

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

Q: How do I interpret a Forrester Wave?

Reference