Loris: catalog profile

What it does

Loris functions as an analysis layer that sits atop customer support communication channels. It uses natural language processing to ingest chat transcripts and email threads to categorize them based on sentiment, intent, and resolution quality. Rather than just tracking volume, the software attempts to surface the 'why' behind customer frustration or success. It provides dashboards that highlight coaching opportunities for agents, such as identifying where a conversation went off the rails or which empathetic responses yielded a positive outcome.

How people actually use it

In practice, support leads use Loris to automate the quality assurance process that traditionally requires manual oversight. Instead of a manager auditing a random five percent of tickets, Loris flags outliers for them. The tool is often used to identify recurring friction points in a product or service. If customers consistently struggle with a specific login step, the data from Loris makes this clear to product teams. Agents also use it to receive automated feedback on their phrasing, helping them identify if they are being too robotic or if they are failing to address specific customer concerns. It effectively turns the support department into a primary research arm for the rest of the company.

Where it falls short

Loris relies heavily on the quality and structure of existing text data. If a support team uses poor shorthand, improper tagging, or operates across highly fragmented channels, the insights may lack depth. It is also not a direct intervention tool; it provides the data to improve, but the heavy lifting of coaching agents or changing product workflows still rests on human shoulders. Users often report that the initial setup phase requires significant calibration to ensure the AI understands the specific nuance and tone required by their particular brand voice.

Whether it builds skill

Loris contributes to skill development by providing a mirror for agent performance. By offering objective, data-backed feedback on sentiment and resolution quality, it moves the conversation away from subjective critiques. Agents who engage with the provided insights can learn to identify their own patterns of communication and adjust their tone and problem-solving strategies in real time. However, the risk remains that users might rely on the tool as a crutch rather than developing the inherent instinct for customer empathy. If used as a compass rather than a map, it can improve the analytical capacity of support managers and the emotional intelligence of individual agents.