Deployment Strategist

Mark Susser of ElevenLabs on deploying AI agents successfully in Financial Services. At a time when more and more banks and financial institutions are integrating AI-powered solutions into their systems and processes, what separates successful deployments of AI technology from failed, expensive, or incomplete ones? At FinovateFall 2026, I sat down with Mark Susser, GTM Lead for Financial Services at ElevenLabs. In its conversation, Susser explained what separates successful AI deployments from those that stall, how companies can identify the right use cases for a given AI solution, and why modularity and meeting customers where they are are key. "The ones that stall out typically make one of two main mistakes: One is that they have too much of a broad platform initiative. Nothing specific to prove, no use case that has been picked out, and they're more focused on what the technology could do versus what the actual business needs. Another area that we see some faltering in is that they don't bring in their compliance partners from day one. What we find is that when compliance partners are brought in on day one, they're actually architecting the foundational elements that will make sure that it's successful and it'll get approved once it goes into production." Mark Susser is GTM Lead for Financial Services at ElevenLabs. ElevenLabs is an AI company that enables businesses to deliver seamless and intelligent customer experiences, with the integrations, testing, monitoring, and reliability necessary to deploy voice and chat agents at scale. The company builds voice and audio models and delivers them in a platform that can power every interaction across a company: from AI agents for sales, support, and business operations, to creative tools for marketing and media. Founded in 2022, ElevenLabs is headquartered in London.

SquadStack launches Arth V2 speech model for indian-language calls. SquadStack launched Arth V2, an in-house speech-to-text model for Indian customer calls. The company reported an 8.85% semantic word error rate and 67-millisecond latency on its own benchmark, while making the model available in closed beta. 12 Oct 2026 16:35 IST SquadStack.ai has launched Arth V2, a speech-to-text model designed for Indian-language customer calls, as the company seeks to reduce transcription errors in voice-based artificial intelligence (AI) systems used for sales and customer interactions. The company said Arth V2 recorded a semantic word error rate of 8.85% and a latency of 67 milliseconds on its internal Conversational Streaming Automatic Speech Recognition (ASR) Benchmark, which uses 863 real Indian sales calls recorded over 8 kHz telephone audio. According to SquadStack, the model produced fewer meaning-changing transcription errors than several competing systems, including models from Sarvam, Google, OpenAI and ElevenLabs. These results are based on the company's own benchmark and have not been independently verified. The Noida-based company said Arth V2 has processed more than one crore minutes of live customer calls over two months. The model is deployed within SquadStack's voice AI agents for enterprise customers and is also available in closed beta for organisations seeking to test it on their own call recordings. Benchmark focuses on indian-language conversations. SquadStack said its benchmark was designed to reflect conditions in Indian contact centres, where customers frequently switch between languages, speak over agents and make calls against background noise. More than 80% of the speech minutes in the test dataset are code-mixed, while nearly 90% contain background noise, according to the company. The recordings use narrowband telephone audio, which captures a more limited frequency range than the wideband recordings commonly used in speech-recognition testing. The benchmark measures semantic word error rate, which focuses on transcription mistakes that alter the intended meaning rather than treating every word substitution as equally consequential. SquadStack reported that Arth V2 recorded fewer meaning-changing errors than Sarvam Saaras v4, Google Gemini 3.5 Transcribe, OpenAI gpt-live-transcribe and ElevenLabs Scribe v2 Realtime. However, the release also acknowledged that Soniox v5 recorded a marginally lower error rate of 8.77%, compared with Arth V2's 8.85%. The company said Arth V2 delivered its final transcript within 67 milliseconds of a customer stopping speaking on 80% of turns. It reported that the model was faster than the competing systems in its tests, although the release did not provide a complete set of comparable latency measurements for every model. SquadStack said the benchmark's test audio, reference transcripts and scoring methodology are available on Hugging Face, allowing other developers to evaluate their own speech-recognition systems against the dataset. Company builds in-house Speech Recognition. Arth V2 is the latest version of SquadStack's in-house speech-recognition technology. The company said it trained the model from scratch using more than 60 crore minutes of Indian sales conversations, covering over 10 crore unique speakers. According to SquadStack, Arth currently handles 80% of the more than 50 lakh AI calls the company makes each day. Its chief technology officer, Vikas Gulati, said the company plans to release updated versions of the model every few weeks as it identifies and addresses transcription errors in production. The approach is intended to improve the speech-recognition component of voice AI agents, where an incorrectly transcribed amount, date or customer response can affect the system's subsequent action. Apurv Agrawal, co-founder and chief executive officer of SquadStack, said transcription remains a significant failure point for voice AI agents because downstream language models depend on correctly capturing what a customer says. The company provides voice AI agents for consumer sales and said it works with more than 60 large brands, including Kotak Mahindra Bank, PhonePe, Angel One and IndiaMART. The launch comes as companies deploy conversational AI systems for customer support, sales and other telephone-based interactions. For these applications, performance on real-world telephone audio may differ from results obtained using clean recordings or general-purpose speech benchmarks. Arth V2's broader performance will depend on how it compares with competing models across independently evaluated datasets, languages and use cases beyond SquadStack's own customer calls.