AIONOS CTO on building AI stack for enterprises at the India AI Impact Summit 2026

At the India AI Impact Summit 2026, AIONOS shared a clear view of how enterprise AI is evolving beyond chatbots and hype. Arjun, the company’s Chief Technology Officer, outlined a practical, productized approach to embedding AI into business operations—focused on measurable value, security, and scale.

Building AI where it truly matters

AIONOS positions itself as an AI-first company dedicated to bringing tangible value to enterprises. Its primary focus spans travel, transport, logistics, hospitality, telecom, and healthcare—sectors where data, people, and complex systems intersect. Rather than starting from monolithic foundation models alone, the company’s application stack is designed to map the critical touchpoints between users, data, and processes, then applies AI where it can meaningfully transform outcomes.

Productized solutions, designed for scale

Instead of building entirely bespoke solutions for each client, AIONOS follows an industry product approach: build for a sector, then deploy across multiple customers within it. This strategy accelerates time-to-value and reduces implementation overheads. According to Arjun, patterns discovered in one vertical typically translate well across others, making the company’s products reusable across the TTLH spectrum (travel, transport, logistics, hospitality), as well as telecom and healthcare. The result is a portfolio that can be adapted without starting from scratch for every deployment.

LLM-agnostic by design

While many enterprises consider developing proprietary large language models, AIONOS takes an LLM-agnostic stance. The company integrates a range of models—particularly open-source options—selecting what best fits the use case, data governance needs, and performance requirements. Arjun noted that the team actively works with models such as Llama, Qwen, and Kimi 2.5, emphasizing flexibility and rapid iteration over vendor lock-in.

This approach enables AIONOS to:

• Match models to domain-specific tasks and constraints
• Optimize cost-performance trade-offs across workloads
• Switch or upgrade models as capabilities evolve
• Maintain tighter control over data residency and compliance

Sovereign data and security take center stage

A defining trend over the past several months, highlighted by Arjun, is the push for sovereign data. Enterprises—and increasingly, entire countries—want assurance that sensitive information remains within national borders and under their control. Open-source models and LLM-agnostic architectures make it easier to deploy AI within on-premises or in-country cloud environments, aligning with regulatory mandates and internal risk policies.

By prioritizing sovereignty, AIONOS helps clients:

• Keep data local while enabling advanced AI capabilities
• Reduce exposure to cross-border data transfers
• Align with evolving compliance and audit requirements
• Retain transparency over model behavior and data flows

Beyond chatbots: operational AI for the enterprise

The company’s philosophy is to move past generic conversational bots and into operational AI that enhances productivity and decision-making. This includes orchestrating information across legacy systems, codifying workflows, and creating AI agents that assist teams in high-impact functions across TTLH, telecom, and healthcare. The emphasis is on reliability, repeatability, and integration rather than standalone demos.

Why the model stack matters less than the outcome

AIONOS underscores that the real differentiator is not merely the choice of model, but how well AI is integrated into the organization’s workflows. That means harmonizing data sources, defining clear decision paths, and ensuring AI outputs are explainable and actionable. The company’s stack is built to discover where AI adds the most leverage—then deliver it as a reusable, scalable product.

Key takeaways

• AIONOS focuses on enterprise-grade, productized AI for travel, transport, logistics, hospitality, telecom, and healthcare.
• The company’s application stack is built to connect people, data, and systems—deploying AI where it has the most transformative impact.
• Its LLM-agnostic approach leverages multiple models, including open-source options like Llama, Qwen, and Kimi 2.5.
• Sovereign data is a priority: organizations want AI without sending sensitive data out of the country.
• Value comes from integration and outcomes, not just model choice—favoring scalable, repeatable solutions over one-off builds.

In short, AIONOS is shaping an enterprise AI strategy centered on practical deployment, data sovereignty, and cross-industry scalability—an approach well-suited to the demands of modern, regulated, and data-rich sectors.