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.
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.
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 per cent with the AI model enabled to 92.1 per cent when the same enterprise knowledge was structured as a knowledge graph. Enabling the AI model, by comparison, improved correctness from 59.9 per cent to 62.7 per cent.
The evaluation was largely based on synthetic data and represents the company’s internal findings, not an independent benchmark.
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.
