Loris

Name: Loris
Brand: Loris
Rating: 4.8 (12 reviews)

Loris turns conversations into customer and quality insights to help you improve experience, efficiency and automation.

Est 2017|Valuation $60M|Raised $19.1M|175 Ref Customers

LinkedIn 10K|Web traffic 2K|FTEs 47|HQ New York, New York, US

MarketsConversation Intelligence CS Quality Management & Coaching

01Competitive Landscape

Narrative analysis (where alternatives may excel)

- Observe.AI: may be preferable when real-time agent assist and integrated AI agents are a primary requirement - CallMiner: may be stronger for deep call mining, compliance monitoring, and mature enterprise speech analytics needs - MaestroQA (adjacent): may be preferable when the primary requirement is QA workflow management rather than broader conversational modeling - Qualtrics (adjacent): may be a better fit when VoC is dominated by survey-based and omnichannel experience management rather than contact-center-native QA

Market trends and adoption

The vendor's acquisition by a larger CX analytics provider indicates strategic interest in integrating digital journey analytics with conversation analytics. Buyers should monitor integration timelines and potential roadmap changes following the acquisition.

	Loris	Observe.AI	CallMiner
Starting Price	Not publicly listed (quote-based)	Not publicly listed (sales-led)	Not publicly listed (quote-based); third-party commentary has referenced historical package pricing that should be verified with vendors
Most suitable for	CX/support organizations seeking VoC and QA intelligence across channels; teams that expect vendor-supported model development and integration	Larger contact centers seeking combined real-time agent assist and post-interaction QA, with vendor packaging for AI agents	Enterprises requiring broad speech analytics, compliance monitoring, and QA automation at scale
Primary strength	Focus on identifying why customers contact and surfacing quality signals; Trust Center lists security artifacts	Explicit packaging for real-time and post-interaction capabilities and AI agent integration	Established vendor in speech analytics with deep category experience and flexible packaging for large enterprises
Key weakness	Limited public transparency on pricing, roadmap, and detailed feature matrices; limited independent review volume	Implementation effort and cost can be significant for some customers	Can be complex and costly to deploy; heavy implementation and modular pricing may increase TCO

01Competitive Landscape

Narrative analysis (where alternatives may excel)

Observe.AI: may be preferable when real-time agent assist and integrated AI agents are a primary requirement
CallMiner: may be stronger for deep call mining, compliance monitoring, and mature enterprise speech analytics needs
MaestroQA (adjacent): may be preferable when the primary requirement is QA workflow management rather than broader conversational modeling
Qualtrics (adjacent): may be a better fit when VoC is dominated by survey-based and omnichannel experience management rather than contact-center-native QA

Market trends and adoption

	Loris	Observe.AI	CallMiner
Starting Price	Not publicly listed (quote-based)	Not publicly listed (sales-led)	Not publicly listed (quote-based); third-party commentary has referenced historical package pricing that should be verified with vendors
Most suitable for	CX/support organizations seeking VoC and QA intelligence across channels; teams that expect vendor-supported model development and integration	Larger contact centers seeking combined real-time agent assist and post-interaction QA, with vendor packaging for AI agents	Enterprises requiring broad speech analytics, compliance monitoring, and QA automation at scale
Primary strength	Focus on identifying why customers contact and surfacing quality signals; Trust Center lists security artifacts	Explicit packaging for real-time and post-interaction capabilities and AI agent integration	Established vendor in speech analytics with deep category experience and flexible packaging for large enterprises
Key weakness	Limited public transparency on pricing, roadmap, and detailed feature matrices; limited independent review volume	Implementation effort and cost can be significant for some customers	Can be complex and costly to deploy; heavy implementation and modular pricing may increase TCO