Enterprise AI Search is at a turning point. Once treated as an internal productivity layer, it is now becoming the intelligence backbone for copilots, generative AI systems, and agentic workflows.
Modern enterprise deployments index millions, and sometimes billions, of documents across collaboration platforms, CRMs, data lakes, structured databases, and legacy repositories. In this environment, search is now about delivering grounded, contextualized, and permission-aware intelligence that AI systems can safely act upon.
The Q4 2025 SPARK Matrix for Enterprise AI Search by QKS Group reflects this shift. Hybrid retrieval maturity, RAG-readiness, governance enforcement, deployment flexibility, and scalability across complex enterprise data landscapes play a key role in vendor positioning.
The New Standards for Enterprise AI Search Leadership
Across the matrix, three technical realities shape vendor positioning:
1. Hybrid and Multimodal Retrieval as Baseline
Keyword search alone is insufficient. Platforms must blend semantic, vector, graph, and structured retrieval while maintaining explainability and precision across heterogeneous datasets.
2. RAG Integration Beyond Experimentation
Search engines are now expected to power generative AI. That means citation grounding, hallucination mitigation, contextual ranking, and strict permission fidelity, especially in regulated environments.
3. Governance Embedded at Runtime
Document-level access controls, audit trails, encryption, and regulatory alignment are now considered operational safeguards when AI is embedded into enterprise workflows.
SPARK Matrix Leaders
The Leader quadrant reflects platforms that combine retrieval innovation with operational maturity. These vendors demonstrate scalable performance, RAG readiness, and embedded governance across complex enterprise environments.
Kore.ai
Kore.ai’s leadership positioning reflects its agentic-first architecture. Rather than treating search as a standalone function, it embeds retrieval within an orchestration framework for enterprise AI agents. Hybrid, federated, and multimodal search operate inside governed execution layers designed to preserve context and enforce role-based access.
Its differentiation lies in aligning retrieval with automation, observability, and AI governance, positioning search as discovery as well as a controlled operational intelligence layer.
ChapsVision
ChapsVision’s leadership status is anchored in the maturity of its Sinequa platform and its expansion toward agentic AI through ChapsAgents.
Sinequa combines keyword, vector, graph, structured, and multimodal retrieval within a unified relevance framework optimized for precision and explainability. With more than 200 enterprise connectors, it can ingest highly specialized systems across regulated industries, including aerospace, defense, life sciences, and financial services.
What distinguishes ChapsVision in 2025 is its measured balance: advancing toward agentic AI while maintaining strict permission fidelity and observability. For enterprises concerned about RAG risks, data exposure, and compliance violations, this emphasis on controlled innovation provides a practical advantage.
Implementation may require structured onboarding due to the platform’s breadth, but for mission-critical deployments, maturity and governance depth often outweigh rapid experimentation.
Coveo
Coveo positions itself as a contextual relevance engine. Its strength lies in dynamic re-ranking driven by behavioral signals and intent modeling. Enterprises that treat search as a continuously optimized system rather than a static feature often value Coveo’s feedback-loop architecture and adaptability.
Lucidworks
Lucidworks Fusion remains one of the most configurable enterprise search frameworks in the market. Organizations requiring granular indexing pipelines, advanced relevance tuning, and domain-specific customization often gravitate toward its flexibility.
This depth can increase complexity and cost, but for enterprises with dedicated search teams, it offers precision and long-term adaptability.
Elastic
Elastic’s leadership reflects its distributed architecture and scalability. Originally known for full-text search and observability, Elastic now integrates semantic and vector retrieval to support enterprise AI workloads.
Its extensibility and performance under high throughput make it particularly relevant for organizations unifying search with analytics, security monitoring, and operational intelligence use cases.
Algolia
Algolia’s strengths continue to revolve around performance, API-driven flexibility, and hybrid relevance models. Its ability to deliver near-instant search results at scale makes it effective in high-volume digital environments.
As enterprises extend search into AI-driven personalization and contextual discovery, Algolia’s balance of speed and semantic capabilities sustains its competitive position.
Glean
Glean positions enterprise search as a unified workplace knowledge layer. Its intuitive interface, rapid deployment model, and strong governance framework contribute to broad adoption across enterprises.
By integrating contextual intelligence with conversational interfaces, Glean aligns closely with organizations prioritizing ease-of-use and streamlined knowledge access. Integration breadth continues to expand, but enterprises with highly specialized systems may still evaluate connector depth during implementation planning.
IntraFind
IntraFind’s leadership reflects strong retrieval precision and flexible deployment models, including on-premises and hybrid environments.
Its integration of RAG capabilities with robust data protection controls makes it particularly relevant for enterprises prioritizing sovereignty, compliance, and secure modernization of legacy search environments.
Strong Contenders in 2025
The Strong Contenders quadrant includes platforms delivering robust retrieval capabilities and meaningful enterprise impact, though often with narrower ecosystem depth or specialized deployment strengths compared to the leaders.
IBM
IBM Watson Discovery combines semantic retrieval with domain-specific NLP and hybrid deployment flexibility. It performs strongly in regulated industries where explainability and governance alignment are essential.
While onboarding complex datasets may require planning and training effort, the platform demonstrates stable performance once configured.
Microsoft
Azure AI Search benefits from deep integration within the Azure ecosystem and supports RAG deployments via Azure OpenAI integrations. It delivers scalability, security, and governance alignment for Microsoft-centric environments.
However, broader connector coverage outside Microsoft-native systems may require additional customization.
Google Cloud Search excels within Google Workspace environments, leveraging strong natural language interpretation and productivity integration.
In heterogeneous enterprise ecosystems, integration depth and cross-platform extensibility become key evaluation factors.
AWS
Amazon Kendra aligns naturally with AWS-native enterprises, supporting hybrid keyword-vector search and RAG workloads within cloud infrastructure.
Its scalability and security alignment are strengths, though ingestion flexibility and enterprise search mindshare continue to evolve relative to specialized competitors.
OpenText
OpenText uses its information governance heritage to deliver enterprise search suited to large content repositories and regulated environments.
Deployment flexibility across on-premises, cloud, and hybrid models is a differentiator, though pricing and support consistency influence evaluation.
Yext
Yext’s enterprise AI search builds on its digital knowledge management lineage. Its strengths include usability, implementation simplicity, and intent-driven discovery.
Regional footprint concentration and premium pricing remain considerations for global enterprises.
Squirro
Squirro’s Enterprise AI Search unifies structured and unstructured data into a contextual intelligence layer. Supporting SaaS, private cloud, and on-premises deployments, it integrates with Salesforce, ServiceNow, Tableau, and other enterprise systems.
Through semantic enrichment, RAG frameworks, and its conversational assistant SquirroGPT, the platform delivers intent-aware retrieval while preserving data confidentiality.
Recent acquisitions of open.exchange and Synaptica strengthen geographic reach and enhance taxonomy and knowledge graph capabilities, supporting more structured and explainable retrieval.
Realizing full platform potential may require configuration and onboarding effort, but its combination of contextual intelligence, deployment flexibility, and secure architecture positions it as a credible and evolving contender.
What the 2025 SPARK Matrix Signals
The 2025 matrix reflects a market that has matured beyond experimentation.
Enterprise AI Search is now assessed as a strategic intelligence layer that determines how safely and effectively AI systems operate within enterprise boundaries.
Differentiation now hinges on:
- Connector breadth and ingestion depth
- Precision in hybrid ranking
- RAG governance and explainability
- Deployment flexibility
- Workflow embedding
- Scalability under enterprise load
The vendors positioned as Leaders in 2025 are those that combine innovation with governance, advancing AI-powered retrieval while preserving security, compliance, and operational stability.