Hall 6 Diaries: What India AI Impact Summit 2026 Looked Like From a Startup Pod

By Peush Bery — CEO, Xtreme Gen AI

Two summits exist at the same time

There are two versions of a summit like the India AI Impact Summit / Expo 2026. One plays out on the main stages—keynotes, polished narratives, and future-facing announcements. The other happens in the aisles, where people stop walking, look you in the eye, and ask the question that decides everything: “Can this go live for us?”

I experienced the second version because Xtreme Gen AI had a startup pod in Hall 6 at Bharat Mandapam, New Delhi. Hall 6 wasn’t about theory—it was about product friction, production readiness, and whether a demo can survive real calls, real customers, and real constraints.

Hall 6 is where “AI” becomes a product

Hall 6 had a different energy from the rest of the expo. It was dense, loud, and intensely practical—like a startup bazaar where every booth is a live A/B test. You could feel founders compressing months of engineering into 90 seconds, while buyers compressed their curiosity into three hard questions: what does it solve, how reliably does it work, and what will it cost at scale?

Standing behind a pod for hours changes how you talk about AI. You stop selling “capability” and start selling resilience. The conversation naturally shifts to latency, interruptions, fallbacks, integrations, and what happens when the real world behaves unlike your best demo environment.

The scale of the summit changed the odds for startups

This expo was not small enough to casually “visit.” It was big enough to navigate—multiple halls, multiple zones, and a steady river of footfall. That scale matters because it changes probability. In a smaller event, you meet mostly enthusiasts. In an event at this magnitude, you meet operators, founders, and leaders who are actively shopping for solutions to deploy.

For a bootstrapped startup, the value of scale is simple: one serious conversation can offset dozens of casual ones. In Hall 6, that tension was visible everywhere. Founders were juggling patience with urgency—because the right buyer might arrive at any time, and you only get one shot to make the product feel real.

The Feb 19 pause reminded everyone: infrastructure decides adoption

On February 19, the expo flow changed due to security logistics linked to VIP schedules and global leader movement. It was a reminder that large-scale adoption is always shaped by the layers people don’t romanticize—security, infrastructure, operations, and the constraints of the physical world.

As a voice AI builder, I couldn’t ignore the parallel. In production, the model is only one part of the experience. Routing, reliability, network behavior, failure recovery, and turn-taking control are often what decide whether users trust the system or abandon it.

Big tech looked superior, but startups looked inevitable

The big-tech zones showed what maturity looks like when you have years of research, infra, and tooling behind you. There’s a crispness to those demos that’s hard to match. But Hall 6 told the downstream story: how those capabilities become real businesses when teams package them into workflows that solve specific, repeated problems.

Across the aisles, you could see startups translating APIs into outcomes—healthcare workflows, education funnels, agriculture advisory, customer support deflection, lead qualification, appointment booking, collections follow-ups. The differentiator was rarely “who has the most advanced model.” It was “who can survive messy reality and still land the outcome.”

The practical theme that dominated: languages and conversational voice AI

From a commercial lens, the most repeated signal was languages and conversational voice. Not as a fancy demo, but as a real interface shift. India isn’t debating whether voice AI will be used. India is debating how fast it can be deployed across languages, accents, and code-mixed conversations that don’t behave like clean benchmark audio.

That’s why voice agents showed up everywhere—funded companies, bootstrapped teams, and India-first builders. In India, the phone call remains the fastest path to resolution. And when your population is multilingual by default, language capability is not a feature—it is the product.

Two philosophies emerged in voice AI: build the stack vs orchestrate the stack

Hall 6 conversations made one thing clear: there are two serious approaches to building voice AI businesses in India. One path is full-stack ownership—speech models, language models, and deep tuning for Indian linguistic diversity. The promise is control and long-term defensibility, especially as cost curves improve.

The second path is orchestration-first—treating STT, LLM, and TTS as swappable components, while building a strong control plane around them. This includes state machines, tool calling, interruption handling, fallbacks, CRM sync, and analytics. This approach tends to win when the buyer’s requirement is simple: “Don’t show me a lab. Show me something that can go live.”

Telcos made a quiet point: the network is part of your AI product

Another underappreciated thread at the summit was telecom. When you build real-time voice systems, the network isn’t background—it is the experience. Jitter, packet loss, routing variance, and last-mile instability can make a good model feel bad. In India, those conditions are common, which is why the telecom layer becomes central to large-scale voice AI adoption.

This is also why reliability engineering matters more than people expect. Voice AI isn’t judged like a web app. Users judge it like a human conversation. If it lags, interrupts awkwardly, or fails mid-call, trust drops instantly.

Everyone wants to go live, but production economics still creates hesitation

The most encouraging thing I heard across industries was intent: everyone wants to adopt voice AI. Businesses feel the leakage—missed calls, unqualified leads, manual follow-ups, appointment drop-offs, and slow resolution. They don’t want more dashboards. They want outcomes.

But the biggest friction point is still production price. At pilot stage, most systems look affordable. At scale, cost per minute and cost per resolved task becomes existential. My view is that costs will compress meaningfully over the next several months through competition and optimization, but the bigger unlock will be architectural: treating voice as a controlled pipeline, routing tasks intelligently, keeping turns short, and avoiding expensive loops.

What CTOs and product leaders should take back from Hall 6

After the demos fade, the right evaluation lens is not “who sounded smartest.” It’s “who survives real calls.” Real readiness shows up in end-to-end latency, interruption handling, failure recovery, observability, integrations, and a clear cost-control strategy once volumes increase.

Hall 6 taught a simple truth: the winners won’t only be model teams. They will be systems teams—builders who can make conversational voice reliable in India’s multilingual, noisy, high-variance reality.

Conclusion: Hall 6 felt like India’s builder heartbeat

Hall 6 was less about announcements and more about endurance. Founders weren’t selling AI. They were selling reliability—systems that can hold up when the world is messy. And that’s exactly why the future of language-first conversational voice AI in India looks bright from where I stood.

Not because the problem is easy, but because the demand is loud, the builders are many, and the ecosystem is aligning around what matters most: shipping systems that work in the real India.