Call centers are one of the most expensive parts of global business operations. Companies across the US, UK, and Australia rely on thousands of human agents to make collection calls, lead qualification, process claims, or follow up with clients. While automation has transformed many back-office tasks, voice-based customer communication via call centers is still stuck in the past and is riddled with inefficiency, high attrition, and endless cycles of retraining.
It was this problem that caught the attention of Kumar Saurav when enterprises wanted bots that could talk, instead of having humans do mundane tasks. He started Vodex AI, betting that the next big leap in enterprise automation won’t be text-based, but speech.
Founded in 2022 and headquartered in Madhya Pradesh, Vodex AI was built to enable businesses to deploy AI-powered voice agents that sound natural, understand context, and integrate directly into existing enterprise systems. The startup has raised $2 million in a seed funding round from Unicorn India Ventures and Pentathlon Ventures.
Tackling the Hardest Part: Human-like Voice at Scale
“We saw clear pain points in call centers. Agents required constant training, turnover was high, and scaling teams up or down was costly. On top of that, many of the tasks like initial calls, claim processing, or collections were repetitive and didn’t really need a human at all.”
While the system worked, Kumar sheds light on the processes that come with it, which are hard scalability, skyrocketing expenses, riddled with problems like attrition and retraining.
Vodex AI was founded to close this gap. The platform enables businesses to create natural, human-like
voice agents that cut costs by up to 40% and save as much as 90% of the time.
Vodex’s platform focuses on delivering industry-ready AI agents with capabilities that go beyond generic voice assistants:
Domain-trained models from day one: Unlike generic voice AI that just plugs in GPT or Gemini, Vodex builds its own models trained for specific industries like sales, collections, or healthcare. That means the AI starts with expertise similar to a trained human agent, reducing the long onboarding and training cycles humans require.
Auto-learning instead of retraining: Human agents need periodic classroom-style training, and many still drop off. Vodex’s agents improve continuously with every call, automatically learning from real-world interactions without downtime.
Reduced errors and bias: Their proprietary algorithm controls hallucinations, ensuring the AI doesn’t make mistakes or provide misleading answers during customer-facing conversations. This tackles the accuracy and compliance issues that plague human teams.
“In a recent case study with a BNPL (buy now, pay later) company in the US, they handed us $2.1 million in debt across 140,000 people. With human agents, they typically recovered 23% in 30 days. Using Vodex, we recovered 27% in just three days, and by the 18th day, we had reached 92%. That’s the scale of impact we’re able to create.”
Scaling Call Centers with AI-Powered Voice Agents
Vodex AI’s clients are financial services and healthcare companies in the US. These are two industries where phone-based communication is critical and costly. In banking and collections, Vodex’s voice agents handle workflows like mortgage sales, payment reminders, and debt recovery tasks that traditionally require large call center teams. In healthcare, the product supports revenue cycle management by automating billing and insurance follow-ups, reducing errors and delays.
However, Vodex AI didn’t immediately land in finance and healthcare. In its early days, the team tested the product with small businesses ranging from retail shops to plumbing companies, often integrating it into CRMs like HubSpot or HighLevel. The idea was to see if conversational AI could support everyday SMB workflows.
But the experiments quickly revealed a mismatch. Building tailored solutions for small businesses demanded significant development time, yet the value created and the willingness to pay remained low.
"We started building this solution based on our initial research, but soon realized that it was taking too much time and effort. The value we were delivering to customers wasn’t enough, and the revenue we were earning didn’t justify the work."
This led the team back to market research, where they ventured into industries like hospitality and tourism. Kumar points out the clear return on investments that came from narrowing down on the financial services and healthcare sectors, where these industries showed a high acceptance of voice AI solutions.
Vodex’s philosophy then became one of rapid experimentation, quick pivots, and aligning product with proven market need. Kumar notes that the lessons he learned were clear: don’t build for what seems technically interesting or “cool.” Instead, focus on domains where the demand is strong, the pain points are clear, and the value creation is substantial.
Finding the Right Market and Monetizing AI Conversations
While building for enterprises, Vodex was quick to realise that they didn’t want a standalone voice AI platform. They wanted a system that fit into the tools they were already using.
Banks, healthcare firms, and BNPL companies all ran on their own CRMs, ERPs, and records systems, so Vodex was designed as an infrastructure layer accessed largely through APIs. For clients that do need dashboards, the product offers customizable UIs, but the core philosophy remains seamless integration over rigid workflows.
When Vodex AI first tested its product in India in 2022, most buyers compared it to basic IVR systems, signaling that the market was too early for natural voice AI. At the same time, the team was seeing strong inbound demand from US call centers and enterprises, making the US a far more receptive market to start with.
“A mortgage company in the US uses us for their sales and collections. If a customer misses a due date, the system automatically makes a collection call. And if someone promises to pay but doesn’t follow through, it triggers another reminder call,” Kumar says.
Unlike human teams, the AI doesn’t churn or require retraining, making it easy to scale up or down based on call volume.
Vodex currently works with 17 enterprise customers, with deployment models varying by organization. Some focus on creating agents, others on triggering calls, and others on analytics, where the core business model is built around usage. Customers are charged based on the number of minutes the AI spends in conversation with end users, whether live calls or voicemails.
“It’s the same way you’d pay for human hours, just translated into AI minutes,” Kumar explained.
GTM Strategy
Kumar says, “Over time, inbound demand also began coming through generative AI platforms like ChatGPT and Gemini, where prospective clients discovered the product after interacting with these systems.”
Bridging AI and Compliance for Real-World Use
In the early stages, the team’s biggest challenge was building something that worked outside of demos. “Ninety-five percent of enterprise AI products fail because they look good in a demo but don’t hold up in production,” Kumar noted. The reason, he explained, was that models trained on broad internet data tend to hallucinate when asked to handle specific, regulated tasks.
For a business use case, even a single wrong output could damage trust or trigger legal consequences, and that lesson became clear when Vodex first tried applying its conversational AI to debt recovery. Early pilots seemed promising until industry experts flagged gaps the team had overlooked. Debt collections in the US are governed by strict laws like the FDCPA and TCPA, and compliance failures could make the system unusable.
“We did not even know that these acts exist,” Kumar admitted. “That feedback came from our expert who first read this and asked us to create a model or a workflow for it.”
The product had to be rebuilt around compliance guardrails and domain-specific training data. The AI needed to recognize right-party contact, handle “do not call” requests, and avoid sharing sensitive details with the wrong person.
Over time, the team invested heavily in cleaning and training data for each domain and added real-time checks to prevent hallucinated responses from reaching end users.
Tracking Compliance, Quality, and Time
Vodex’s direction is not measured by a single number but by a culmination of different numbers for different sectors in which the platform functions. Kumar explained that the company instead uses what he calls an “overall metric.”
This metric combines compliance, conversation quality, and average call duration to evaluate how well its AI agents are performing for enterprise clients.
Kumar explains, “We mark every conversation on compliance, we create a score for it, then we also mark what the conversation quality was.”
The benchmark varies by use case. In collections, Vodex is satisfied if calls last more than 42 seconds, while in sales, anything above 47 seconds is considered strong. Customer support typically demands longer engagement, sometimes over a minute.
Rethinking Voice and Human-like Interaction
For Kumar, the next big frontier lies in how machines listen and respond. Despite the rapid progress of large language models, he believes they still fall short when it comes to voice.
The problem, as he sees it, is structural. Current models are built around text, breaking speech into tokens and then converting back into words. That process strips away the fluidity of human conversation, where listening, thinking, and speaking often happen in parallel.
“That’s how the human brain operates. We don’t just wait, we think while we speak, and we think while we listen. But when you speak to a voice agent today, you feel processed, not heard,” he says
His team is now experimenting with a different approach – a foundational model that can both hear and speak natively, without shuttling everything through text. The goal is to create more natural exchanges, where machines respond with the rhythm and presence of a real conversation.
Looking ahead, he also sees untapped potential in voice-driven interfaces. Entire applications, he argues, could be built or navigated by speaking, pointing to it as the blank space and to the vast opportunities that lie in designing user experiences where the voice itself becomes the primary input.
