Loris

What is Loris?

Loris is a conversation-intelligence tool that helps brands improve their customer service. It automates the review of customer chats, identifies recurring customer issues, provides agents with real-time guidance during interactions, and extracts insights from conversations. It can be tailored to different industries, including BPOs, Fintech and Financial Services, Retail & E-commerce, and Telecom. Loris uses AI models trained on large volumes of real customer service conversations to make service interactions more efficient, accurate, and satisfying.

Who created Loris?

Loris was founded by Guy Halevy, Seth Levine, Christina Drum, and Kavan Edwards and launched on December 17, 2021. The company aims to bring a more empathetic touch to customer conversations, using AI to improve the customer experience and make interactions smoother and more accessible. It is based in New York, U.S.A., and Tel Aviv, Israel.

What is Loris used for?

• Automating customer service processes using AI models trained on real conversations
• Reviewing chats automatically and pinpointing issues customers raise repeatedly
• Giving agents real-time support during live interactions
• Extracting insights from customer interactions and identifying root causes of problems
• Improving efficiency, consistency, and alignment with the brand’s voice
• Increasing customer satisfaction and productivity by handling more interactions at once
• Adapting to sectors such as BPOs, fintech, retail and e-commerce, and telecom

Who is Loris for?

• BPOs (Business Process Outsourcers)
• Customer-focused brands in fintech and financial services, retail and e-commerce, and telecom
• Customer service representatives and agents
• Customer Service Managers
• Customer Experience Analysts
• Customer service trainers

How does Loris work?

Loris uses a collection of AI models trained on actual customer service conversations. This allows it to automatically review chats, accurately pinpoint customer issues, and give agents advice in the middle of an interaction. It audits interaction data, finds the core customer issues behind contacts, and surfaces insights teams can act on. Because it is trained on real conversations rather than generic data, its guidance and analysis reflect how customers and agents actually communicate.