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For years, enterprises hired thousands of human executives to sell loans, answer customer queries and recover abandoned sales. Yet, even with massive teams, many high-value client interactions would slip through the cracks every single day. While some prospects were lost due to poor agent training, others were lost to long support queues. As a result, businesses consistently struggled to reach every lead on their lists. The common assumption was that companies simply needed better training for human agents or simply more calling executives. However, Arrowhead’s founders saw things differently. According to them, the core issue was that human-driven phone processes struggle to function effectively at enterprise scale. The realisation could not have come at a more opportune time. With rapid improvements in AI voice technology, they saw the future clearly – improving human-led call centres would soon matter far less than automating them entirely. This single realisation changed the direction of the startup. Founded in 2022 by Devyani Gupta and Vengadanathan Srinivasan, Arrowhead is building voice AI agents primarily for enterprises. It claims to power autonomous voice systems for many leading banks, NBFCs, ecommerce platforms and healthcare providers. The startup claims that its conversational agents automate everything, from loan sales and customer queries to abandoned cart recovery and collections. In January 2026, the startup raised $3 Mn in its seed funding round led by Stellaris Venture Partners. It claims to work with over 50 BFSI clients, including Tata 1mg, Bank of Baroda Cards, IndiaMART, Aditya Birla Capital, Paytm, Turtlemint, Kissht, InsuranceDekho and others. Yet, Arrowhead did not start with the technology it uses today. The startup’s pivot to enterprise voice AI was the result of a calculated gamble, which forced its team to rethink everything they had built. When Analysing Human Calls Was No Longer Enough Arrowhead didn't start in voice automation. The startup’s initial product was a conversation intelligence tool. Drawing on her experience at BCG across continents, cofounder and CEO Gupta initially built Arrowhead to identify mis-selling and compliance violations on recorded sales calls. Early clients, such as edtech platform upGrad, used the tool to pinpoint bad sales pitches that resulted in refunds. Then came the GenAI boom of 2023 and 2024, and the founders reached an uncomfortable conclusion. “We quickly realised that if we continued analysing just human sales calls, we would soon be analysing something that would potentially go extinct,” Gupta told Inc42. Recognising that human-driven call centres were on borrowed time, Arrowhead pivoted sharply to AI in late 2024. Rather than competing on building another chatbot, the startup focused almost entirely on voice-first enterprise workflows. Arrowhead initially targeted banks and financial institutions, where large calling operations remain critical for loan sales, collections and customer engagement. But, in a stroke of fate, many enterprises that originally bought Arrowhead's analytics software returned as buyers for its autonomous voice agents – this time, to replace their manual calling queues altogether. To scale its voice stack, Arrowhead quickly realised that off-the-shelf software simply would not cut it. The founders needed to control every layer of its underlying engine. Building A Voice AI Stack From Ground Up Instead of relying entirely on third-party API providers, Arrowhead has gradually built much of its voice AI infrastructure in-house. The startup recently launched internally fine-tuned small language models (SLMs), engineered specifically for live enterprise conversations. Rather than optimising purely for lower inference costs, the founders claim that the platform prioritises latency and conversational quality. According to the startup, its latest deployments have achieved response latency below 500 milliseconds, allowing conversations to feel significantly more natural during live phone calls. For context, even small delays in voice interactions can disrupt the flow of conversation, making latency a much bigger challenge than traditional text-based AI systems. To complement its language models, Arrowhead has also developed its own text-to-speech (TTS) models, powered by thousands of hours of custom speech recordings. The startup hired voice artists across specialised agencies to record speech across multiple Indian languages and emotional contexts. This primary data, instead of generic public datasets, enables Arrowhead’s AI models to master subtle human nuances, such as empathy, hesitation, filler words and natural speech rhythms, which generic TTS systems typically miss. Overall, its underlying AI stack also includes: Fine-tuned SLMs built on open-source foundation models such as Gemma and Qwen Proprietary TTS models trained using custom primary speech datasets Emotion-aware speech synthesis systems capable of responding with empathy and contextual tone Optimised inference infrastructure for sub-500 millisecond voice responses Support for multiple Indian languages, including Hindi, Tamil, Telugu, Kannada and Malayalam Enterprise deployments designed around data residency, privacy and single-tenant architecture for regulated industries Voice orchestration systems capable of integrating with multiple enterprise APIs during live conversations Speaking with Inc42, cofounder and CTO Srinivasan said that hosting proprietary language models provides cost savings, predictable latency and superior conversational quality while ensuring sensitive customer data stays safely within India for regulated industries such as banking. On the back of its in-house AI stack, the founders claim that over 90% of users cannot distinguish Arrowhead's voice agents from human representatives unless explicitly told that they are speaking with an AI agent. This conversational fluidity has enabled the startup to move beyond pilots to deploying its agents directly into critical, revenue-generating operations. Arrowhead’s BFSI Strength On the business front, financial services remain Arrowhead's biggest market. Around 80% of the company's revenue comes from banks, NBFCs and fintech companies, although it has also started expanding into sectors such as healthcare, ecommerce and edtech. Overall, Arrowhead’s voice AI platform is currently being deployed across three major enterprise workflows. The first is outbound loan sales, where its AI agents move far beyond basic lead qualification to complete much of the loan application journey. During a live call, the agent addresses customer objections, collects required documents, runs OCR-based verification and hands over fully completed files straight to underwriting. The second core area for Arrowhead is inbound customer support. Here, its AI agents first determine customer intent before tapping into the enterprise database to answer complex policy questions, fetch real-time loan statuses and retrieve account-specific records. This drastically cuts manual workload for enterprises that handle tens of thousands of support hours each month. The third area is abandoned cart recovery. For ecommerce platforms, Arrowhead’s AI agents contact customers shortly after they leave without completing a purchase. During the conversation, the system can modify cart items, apply discount codes, update shipping details, and process transactions without ever transferring to a human representative. The startup recently disclosed that its deployment with Tata 1mg generated conversion rates around 15% higher than human agents during abandoned cart recovery campaigns. But with healthy market traction and product-market-fit (PMF) in its kitty, how is Arrowhead monetising its customer base? How Arrowhead Makes Money Arrowhead employs a hybrid pricing strategy that blends traditional SaaS usage with performance-based monetisation. A portion of revenue is generated through per-minute billing, while another component depends on business outcomes delivered through AI deployments. The founders believe that outcome-based pricing creates stronger alignment with enterprise customers while reducing the long-term impact of declining AI infrastructure costs. While the founders did not disclose Arrowhead’s revenue figures, they said that usage-based contracts drive the majority of revenue and yield gross margins between 25% and 30%. They added that profitability varies depending on customer scale and committed call volumes. That said, the startup, despite operating with a lean team, has scaled rapidly to partner with most of the country's top banks, NBFCs, and fintechs. For the founders, replacing traditional call centres is just the opening move. Their bigger bet is that enterprise voice AI platforms will become completely autonomous, capable of completing entire business workflows on their own. But the road ahead may not be easy. The voice AI space in India is peppered with deep-pocketed homegrown startups such as Observe AI, Bolna, and Gnani.ai, among others. On top of this, unit economics also remains a real challenge, as LLM reasoning and TTS together can be an expensive purchase for early stage startups. Infrastructure and UX challenges also persist, particularly around mobile voice navigation and integration with existing enterprise systems. So, as India’s AI ecosystem matures and voice AI infrastructure turns into a commodity, Arrowhead is tasked with maintaining its tech edge to thrive in the long haul.
document fetched 2026-10-10 02:50 · canonical https://inc42.com/startups/how-arrowhead-is-using-voice-ai-to-fix-the-gaps-in-enterprise-sales-and-support