SPARK Matrix™: Enterprise AI Search, Q4 2025

ChapsVision has been recognized as a Leader in the SPARK Matrix™: Enterprise AI Search, Q4 2025, published by QKS Group.

This independent analyst report evaluates leading Enterprise AI Search platforms based on technology excellence and customer impact, highlighting solutions that deliver real, measurable value at scale.

This recognition reflects a strong focus on innovation within the Sinequa platform, customer value, and the real-world impact of AI-powered search as a foundation for the agentic enterprise.

Get complimentary access to the full SPARK Matrix™ report today.

Inside the SPARK Matrix™ Report

Access the full report to learn:

• Why Sinequa leads the way supporting the deployment of AI assistants and agents at scale, across industries such as manufacturing, life sciences, and financial services
• How Enterprise AI Search vendors are evaluated across technology excellence and customer impact
• Key capabilities and differentiators shaping the Enterprise AI Search landscape
• Insights to guide enterprise decisions around AI-powered search and knowledge access

What the SPARK Matrix™ Recognition Means

The SPARK Matrix™ is QKS Group’s primary competitive intelligence framework for technology markets — a structured evaluation methodology that maps vendors on a two-dimensional grid based on independently assessed scores for Technology Excellence and Customer Impact.

• Technology Excellence measures the depth, maturity, and differentiation of a vendor’s platform — its core architecture, AI capabilities, integration breadth, security posture, and innovation trajectory. A vendor that scores strongly on Technology Excellence has been assessed by QKS Group analysts as delivering a technically advanced, enterprise-ready solution.
• Customer Impact measures the measurable outcomes vendors deliver to enterprise buyers — including deployment success, user adoption, operational value realized, and customer satisfaction across industries and use cases. A vendor that scores strongly on Customer Impact has demonstrated that its platform performs not just in analyst evaluations, but in live enterprise environments at scale.

Vendors positioned in the Leader zone of the SPARK Matrix score highly on both dimensions simultaneously — meaning they combine a technically advanced platform with a proven track record of enterprise customer outcomes. The Q4 2025 edition evaluated the Enterprise AI Search market at a moment when agentic AI and retrieval-augmented generation had moved from emerging capability to enterprise deployment requirement.

Why This Recognition Matters Alongside Gartner and Forrester

Sinequa’s SPARK Matrix Leader recognition complements an independent analyst track record that spans multiple research firms and more than a decade:

• 2015–2022 — Leader, Gartner® Magic Quadrant™ for Enterprise Search / Insight Engines (six consecutive recognitions)
• 2019, 2021, 2023 — Leader, Forrester Wave™: Cognitive Search (three consecutive editions; highest Current Offering score in 2023)
• 2025 — Representative Vendor, Gartner® Market Guide for Enterprise AI Search

Each research firm brings a different evaluation methodology, buyer audience, and analytical lens. Gartner Magic Quadrant assessments are positioned toward strategic planning decisions by enterprise technology leaders. Forrester Wave evaluations are structured around detailed product capability criteria with explicit scoring weights. SPARK Matrix assessments by QKS Group focus specifically on the combination of technology depth and customer-demonstrated impact — bringing a practitioner-level perspective on what vendors actually deliver in production environments.

A vendor recognized across all three methodologies — with specific, differentiated findings in each — presents a qualitatively different credibility profile than one recognized by a single firm. For enterprise buyers assembling a shortlist, multi-firm independent recognition is a meaningful signal of consistent platform quality, not house-view analyst alignment.