Plivo Executive Details Voice AI’s Unique Challenges
Bengaluru, India — Plivo, a leading communications platform, recently highlighted the distinct and complex challenges inherent in developing voice artificial intelligence (AI) systems. Speaking at DevSparks Bengaluru 2026, Ayush Anand, Head of Product Voice at Plivo, emphasized that unlike traditional digital interfaces, voice AI demands that technology adapt to human communication, a fundamental inversion that significantly complicates its development.
Anand explained that while users typically learn to navigate digital interfaces like Instagram or chatbots, voice interactions require AI to understand human speech patterns, including diverse languages, code-mixing, pauses, and reflections. This human-centric adaptation positions voice AI as one of the most technically demanding frontiers in the current technology landscape.
Technical Hurdles: Tolerance Gap and India’s Linguistic Complexity
A key differentiator for voice AI is the significantly lower tolerance for delays and errors. Anand illustrated this by noting that a five-second delay in a chatbot response is barely noticed, whereas a two-second delay on a phone call can instantly lead to user frustration. The stakes for misinterpretations are also considerably higher.
On a chat interface, errors are visible and can be corrected by the user. However, during a voice call, a misheard query can send the AI agent “on its own tangent,” with the user often unaware of the misunderstanding. Anand cited the common Indian city name “Mumbai” as an example, frequently misheard by global voice models despite its prevalence, underscoring the difficulties with more nuanced local names and places.
The challenges for voice AI are particularly acute in markets like India, which presents a structurally tougher environment for development. Anand pointed to India’s 22 official languages and the prevalence of code-mixing, where approximately 60% of calls switch between languages such as English, Hindi, Tamil, or Bengali, sometimes within a single sentence.
Most global AI models are predominantly trained on English, with some representation for Hindi. However, data for many other Indian languages, like Odia or those spoken in the country’s northeast, is scarce. This data deficiency cannot be adequately addressed through simple engineering prompts, further complicating the development of robust voice AI solutions for the region.
Architectural Demands and Plivo’s Solution
The technical architecture underlying a single voice interaction is remarkably complex, involving a cascade of seven distinct models operating in real time. These models, often distributed across different servers and geographies, include noise isolation to filter out background sounds, turn detection to identify when a user has finished speaking, speech-to-text conversion, a language model, and text-to-speech synthesis.
All these layers must be processed within a stringent 750-millisecond window. Anand highlighted the compounding effect of these multiple stages: even if each individual model achieves 99% accuracy, the overall pipeline’s accuracy drops to approximately 93%. In contrast, a typical chat agent processes roughly one such model, illustrating the significantly higher computational and accuracy demands of voice AI.
Plivo, with its extensive background in voice infrastructure for contact centers and call-routing systems, is now extending its developer platform to address these voice AI challenges. The platform enables development teams to integrate their preferred speech-to-text (STT), text-to-speech (TTS), and large language model (LLM) providers through a unified layer.
For organizations with sufficient proprietary data, Plivo’s platform supports building custom in-house models. Additionally, managed agents offer a faster path for builders to establish a voice agent from a prompt, facilitating rapid prototyping and iteration without extensive development cycles. The platform is designed to serve both technical developers and non-technical teams requiring agent management without coding.
Future Outlook for Voice AI
While speech-to-speech models, which could potentially integrate this cascade into a single system and reduce latency, are a future prospect, Anand cautioned that these models are “not ready yet” for widespread application.
The session concluded with a live demonstration of a clinic appointment reminder agent, a straightforward use case. Anand clarified that this example was not fully representative of the complexities of real-world calls, which often involve user frustration, anxiety, and unscripted human behaviors that rigorously test every layer of the voice AI stack. Teams interested in observing production-grade voice AI solutions were directed to Plivo’s booth, where live demonstrations of solved use cases were available. This practical insight underscored Plivo’s commitment to advancing voice AI capabilities in challenging environments.