What limits traditional quality control?
Contact-centre quality is usually checked by sampling: a supervisor listens to and scores a small share of calls. Problems in the remaining calls — repeat contacts, dissatisfaction, deviations from the script — stay invisible.
As a result, decisions rest on incomplete data and issues surface late.
What does AI call analytics do?
AI call analytics transcribes every conversation, measures customer and operator sentiment, scores criteria such as greeting, empathy, resolution and script compliance, and writes a short summary of each call.
The same approach applies to written channels: WhatsApp, Messenger and Telegram chats are analysed against the same criteria.
What results does it deliver?
Management sees trends across all calls: which topics drive repeat contacts, which operators need training, which script steps do not work. Supervisors spend their time on problem cases instead of random samples.
What is needed for a rollout?
Core requirements: high-quality Azerbaijani speech recognition, integration with the telephony system, a scoring model aligned with the organisation’s own criteria, and secure data storage.
A solution that runs on CPU servers lowers infrastructure cost and keeps data processing inside the organisation.