The Challenge
Contact centres handle high volumes of customer conversation that leaves no structured trace. Sentiment, intent and recurring topics were visible only through manual sampling of recordings, routine queries consumed agent time that could not scale, and there was no consistent way to act on what customers were actually saying.
Calls leaving no analysable record behind.
Insight drawn from a fraction of calls.
Agent time consumed by repeatable questions.
Customer needs visible only after the fact.
Approach & Solution
smartData built the platform on an Angular and MEAN stack with cloud infrastructure, converting spoken language to text instantly with support for multiple languages and accents. Automated sentiment analysis gauges customer mood and tone, with contextual understanding interpreting nuanced language rather than keywords alone.
An inference engine extracts actionable insight from conversations and applies predictive analytics to anticipate customer needs, while topic and keyword extraction surfaces what recurs across volume. Interactive bots handle routine interactions so agents take the conversations that actually need them.
Key Features
Speech converted instantly across languages and accents.
Mood and nuance interpreted, not just keywords.
Actionable insight extracted from every conversation.
Routine interactions handled without an agent.
Product Showcase
Final Outcome
Contact centres analyse every interaction rather than a sampled few, recurring topics and sentiment become visible across volume, and routine queries are absorbed by automation instead of consuming agent capacity.
