We at Raise Financial Services (Raise), the Indian company behind stock trading and investing platform Dhan and AI-powered financial markets research assistant fuzz, among others, are developing multilingual voice experiences with Cartesia. We have been investing heavily in building AI specifically for Indian financial markets. Cartesia provides the speech models that help us extend that financial intelligence into natural, multilingual conversation.
The partnership between Raise and Cartesia supports an ambition both companies share: making financial information more accessible through conversations in the languages people use naturally.
“Cartesia gives us best-in-class voice models that integrate well with the real-time voice infrastructure we’ve built in-house,” said Alok Pandey, co-founder and CTO, Raise Financial Services. “We’re looking forward to scaling this partnership across our ecosystem, including Dhan; trader and investor educational platform Upsurge; insurance business Pluto, and other group entities.”
Building financial intelligence, choosing expertise in speech
Our approach to AI started with the requirements of Indian financial markets. Our team developed artham, a 7-billion-parameter language model trained 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.
Artham has been running inside fuzz, stock screener 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 this is where Cartesia comes in. Cartesia’s models currently power a portion of Dhan’s voice bot interactions, including conversational KYC, while our 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 our 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 our 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.
We are building with Cartesia’s models to address the speech component of that work. The partnership gives our team, which 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, we run these models within our own infrastructure, built to meet the data-handling and governance standards that financial applications require.
A relationship informed by design collaboration
Alongside using Cartesia’s models, we have been an early design partner to Cartesia and have contributed to its multilingual model development.
Through this collaboration between Raise and Cartesia, we bring 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. Our domain expertise helps identify the cases that deserve closer evaluation.
For us, working with Cartesia means the speech models we rely on are shaped by the real needs of financial products built for Indian users. We look forward to scaling voice across the Raise ecosystem and continuing the technical collaboration between Raise and Cartesia as the partnership expands.
