Steps to Conversational AI
AI based conversation are entering all areas of customer touch points today to automate entire work-flows. Be it in lead generation and analysis, churn, recommendation, grievance addressal, AI is helping to decide the most optimised message to put across for a better customer relation. With 'deep' expertise in audio analysis and NLP we have helped clients to deliver value to customers through conversational AI
The infrastructure of the system is heavily dependant on the client calling/interaction systems. The main framework should be built around the content delivery system beacuse in this use case apart from the obviously important AI part, minimum latency is of utmost importance. Spark, scala, spark-NLP are some of the scalable combinations we use for low latency.
DL and ML Pipeline
Use custom built deep learning models to analyse audio and text feeds on various aspects like compliance , customer satisfaction, grievance addressal, faq search etc. This is created in a distributed set up with gpu computing enabled to ensure smooth functioning of the flow. Also we use scalable architectures to keep the cost at a minimum.
The strategy of delivery is very tricky here as for real time strategy and call script creation the agent must have insights instantaneously delivered. To enable very curated content delivered at real time we employ streaming models and api in a distributed computing set up and completely automate the AI flow through integration with calling systems.