How Dezerv built post-call analytics for wealth management with Sarvam

Background

Wealth in India is growing faster than the expertise available to manage it. Dezerv was founded in 2021 to close that gap. The firm designs customised investment portfolios for its clients, offered through its SEBI-registered Portfolio Management Services starting at ₹50 lakhs, along with alternative investment funds for those seeking exposure beyond traditional markets. As of 30th June 2026, Dezerv manages over ₹17,000cr+ across PMS, AIF and distribution with a team of 650+ across five cities.

At the centre of the model is the relationship manager, and every client is assigned one. RMs explain portfolio decisions, hold clients steady through volatile markets, and carry the conversations on which long-term trust is built. Those conversations happen over video conferencing, in whichever language the client is most comfortable using.

The Problem

Dezerv’s conversations between relationship managers (RM’s) and clients were one of the company’s richest sources of customer insight. Every call captured what clients were concerned about, which explanations resonated, and where RMs could benefit from more coaching. Across thousands of calls each month, these conversations created a continuously evolving view of client needs and broader business trends. But without a reliable way to turn those conversations into searchable data, much of that insight remained untapped.

The previous pipeline was outsourced end to end, which meant Dezerv could not audit its own quality, and when the team ran its own comparisons, the transcripts fell short, especially on Indic languages. Nothing was queryable across the full call base either.

The transcription itself was the hard part. Dezerv's clients and RMs speak the way urban India speaks, mixing English, Hindi and regional languages within a single sentence, often with dense references to fund names and numbers, over video conferencing calls. With these details central to portfolio conversations, accuracy is critical, especially when it comes to numbers.

Why Sarvam

Dezerv didn't choose Sarvam based on product claims alone. The team benchmarked multiple leading Speech to Text platforms using open-source datasets, including AI4Bharat's Svarah, measuring both Word Error Rate (WER) and Character Error Rate (CER) across English, Hindi, Marathi, Telugu, and Tamil.

Sarvam consistently delivered the strongest performance, with three capabilities standing out during the evaluation.

• Accuracy on real Indian speech: Sarvam's Speech to Text models are trained on over a million hours of Indian audio, including code-mixed conversations, regional accents and telephony recordings. These are the same conditions Dezerv's calls present.
• Diarization built-in: Sarvam's Speech to Text model includes built in speaker diarization, automatically separating different speakers throughout a conversation. In wealth management, knowing who said something is just as important as knowing what was said.
• Scale without operational complexity: Dezerv submits large volumes of conversations asynchronously and receives structured, speaker-separated transcripts back, without building or operating any transcription systems of its own.

Apart from this, Sarvam’s speed of execution also stood out during the pilot. When Dezerv needed video conferencing transcription, a capability that was not supported at the start of the engagement, Sarvam added it within weeks. The same responsiveness carried through when Dezerv later needed support for calls longer than an hour, with the required infrastructure changes implemented in under a week.

The Solution

Dezerv integrated Sarvam through its SDK and today runs three production use cases:

1. Transcription of video meetings
2. Transcription of phone calls
3. Live note-taking for Relationship Managers (RMs) during client conversations

With client consent, recordings are processed via Sarvam's Speech to Text model, using the Batch API with speaker diarization. Every conversation is converted into a structured transcript that captures who spoke, what they said, and when they said it. Client data is managed under Dezerv's data governance framework, and Zero-Retention AI Processing ensures that no client-identifying information is ever stored with Sarvam.

From there, each conversation becomes part of a searchable record of the client relationship. RMs can instantly revisit earlier discussions without replaying hours of audio, giving them a clear view of what the client asked, what required further clarification, and the goals and concerns raised during the conversation. This gives RMs the context they need to carry each client relationship forward.

The Impact

With this pipeline in place, Dezerv now turns every client conversation into a searchable, speaker separated transcript within hours. Last month alone, the company processed close to 3.6 lakh minutes of speech through Sarvam, including around 3.2 lakh minutes of video meetings and 40,000 minutes of phone calls.

At this scale, Dezerv has also significantly improved transcription quality, reducing Word Error Rate by around 20% compared with its previous setup. Bringing the pipeline in-house gives the team direct visibility into performance and quality, while faster transcription turnaround has also enabled Dezerv to bring its insights module in-house.

Looking Ahead

Dezerv is building the wealth management firm India's professionals were never offered: expert-led, technology-first, accountable for outcomes. Learning from every client conversation is central to that vision. Every discussion now improves not only the relationship it belongs to, but also the firm's understanding of what clients need, where advisors can improve, and how better advice can be delivered at scale. As conversation volumes continue to grow and Sarvam advances its speech models for Indian languages, the value of that institutional knowledge will only compound.

Disclaimer: The statements contained herein express the views and opinions of Dezerv, based solely on Dezerv's internal data and personal experience utilizing Sarvam’s Platform.