Text-to-speech-based generative models have propelled the industry towards faster and more efficient consumer engagement and hiring. Yet, replicating human tone, emotion, and subtlety remains far off. Voice AI holds a unique promise, but complexities as well.
Pradhi AI Solutions, a Hyderabad-based startup, has been focused on pioneering a voice-driven, emotionally intelligent AI platform.
From Multigraphs to Market Models
The company’s roots lie in deep tech research. CEO & co-founder Vijayalaksmi Raghavan, in a conversation with AIM, recalled how her team translated abstract mathematical concepts, such as multigraphs, into deployable predictive models. These could monitor real-time metrics such as the melt flow index.
Their system is built in layers. It extracts over a hundred voice-based measures, refines them through network graph theory and statistical techniques, and delivers actionable insights.
This is more than academic. The model is designed for enterprise deployment, embedding intelligence into everyday customer and sales conversations. By focusing on extracting significance from voice, Pradhi AI allows organisations to see far beyond what traditional text-based interfaces can capture.
Moving Beyond Sentiment Analysis
Voice AI has long been associated with sentiment detection: happy, sad, or angry tones tagged at a superficial level. Raghavan argues this is shallow.
“Speech emotion recognition is an evolving field. Today, semantic analysis only tells you so much. A human can distinguish the difference between a flat ‘okay’ and an enthusiastic ‘okay!’, but a large language model can’t,” she explained.
Pradhi AI, in collaboration with IIT Delhi, is researching prosody — the rhythm, stress, and intonation of speech. These markers can indicate tension, hesitation, or emphasis. When modelled correctly, it can expand AI’s interpretive depth.
The implications are vast. In customer service, for instance, the need may not be to identify anger but to equip a bot with empathetic responses that mirror human interaction. “We’re very far away from that reality,” Raghavan admitted, “but our work is laying the groundwork to get there.”
India’s Multilingual Reality
One of the toughest challenges for voice AI is handling Indic languages. Unlike English, which has benefited from decades of corpus development and tokenisation research, Indian languages lack extensive digital datasets.
Some large models, such as Google’s Gemini stack, perform significantly better with Indic languages than others, Raghavan pointed out. Consequently, Pradhi AI has adopted a hybrid approach that combines augmented models with datasets, leverages heuristic methods for recognising dialects, and introduces a human-in-the-loop mechanism to achieve fine-tuned accuracy.
“Achieving the 98-99% accuracy levels for Indian languages will take time,” she said. “But until we go under the hood, embedding-level improvements, tokenisation, and larger datasets, the gap will persist.”
Privacy as a Cornerstone
If emotion recognition is a research challenge, data privacy is the commercial one. Enterprise customers are often hesitant to transmit voice data outside their controlled environments. Raghavan is acutely aware of this barrier.
She revealed a significant breakthrough. “From a data privacy standpoint, we do not make any API calls. That means the data remains within your environment only. All our models are locally installed. Plus, our data is protected with an advanced cryptography solution. The breakthrough is no API calls.”
Pradhi AI uses elliptic-curve encryption to secure audio both in transit and at rest. Clients can run the stack on-premises, ensuring compliance with strict privacy standards.
The Indian AI Ecosystem
Voice-first platforms in India face infrastructure barriers. Many enterprises are still adapting their user interfaces, designed originally for text, to accommodate voice inputs.
Raghavan likens the process to plumbing: “You can’t install a fancy tap until the pipes are laid down. For us, the first work often is just putting that infrastructure in place.”
Yet the enthusiasm remains high. Once businesses see the potential for natural and accurate interactions, they recognise the long-term value.
The funding climate for AI has shifted dramatically since 2023. Back then, a startup could attract investors merely by mentioning “AI” in its pitch deck. Investors demand a clear path to revenue now, says Raghavan.
Voice AI has received significant investment on the front-end side, making voices sound human, Indian, or empathetic. But voice as data input and voice analytics remain underfunded. That is precisely where Pradhi AI is carving its niche.
“We will look for institutional funding soon,” Raghavan said, “but right now our focus is on traction and building an ARR-driven business model.”
Her career reflects the startup’s layered approach. She worked in the corporate sector, transitioned to the non-profit world, and ultimately embraced entrepreneurship.
“Corporate impact can feel limiting,” she said. “Non-profits deliver scale but often depend on stakeholders and grants. As an entrepreneur, I can shape not just my organisation but also the behaviours of others through the products we create.”
This philosophy underpins Pradhi AI’s dual vision: advancing research in emotion recognition while creating tangible impact for enterprises.
Future of Voice AI with Emotional Intelligence
Pradhi AI is tackling the gap between human emotion and machine intelligence.
Its layered model stands apart in a domain still in its early stages.
The aim is not to replace human agents but augment them, providing better assessments of conversations and stronger decision-making for enterprises.
As Raghavan put it: “The trick is not in solving 80% of the use cases, but in knowing when the AI is dealing with the critical 20% it can’t handle yet. That’s why we always keep the human in the loop.”
In doing so, Pradhi AI is shaping not just India’s AI story but also the global debate on how machines can truly learn to listen.
The post How Pradhi AI Embeds Emotional Intelligence in Voice AI appeared first on Analytics India Magazine.
