Indian startup Smallest.ai is tackling one of voice AI’s biggest challenges — latency — by building smaller, purpose-built models that enable near real-time conversations for enterprise use cases such as call centres and customer support.
Voice AI has made rapid strides in recent years, but for enterprises deploying it in real-world environments, one problem has remained stubbornly persistent: latency. Even slight delays in speech responses can break conversational flow, frustrate users, and render AI assistants unusable in high-stakes settings like customer support or sales calls. New Delhi–based Smallest.ai believes the solution is not bigger, more powerful models — but smaller, tightly optimised ones.
Founded to address enterprise-grade voice automation, Smallest.ai is betting against the prevailing trend of scaling ever-larger foundation models. Instead, it is building compact, task-specific AI models designed to respond faster, consume fewer resources, and deliver human-like conversations with minimal delay.
Why Latency Is the Achilles’ Heel of Voice AI
In voice-based systems, latency compounds across multiple layers: speech-to-text, natural language processing, reasoning, and text-to-speech. Large language models, while powerful, introduce delays because of their size, inference costs, and dependence on cloud infrastructure. In consumer chatbots, this may be tolerable. In live phone calls, it is not.
For enterprises, especially call centres handling thousands of simultaneous conversations, even a one-second lag can translate into dropped calls, lower customer satisfaction scores, and operational inefficiencies. This is the gap Smallest.ai is targeting, focusing on responsiveness as a core product metric rather than an afterthought.
Smaller Models, Faster Conversations
Smallest.ai’s approach centres on building small language and speech models trained specifically for voice interactions. By stripping away unnecessary general-purpose capabilities and focusing narrowly on conversational tasks, the company reduces compute overhead and inference time.
These models are designed to work closer to the edge, reducing round trips to distant servers and enabling near real-time responses. The result, according to the company, is voice AI that feels less like a machine waiting to process a query and more like a human agent reacting instantly.
This philosophy runs counter to the dominant narrative in AI, where scale is often equated with quality. Smallest.ai argues that for voice, relevance and speed matter more than raw parameter counts.
Enterprise Use Cases Drive the Design
Unlike consumer-facing voice assistants, Smallest.ai is building primarily for enterprises. Its customers include businesses deploying AI agents for customer support, lead qualification, appointment booking, and transactional conversations.
In these environments, accuracy must coexist with speed. A perfectly reasoned response that arrives too late is functionally useless. By optimising models for specific workflows — such as resolving common customer queries or handling scripted interactions — Smallest.ai aims to deliver consistent performance at scale.
This focus also allows the startup to integrate more deeply with enterprise systems, from CRMs to telephony infrastructure, without the cost and complexity typically associated with large AI deployments.
Cost, Scale, and Reliability
Latency is not the only advantage of smaller models. Lower compute requirements translate into reduced costs, making large-scale deployment financially viable for enterprises. It also improves reliability, particularly in regions with inconsistent connectivity, where dependence on heavy cloud-based inference can become a bottleneck.
For emerging markets, where many enterprises operate on thinner margins and less robust infrastructure, this model-first efficiency becomes a competitive differentiator.
A Contrarian Bet in the AI Arms Race
Smallest.ai’s strategy reflects a broader, emerging rethink in the AI ecosystem: that bigger is not always better. As enterprises move from experimentation to production, practical considerations like latency, uptime, and unit economics are beginning to outweigh benchmark performance.
By anchoring its product around small, fast models, Smallest.ai is positioning itself for this shift, especially as voice AI adoption accelerates in customer-facing roles.
What Comes Next
As voice AI becomes a core interface for businesses, the winners are likely to be those that can deliver seamless, real-time interactions at scale. Smallest.ai’s bet is that solving latency through model design, rather than brute-force scaling, is the path forward.
In an industry enamoured with size, the company is making a quieter, more pragmatic claim: that sometimes, the smallest models can make the biggest difference.
