Auto Call Answering Auto Call Answering

Gnani’s enterprise-ready auto call answering with it’s cutting edge NLP engine understands the intent and context of the callers. The ability to interactively answer complex questions delivers the right solution to the customer. This increases agents’ efficiency and helps reduce the overall cost for the company. Cognitive call/contact centers use AI technologies for Continuous Speech Recognition (CSR), Natural Language Processing (NLP), Speech Synthesis, (Text-to-Speech), Voice Biometrics, etc. Intent identification for auto call answering in customer support is critical for conversational systems. Gnani's Intent Identification engine helps ease that task by determining the intent of the caller and accomplish the specific tasks thereby providing superior customer experience

*Please note that member solutions are often customizable to meet the needs of individual enterprise end users.



  • Domain-Specific Speech Engine
    Gnani’s AI-powered speech recognition and the contextual NLP engine transform user engagement. It can be tailored to fit in perfectly with the required domain and supports multiple Indic languages like Hindi, Kannada, Tamil, English, Telugu & Gujarat
  • 24/7 Customer Support
    Customer queries are sorted 24/7, reducing the number of calls handled by human agents and capable of handling large volumes of inbound and outbound calls. Queries that require agents' assistance are seamlessly rolled over to the respective agents.
  • Get Real Time Insights
    Every conversation can be captured, analyzed and aggregated to deliver real‑time insights.


WorldwideBrazilFranceGermanyIndiaJapanKoreaMainland ChinaMexicoOther - Asia PacificOther - Europe and AfricaOther - North and South AmericaTaiwanUnited KingdomUnited StatesAI Software/SaaSCommunicationsCross-IndustryFinance and InsuranceConversational Bots and Voice AgentsCSP - Amazon Web ServicesCSP - Google CloudOn-premise (Private Cloud, Other)Intel® Xeon® Scalable ProcessorIntel® Distribution for Python*Models can be trained - requires labeled dataLinux ProprietaryMachine Learning