The AI Orchestration Stack: How AIONOS Is Engineering Accountability Into Enterprise Models

When it comes to AI, it’s never about what’s achieved so far, the more important question is what’s worth building next. The realisation also gripped CP Gurnani after he called it quits to his corner office at IT giant Tech Mahindra. 

The realisation turned stronger by the day as he saw AI’s sweeping change unfold before him. The seasoned corporate executive eventually took the plunge in the $13.99 Bn AI orchestration market with dogged determination to develop a technology that transcends simple scalability using AI. The idea resonated with Rahul Bhatia, group MD of InterGlobe Enterprises, which flies international carrier IndiGo.

Gurnani and Bhatia eventually rolled out AIONOS in 2024 with the goal of building AI that is fundamentally purposeful, ethically accountable, and synced to the increasing complexity in  modern-day businesses.

While AIONOS focussed primarily on travel, transportation, logistics, and hospitality, and telecom, its secondary focus areas included BFSI and healthcare. In an era of rapid digital shifts, driving the AI orchestration market at a 22% annual growth rate to reach $82 Bn by 2035, the Agentic AI startup claims to be playing the “steady guide for businesses” towards meaningful transformation.

“Most organisations have data, analytics, automation, and human expertise operating in silos, limiting the real impact,” Gurnani said.

“The key opportunity lies in unifying this intelligence into orchestrated systems that connect insights to decisions and actions.”

AI orchestration is the coordination and management of AI models, systems and integrations, and covers the effective deployment, implementation, integration and maintenance of the components in a greater AI system, workflow or app.

AIONOS does not seek to compete in the crowded race of building raw AI models. Instead, it defines itself as an Enterprise AI Orchestration firm. The company’s core philosophy is rooted in a simple but profound observation: the modern corporation is not suffering from a shortage of AI tools, rather from a lack of cohesive, responsible systems that can bridge the gap between a digital insight and a real-world result.

The Four Pillars Of The AIONOS Stack

At the heart of AIONOS lies a hybrid AI stack that combines external foundation models with in-house orchestration and vertical intelligence layers. CTO Arjun Nagulapally explained the company’s architecture through a modular, four-layer stack. 

The Models Layer 

AIONOS doesn’t tether itself to a single AI provider. Instead, it utilises a best-of-breed approach, mixing high-end commercial models with flexible open-source Large Language Models (LLMs).

• Purpose-Built: Models are chosen based on the specific task at hand.
• Enhanced Intelligence: To ensure accuracy, these models are fine-tuned or use Retrieval Augmented Generation (RAG), which allows AI to look up for private enterprise data before answering.
• Future-Proof: The system is model-agnostic, where a better, cheaper, or faster model is released and an enterprise can swap it without having to rebuild their entire infrastructure.

Data And Integration Layer: UniWeave 

For AI to be useful, it needs to understand the business on a real-time basis. UniWeave serves as the link between AI and the core legacy systems like ERPs for finance or CRMs for customer data.

• Live Data Streams: Rather than relying on old training data, UniWeave feeds live, structured, and unstructured information into the AI.
• Operational Backbone: This ensures that AI agents make decisions based on what is happening right now such as current inventory or flight delays, rather than static, outdated prompts.

Orchestration And Control Layer: UniStack 

Think of UniStack as the Mission Control of the enterprise. As AI becomes more complex, businesses need a way to manage, monitor, and restrict it.

• Smart Routing: It directs every request to the most cost-effective and efficient model.
• Safety And Compliance: It enforces guardrails to ensure the AI stays on brand and follows company policy.
• Auditability: Crucially, UniStack records the reasoning pathway behind every major decision. If AI makes a material choice, the business can look back and see exactly why it did so, ensuring full accountability.

Agentic Application Layer: UniVerity 

This is the last mile where AI turns into a functional worker. While the lower layers handle the tech, this layer focuses on vertical skills that are specific AI agents designed for niche industry problems.

• The Marketplace: Through UniVerity, companies can package and even monetise specific data-driven skills.
• Industry Expertise: AIONOS builds specialised agents for complex tasks, such as:
  • Airlines: Managing Irregular Operations (IROPs) like mass cancellations
  • Marketing: Automating SEO and Answer Engine Optimization (AEO)
  • Operations: Coordinating maintenance or dynamic pricing in real-time

In effect, AIONOS does not compete at the foundation model layer. It competes at the orchestration and domain layer, turning enterprise stacks into AI-native, self-optimising systems without ripping and replacing legacy platforms.

Large Businesses In Complex, Regulated Space

AIONOS is not targeting early-stage startups or AI-native digital players. Its primary customer base consists of large and upper mid-market enterprises undergoing AI-led transformation. These include Fortune 500 corporations, global telecom operators, airlines and aviation groups, hotel chains and hospitality brands, travel management companies, large service organisations, BFSI institutions, and transport and logistics operators.

There are some common traits among these sectors such as legacy technology stacks, complex workflows, high regulatory oversight, and significant operational scale.

Rather than replacing the existing ERP, CRM or reservation systems, AIONOS aims to integrate them. Enterprises get reusable AI skills, governed rollout, and closed-loop optimisation across customer journeys and operations.

Nagulapally highlighted one of the use-cases to help understand where it intervenes. “Imagine a passenger calling to ask, ‘Can I move to a slightly later flight?’ The AI agent will identify the passenger, PNR, journey context, and status tier in real time by reading data from the airline’s reservation, loyalty, and revenue systems,” he said.

The agent resolves the request end-to-end, surfaces contextual ancillary offers, collects payment, updates backend systems and logs preferences for future personalisation.

“From the traveller’s perspective, one ‘Can I change my flight?’ call becomes a single, smooth experience,” he added. “For the airline, that same inbound service call shifts from pure cost to incremental ancillary revenue, higher CSAT/NPS, and better data on passenger preferences that feeds future personalization.”

For enterprises, the value proposition this example brings is to move from fragmented tools and manual workflows to a unified, agentic operating model.

An Inside View Of The AI Hype Cycle

The 2025-2026 AI hype cycle is shifting from general, experimental, Generative AI (GenAI) to specialised, agentic (AgenticAI) systems. And, Gurnani takes a measured approach to the evolving dynamics. 

Are companies over-indexing on scale? “In many cases, yes,” he said.

“There is a strong push to scale AI quickly, driven by market excitement and competitive pressure, but not enough focus on whether these systems are actually delivering lasting business value.”

He believes that AI provides lasting worth only when it addresses genuine challenges and fits seamlessly into professional operations. In his view, growth should naturally follow the value a tool provides, rather than being used as a metric to mask a lack of utility.

On the home turf, as the $7.63 Bn AI market grows 42.2% a year to reach $131.31 Bn by 2032, India emerges as the third-largest contributor to the global AI talent pool, attracting investment commitments of $20 Bn. “Yet, a significant share of the value being created still flows to foreign markets,” Gurnani said. 

“The next wave of Indian AI unicorns won’t come from copying Silicon Valley prompts, they’ll come from founders who treat India as the primary market, and not a pilot market.” 

For newer founders, Gurnani has a piece of pragmatic advice. “Don’t build AI just because you can, build it because it solves a real problem. Enterprises don’t buy AI, they buy outcomes.” With AIONOS, he is betting that the enterprise AI race will not be won by the largest model, but by the most accountable system.

Edited by: Kumar Chatterjee

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