India’s potential emergence as Krisp’s largest voice AI deployment market is not only a technology story. It points to a possible shift in where globally connected service work can be performed, as multilingual translation and accent-neutralisation tools reduce some of the barriers that have historically concentrated international call-centre activity in specific labour markets.
Krisp, an artificial intelligence-based audio processing company, expects India to overtake the Philippines as its largest deployment market in the next few years. The company’s co-founder and chief executive officer, Davit Baghdasaryan, said the transition would be driven by voice translation, which he expects to grow faster than the company’s other offerings. The United States is currently Krisp’s largest market.
The forecast remains a company expectation rather than an announced deployment plan. But the reported direction of travel is significant because it connects three developments: the expansion of multilingual customer-service systems, the measurable pursuit of lower interaction costs, and the possibility of locating service work in a wider range of Indian cities.
Krisp operates across three business verticals. Its AI meeting assistants record notes and transcribe meetings in real time. Its call-centre products provide background-noise cancellation and accent conversion. Its application programming interface supports communication, gaming and frontier AI platforms. At present, noise cancellation and accent conversion account for most of the company’s topline, while voice translation is the part of the call-centre business expected to drive the next phase of expansion.
That distinction matters. The current market is not based solely on replacing human agents with automated systems. Much of the reported use is centred on giving human agents tools to communicate more clearly across languages and accents. Krisp said its technology can translate 12 Indian languages, while six more are being refined. These systems are being used in call centres serving healthcare, retail, telecom and financial services.
The company’s argument is that voice technology can alter the location economics of service work. Vimal Nair, Krisp’s chief growth officer, said companies could appoint employees in Tier-I, Tier-II and Tier-III cities and serve customers globally without the same concerns about English proficiency or mother-tongue influence. If that model scales, the relevant urban question is not simply how many jobs artificial intelligence creates or removes. It is whether service-sector employment can become less dependent on a small group of established metropolitan labour markets.
India’s cities already have sharply different relationships with the services economy. Large urban centres provide deeper pools of English-speaking workers, specialised training, office infrastructure and business-process networks. Smaller cities may offer lower operating costs and large populations of potential workers, but employers have historically faced constraints involving language, training and access to globally standardised customer-service work. Voice AI is being presented as a technical response to some of those constraints.
The available evidence suggests that multilingual deployment is moving beyond limited experimentation. A survey by Nasscom and Krisp, cited in the report, found that more than one-third of organisations in India had moved beyond bilingual voice AI and were supporting at least three Indian languages in production. The survey also identified multilingual agents capable of handling four or more languages as an emerging deployment category.
The survey’s other important finding concerns how organisations measure these systems. The emphasis is shifting from activity metrics to business-outcome metrics, with efficiency becoming the central proof point. Sixty-one per cent of respondents reported a reduction of more than 20 per cent in cost per eligible interaction. Thirty per cent of scaled organisations reported a positive impact on revenue. The figures are survey findings and are not presented as an economy-wide measure of productivity, but they indicate why companies may be willing to move voice AI from pilot projects into operational systems.
For urban economies, cost efficiency can influence location decisions. If the cost and quality gap between major-city and smaller-city service operations narrows, companies could distribute teams across more locations. That would potentially widen access to international customer-service work, but the report does not establish how many jobs would be created, which cities would receive new operations, or whether existing work would be relocated. Those questions remain outside the evidence supplied by the company and the survey.
The technology also changes the role of language in the geography of work. Baghdasaryan said Indian agents had previously faced disadvantages related to accents and grammar when compared with workers in the Philippines. He argued that improvements in accent neutralisation and translation had reduced those hurdles. In this account, voice AI is not eliminating the need for local workers; it is attempting to make their speech more intelligible to international customers and to allow them to handle a wider range of languages.
That could be especially relevant to cities where workers speak several Indian languages but have limited access to globally oriented employment. A multilingual agent system can make linguistic diversity an operational asset rather than treating it only as a training challenge. However, the supplied report does not provide evidence on whether workers are receiving higher wages, whether the technology changes performance expectations, or how much control agents retain over customer interactions. These are important labour questions, but they cannot be resolved from the deployment figures alone.
The institutional structure behind this transition is also visible in the way the market is being described. Krisp’s products sit between software infrastructure and business-process operations. Companies in healthcare, retail, telecom and financial services use call centres to interact with customers, while technology providers supply the audio processing, translation and accent-conversion tools. The value chain therefore involves employers, business-process operators, software companies and workers rather than a single technology platform.
This arrangement could make deployment easier because companies do not have to build every capability internally. Krisp’s API business also suggests that voice tools can be embedded into larger communication and gaming platforms. But the report does not disclose the contractual structures, data-governance arrangements, workforce training requirements or regulatory approvals involved in these deployments. Nor does it establish how customer consent, translated speech and recorded interactions are managed.
The survey results point to a market that is becoming more outcome-oriented. A 20 per cent reduction in cost per eligible interaction is a direct incentive for call-centre operators, while reported revenue gains provide a reason to view voice AI as more than a back-office efficiency tool. Yet the difference between a successful deployment and a scalable urban employment model depends on factors that are not captured by cost metrics alone: the availability of reliable digital infrastructure, the ability to train workers, the quality of local office space and the capacity of smaller cities to support round-the-clock operations.
Krisp has raised $20 million since its launch in 2017 and has revenue of less than $500 million, according to the report. These figures place the company’s forecast in the context of a relatively young business rather than an already established national infrastructure programme. India’s possible position as Krisp’s largest deployment market would therefore reflect adoption by its customers and partners, not a government-designated technology corridor or a formally announced urban development initiative.
The larger urban question is whether digital tools can distribute service-sector opportunity without distributing only the lowest-value or most closely monitored work. The report provides evidence that multilingual voice AI is being used in production and that organisations are reporting efficiency gains. It also records a clear expectation that India’s Tier-I, Tier-II and Tier-III cities could participate more directly in international service delivery.
What remains uncertain is the scale and durability of that shift. The supplied evidence does not identify specific Indian cities, employment numbers, investment commitments or implementation timelines. It does show why India is being positioned as a major voice AI market: a large multilingual workforce, established call-centre activity and technology that aims to reduce communication barriers. The next evidence to watch is whether the reported deployment trend translates into named city-level operations, measurable workforce expansion and sustained business outcomes beyond pilot or early production use.
