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The era of rule-based conversational AI is dead: Ganesh Gopalan, Co-Founder & CEO, Gnani.ai

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In the fast evolving space of artificial intelligence, the integration of Generative AI into customer service chatbots has become a game-changer, redefining how businesses engage with their customers. Express Computer recently had the privilege of conversing with Ganesh Gopalan, Co-Founder & CEO of Gnani.ai, a company focused on AI-driven customer experience solutions. Ganesh sheds light on how Gnani.ai is leveraging Generative AI to revolutionize customer interactions and elevate the conversational AI landscape

Is Gnani applying Generative AI to enhance customer service chatbots, specifically in generating personalized responses and handling complex inquiries effectively?

Yes. Generative AI is integrated into Gnani.ai’s industry-leading AI CX platform, facilitating faster bot development and more accurate responses to complex queries. Through our Automate365 platform, Gnani.ai develops chatbots for both voice and text channels. Additionally, our Assist365 platform enables real-time agent assistance, where Generative AI serves as a co-pilot, aiding human agents during interactions.

In the context of automation, one example is the collaboration we have with an e-commerce company. While traditional intent-based chatbots could address inquiries solely related to products within the store, the introduction of Gen AI revolutionizes the customer experience. For instance, if a customer mentions having arthritis and seeks recommendations for suitable shoes, our Automate365 platform harnesses Gen AI capabilities to discern the specific shoe features beneficial for the customer’s condition. Subsequently, it suggests the most appropriate products available in the store tailored to the specific customer’s needs.

In the case of agent assistance, our Assist365 platform functions as a co-pilot, particularly exemplified in our collaboration with a Healthcare customer in the US. By leveraging past voice recordings of calls, we rapidly developed the capability to provide real-time assistance to agents in addressing queries. This streamlined approach significantly accelerated the time required for deployment, reducing it from months to weeks.

Do you think the development of Generative AI will or has impacted the field of conversational AI? Also, can Generative AI models effectively simulate human-like conversations, and what are the challenges involved?

Yes. The Gnani Platform employs an orchestration of intent-based and generative AI-based responses to effectively handle out-of-context questions, enhance human-like interactions, and ensure effectiveness. While the typical challenges of Gen AI include issues like hallucinations or accuracy issues and latency, at Gnani.ai, we mitigate these challenges through custom-tuned Gen AI models tailored to specific knowledge bases. By restricting the scope of the models, we address hallucination concerns. Additionally, we utilize various tuning methods to minimize latency, ensuring the best possible experience for end customers.

What according to you are the key differences between rule-based conversational AI systems and those powered by Generative AI?

The era of rule-based conversational AI is dead, paving the way for intent-based conversational AI models trained with sample data. This evolution enables bots to answer questions expressed in various forms. However, the latest advancement involves bots powered by either Gen AI alone or an orchestration of Gen AI and intent-based models. These hybrid models are quicker to develop, excel at handling out-of-context inquiries, and can exhibit more human-like conversational abilities.

How does Gnani’s Generative AI models handle context and maintain coherence in longer conversations compared to traditional conversational AI?

Gnani’s Generative AI models utilize conversation buffer summarization memory, which stores contextual summaries of ongoing conversations. This stored information is then used to generate coherent responses to user utterances. Regardless of the conversation’s length, all key aspects are retained and referenced, ensuring continuity and relevance throughout the conversation.

Are you leveraging Generative AI to address language barriers wrt to Indian market and improve multilingual conversational AI systems?

For the past four years, we have established dominance in the voicebot market across multiple Indian languages, leveraging our proprietary speech-to-text and text-to-speech AI models known for their superior accuracy compared to competitors. In addition, we have expanded our capabilities in generative AI and introduced our own gen AI models tailored specifically for Indian languages.

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