Raise Financial Services (Raise), the Indian company building one of India’s fastest growing trading focused platforms – Dhan, investing platform – Millions, and AI-powered financial markets research assistant fuzz, among others, is developing multilingual voice experiences with Cartesia. Raise has been heavily investing in building AI specifically for Indian financial markets. Cartesia provides the speech models to extend that financial intelligence into natural, multilingual conversation.
The partnership supports an ambition both companies share: making financial information more accessible through conversations in the languages people use naturally.
Cartesia provides us best-in-class voice models that integrate well with the real-time voice infrastructure that we’ve built in-house. We’re looking forward to scaling this partnership across our ecosystem, including Dhan & Millions; trader and investor educational platform Upsurge; insurance distribution platform Pluto, and other group entities.
Building financial intelligence, choosing expertise in speech
Raise’s approach to AI started with the requirements of Indian financial markets. Its team developed artham, a 7-billion-parameter small language model trained exclusively on Indian Financial and Capital Markets data. Built and hosted in India, artham is designed around the structure, vocabulary, and regulatory context of the domestic Indian market. AWS (Amazon Web Services) recognized artham at its global developer event re:Invent 2025 in Las Vegas, USA.
Artham has been running inside financial co-pilot platform fuzz, stock screening platform ScanX, and Dhan for over a year, summarizing market news, reading context into price moves, and answering user queries with live data drawn straight from official sources.
Voice is the newest addition to that stack, and where Cartesia comes in. Cartesia’s models currently power a portion of Dhan’s voice bot interactions, including conversational KYC, while human teams continue to lead on servicing, complex queries, and relationship support.
The scale of multilingual demand is already clear: over 75% of these interactions happen in languages other than English, with Hindi alone accounting for more than half.
Access extends beyond language. Over a third of Raise’s voice bot calls are handled outside business hours, entirely without agent involvement, extending access to users who reach out in the evenings and overnight. These early numbers point to where voice is headed across Raise’s ecosystem.
Expressing financial information through speech introduces its own set of technical requirements. An accurate written response must retain its meaning when spoken aloud. Numerical expressions need an unambiguous pronunciation, and financial terminology must fit naturally into the surrounding language. Audio generation also needs to begin promptly enough to sustain a conversation.
Raise is building with Cartesia’s models to address the speech component of that work. The relationship gives the team that has built its own financial AI and production architecture access to specialized speech models purpose-built for multilingual conversation.
Multilingual voice infrastructure for the demands of Indian finance
Sonic, Cartesia’s text-to-speech model gives developers a choice of voices and regional accents, with controls for pace and emotional expression. It supports 11 Indic languages including Hindi, Tamil, Telugu, and Marathi, alongside language code-switching within a single generation. That flexibility lets teams adapt delivery to different contexts while preserving a consistent voice as a conversation moves between languages.
For financial content, expressiveness must be accompanied by precision. Cartesia’s work on transcript fidelity and locale-aware pronunciation addresses the treatment of alphanumeric identifiers, dates, and monetary amounts. These capabilities are particularly relevant when English financial terminology appears within an Indian-language sentence, where pronunciation and rhythm need to remain natural without obscuring the information.
Cartesia’s models are built for secure, high-volume, real-time workloads, with deployment options in India. With Cartesia, Raise runs these models within its own infrastructure, built to meet the data-handling and governance standards that financial applications require.
A relationship informed by design collaboration
Alongside its use of Cartesia’s models, Raise has been an early design partner and has contributed to our multilingual model development.
That collaboration brings a financial-domain perspective into speech research. Broad measures of naturalness do not, on their own, establish whether a model preserves numerical detail in a code-switched financial sentence. Domain expertise helps identify the cases that deserve closer evaluation.
For Cartesia, working with Raise connects model development to the requirements of a partner building financial products for Indian users. We look forward to supporting Raise’s voice development and continuing that technical collaboration as the partnership expands.