India AI Impact Summit 2026: Production-ready voice AI, not flashy demos, will define winners, says Gnani.ai CEO Ganesh Gopalan
AI Impact Summit 2026: Real-world deployment challenges such as latency, cost, and reliability remain the decisive factors separating viable products from experimental systems, says Gopalan
- Feb 17, 2026,
- Updated Feb 17, 2026 1:03 PM IST
India AI Impact Summit 2026 | India’s rapid adoption of voice-based artificial intelligence is creating a new class of enterprise applications, from commerce to financial services, that demand production-scale performance rather than flashy demos, according to Gnani.ai Chief Executive Officer Ganesh Gopalan.
Speaking to Business Today in an interview on the sidelines of the India AI Impact Summit 2026 on February 17, Gopalan said real-world deployment challenges such as latency, cost and reliability remain the decisive factors separating viable products from experimental systems.
“What really matters… is not what you show in demos, but what works at scale,” he said, noting that systems that appear impressive in demonstrations often struggle in production environments.
The comments come as Gnani.ai unveiled Inya VoiceOS, a 5-billion-parameter voice-to-voice model under the government-backed IndiaAI Mission, though Gopalan emphasised that enterprise adoption, not model size, will determine success.
Enterprise deployments and use cases
The Bengaluru-based AI startup is piloting voice-driven commerce systems that could change how consumers interact with digital platforms, moving beyond traditional customer-service automation.
Gopalan cited trials with a major e-commerce company to enable users to search, compare and purchase products through conversational voice interfaces.
“This is like an incredible opportunity… an ability to shop for hotel rooms using voice,” he said.
While voice AI has historically been used to automate call centres and routine processes, he argued the technology is now expanding into core business workflows.
Gnani.ai currently serves more than 200 enterprise customers globally on its legacy systems and has added over 100 customers during the current financial year, he said, with particularly strong adoption in banking and financial services.
Almost every category of lender, including private banks, public banks, non-bank finance companies and microfinance institutions, is exploring voice-based transformation, he added.
Latency, cost and engineering
Gopalan said production deployments are constrained by technical factors often overlooked in the race to build larger models.
Latency, the delay between a spoken input and the system’s response, remains a major obstacle for global providers operating through remote cloud infrastructure. Cost is another critical issue, as high operating expenses can make large-scale deployments uneconomic.
Companies that simply build interfaces on top of third-party models “can make demos work, but at production scale, they struggle,” he said.
Gnani.ai claims its systems achieve high efficiency even on advanced GPUs, enabling lower operating costs and large-scale deployment.
Compute power and government support
Gopalan said the company has used more than 1,000 graphics processing units at peak and has received allocations under government programs.
“At peak we will be using more than 1,000 GPUs you can say,” he told Business Today.
However, he downplayed the importance of headline GPU counts, saying expansion would depend on performance outcomes rather than spending.
“We are not here to consume, we are here to deliver,” he said.
Voice AI as workforce augmentation
Rather than replacing workers, Gopalan said many deployments focus on augmenting human capabilities.
Examples include training large sales forces through simulated voice interactions and providing branch employees with real-time knowledge assistance.
Companies in sectors such as home lending struggle to train thousands of frontline staff amid high attrition, creating demand for automated coaching systems, he told Business Today.
Safety, deepfakes and regulation
Rising concerns about voice cloning and AI-driven fraud have pushed companies to embed safeguards directly into their systems.
Gopalan said Gnani.ai does not train models on private or personally identifiable data and has implemented strict governance frameworks.
“In a typical AI system, we have 70% guardrails and 30% functional use cases,” he said.
The firm has also developed voice biometrics technology to verify that a caller is a genuine individual rather than a synthetic clone, positioning it as a secondary authentication factor for banks and other institutions.
On funding plans
Gopalan expects voice interfaces to become a dominant layer of human-machine interaction as adoption accelerates globally.
“This is the year of voice,” he said, predicting that engineering execution will increasingly determine which companies succeed.
He also indicated that the company is in discussions with investors for a potential funding round, though he declined to provide details.
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India AI Impact Summit 2026 | India’s rapid adoption of voice-based artificial intelligence is creating a new class of enterprise applications, from commerce to financial services, that demand production-scale performance rather than flashy demos, according to Gnani.ai Chief Executive Officer Ganesh Gopalan.
Speaking to Business Today in an interview on the sidelines of the India AI Impact Summit 2026 on February 17, Gopalan said real-world deployment challenges such as latency, cost and reliability remain the decisive factors separating viable products from experimental systems.
“What really matters… is not what you show in demos, but what works at scale,” he said, noting that systems that appear impressive in demonstrations often struggle in production environments.
The comments come as Gnani.ai unveiled Inya VoiceOS, a 5-billion-parameter voice-to-voice model under the government-backed IndiaAI Mission, though Gopalan emphasised that enterprise adoption, not model size, will determine success.
Enterprise deployments and use cases
The Bengaluru-based AI startup is piloting voice-driven commerce systems that could change how consumers interact with digital platforms, moving beyond traditional customer-service automation.
Gopalan cited trials with a major e-commerce company to enable users to search, compare and purchase products through conversational voice interfaces.
“This is like an incredible opportunity… an ability to shop for hotel rooms using voice,” he said.
While voice AI has historically been used to automate call centres and routine processes, he argued the technology is now expanding into core business workflows.
Gnani.ai currently serves more than 200 enterprise customers globally on its legacy systems and has added over 100 customers during the current financial year, he said, with particularly strong adoption in banking and financial services.
Almost every category of lender, including private banks, public banks, non-bank finance companies and microfinance institutions, is exploring voice-based transformation, he added.
Latency, cost and engineering
Gopalan said production deployments are constrained by technical factors often overlooked in the race to build larger models.
Latency, the delay between a spoken input and the system’s response, remains a major obstacle for global providers operating through remote cloud infrastructure. Cost is another critical issue, as high operating expenses can make large-scale deployments uneconomic.
Companies that simply build interfaces on top of third-party models “can make demos work, but at production scale, they struggle,” he said.
Gnani.ai claims its systems achieve high efficiency even on advanced GPUs, enabling lower operating costs and large-scale deployment.
Compute power and government support
Gopalan said the company has used more than 1,000 graphics processing units at peak and has received allocations under government programs.
“At peak we will be using more than 1,000 GPUs you can say,” he told Business Today.
However, he downplayed the importance of headline GPU counts, saying expansion would depend on performance outcomes rather than spending.
“We are not here to consume, we are here to deliver,” he said.
Voice AI as workforce augmentation
Rather than replacing workers, Gopalan said many deployments focus on augmenting human capabilities.
Examples include training large sales forces through simulated voice interactions and providing branch employees with real-time knowledge assistance.
Companies in sectors such as home lending struggle to train thousands of frontline staff amid high attrition, creating demand for automated coaching systems, he told Business Today.
Safety, deepfakes and regulation
Rising concerns about voice cloning and AI-driven fraud have pushed companies to embed safeguards directly into their systems.
Gopalan said Gnani.ai does not train models on private or personally identifiable data and has implemented strict governance frameworks.
“In a typical AI system, we have 70% guardrails and 30% functional use cases,” he said.
The firm has also developed voice biometrics technology to verify that a caller is a genuine individual rather than a synthetic clone, positioning it as a secondary authentication factor for banks and other institutions.
On funding plans
Gopalan expects voice interfaces to become a dominant layer of human-machine interaction as adoption accelerates globally.
“This is the year of voice,” he said, predicting that engineering execution will increasingly determine which companies succeed.
He also indicated that the company is in discussions with investors for a potential funding round, though he declined to provide details.
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