Indian voice AI company, Shunya Labs is developing smaller, specialised AI models to make enterprise voice applications more efficient, reduce processing requirements and enable deployment closer to where data is generated. Its technology combines speech recognition, voice generation, translation and structured enterprise knowledge across cloud, edge and on-device environments, including offline Hindi speech recognition.
As businesses move AI initiatives from experimentation to production, they face growing concerns around processing costs, data privacy, reliability and infrastructure. Shunya Labs aims to address these challenges by using specialised models for defined tasks rather than relying on large, general-purpose AI models for every interaction.
Four layers, one AI foundation
Shunya Labs’ technology comprises four interconnected layers. Its voice layer supports speech recognition, generation and translation, including accents, hesitations and multilingual conversations. The understanding layer interprets customer queries through a shared processing framework, reducing the need for repeated AI processing.
The knowledge layer organises enterprise information including customers, products, business rules and relationships into knowledge graphs. These enable systems to retrieve relevant information through defined connections and provide traceable answers. The delivery layer determines how queries are routed, checked and processed, and where the AI system operates.
The company believes this integrated approach can improve efficiency, security and operational control while helping enterprises deploy AI at scale.
Knowledge graphs and measurable accuracy
A key element of the platform is its use of knowledge graphs to structure enterprise information and connect data points, business rules and relationships. Instead of relying solely on a model’s ability to generate responses, the system retrieves information through defined connections, making the reasoning path easier to trace.
According to Shunya Labs’ internal evaluation, answer correctness increased from 62.7% with the AI model enabled to 92.1% when the same enterprise knowledge was structured as a knowledge graph. Enabling the AI model, by comparison, improved correctness from 59.9% to 62.7%.
The evaluation was largely based on synthetic data and represents the company’s internal findings, not an independent benchmark.
Reducing processing requirements and improving control
The platform routes routine queries to smaller, specialised models while reserving larger models for tasks requiring more advanced reasoning. Its delivery layer incorporates routing rules, safeguards to restrict access to relevant customer information, and internal evaluation checks before responses are released.
The architecture supports deployment in the cloud, on customer premises, at the edge, in disconnected environments and within mobile applications, including on standard processors without specialised AI hardware.
Based on its internal estimates, Shunya Labs says approximately 85% of queries handled by its system can be processed without invoking a large AI model. The company also estimates that its approach can require roughly 240 times less AI processing than its conventional design. These figures depend on the tasks, architecture and comparison methodology.
The Stanford AI Index Report 2025 reported that the inference cost of a system performing at a level comparable to GPT-3.5 declined by more than 280 times between November 2022 and October 2024, highlighting the wider industry’s shift towards more cost-efficient AI.
Bringing voice AI closer to users
Shunya Labs voice technology covers 55 Indian languages, supporting code-switching between each language and English, as well as 22 East and Southeast Asian languages. Its Radiance Universal model can detect and speak 216 languages.
Its Hindi speech recognition model is designed to run on smartphones without an internet connection, while compact text-to-speech models are being developed for on-device use in six languages. Additional capabilities include voice activity detection, speaker diarization, noise cancellation and accent conversion.
The company sees potential applications across banking and financial services, insurance, healthcare, telecommunications and customer support, where voice interfaces can help customers access information and complete workflows in their preferred languages.
Leadership perspectives
“The next phase of enterprise AI will depend on how effectively businesses connect intelligence with their own knowledge, rules and operating environments. Not every question requires a large AI model. We build voice, understanding, knowledge and delivery as one foundation, and delivery is the layer that decides whether an AI system survives contact with production. By combining specialised models with structured enterprise knowledge and flexible deployment options, businesses can explore AI systems that are more efficient and better suited to their operational requirements,” said RituMehrotra, Co-founder and CEO of Shunya Labs.
Shunya Labs continues to develop voice, language and knowledge technologies focused on efficient processing, multilingual interactions and flexible deployment, enabling organisations to retain greater control over their data and infrastructure.
“Most of the accuracy we measured did not come from a bigger model. It came from how the knowledge was represented. Switching the AI model on moved correct answers by about three points. Organising the same information as a knowledge graph moved them by close to thirty. The information was there all along; what changed was whether the system could reach it. We also publish where our own measurements stop holding, because a number is only useful if you know how it was obtained,” said SouravBandyopadhyay, Co-founder and Chief Scientist of Shunya Labs.