Год: 2025

Only 5% of India’s global capability centres (GCCs) qualify as mega GCCs, yet these 88 centres account for half of the country’s entire GCC workforce, according to Zinnov’s new report titled ‘Unlocking the Advantage of Mega GCCs’. And their influence is set to grow.

More than 230 additional companies are expected to join this elite cohort over the next five years.

To understand this shift, it is necessary to revisit what a GCC represents. GCCs are offshore units set up by multinational companies to run strategic, knowledge-intensive functions, spanning technology, engineering, digital operations and shared services, drawing on India’s vast talent pool and operational strengths.

Mega GCCs sit at the top of this hierarchy. These are centres with more than 5,000 employees and supported by parent organisations generating over $1 billion in global revenue. They stand apart because of their scale, influence and global impact.

This brings us to wonder how India’s GCCs are entering a new era defined not by scale alone, but by strategic depth, global ownership and enterprise-wide innovation mandates.

Talking to AIM, Namita Adavi, partner at Zinnov, mentioned that mega GCCs stand apart because “they operate with strategic intent and deep global ownership.”

These centres are no longer mere extensions of global teams. They replicate the enterprise itself, shaping architecture, influencing global decisions and delivering high-value capabilities across areas such as AI, cloud, cybersecurity and digital engineering.

What Makes a Mega GCC Different?

Mega GCCs are distinguished not just by size, but by the criticality of the work they lead.

Spanning BFSI to automotive sectors, these centres anchor global functions, run large-scale platforms and accelerate modernisation agendas.

Piyush Kedia, co-founder and CEO of InCommon, told AIM that “mega GCCs that work well have one thing in common: they focus on ownership, not output—and that shift changes everything.”

Over a three to five-year horizon, he noted, these centres often become the place where enterprises test new bets, incubate cross-functional pods, and groom future global heads of engineering, product and operations.

Adavi highlighted similar trends, pointing out that JPMorgan Chase’s India GCC runs core platforms for payments, risk, and digital operations. Amazon’s India teams drive rapid experimentation for customer experience and personalisation systems deployed worldwide.

Meanwhile, Wells Fargo’s engineering teams shape global risk, fraud and servicing platforms. In the automotive industry, Mercedes-Benz Research and Development India leads key components of the software-defined vehicle roadmap.

Close to 90% of mega GCCs sit in the portfolio or transformation stages, where India teams influence architectural decisions and modernisation programs.

“Leadership models set them apart as well. Target’s India leadership, for example, manages both centre operations and global product or engineering portfolios, turning the GCC into a launchpad for enterprise-wide innovation and leadership,” Adavi added.

Why a Few GCCs Capture Nearly Half of India’s GCC Workforce

Mega GCCs scale because they operate as fully integrated replicas of the global enterprise.

Adavi noted that semiconductor leaders such as Intel, Qualcomm and Texas Instruments run design, verification, silicon engineering and AI-driven tooling under one roof in India. This vertical integration makes India the technical heartbeat of their global R&D.

Furthermore, Cisco leads network automation, cloud operations and advanced security engineering from its Indian centres, while Amazon and Target unify product, engineering, data and operations to strengthen end-to-end execution.

At the same time, HSBC and Wells Fargo maintain tight global alignment while driving India-led innovation and local talent strategies.

Long-term presence also matters. Mature engineering-led GCCs have built deep competency in digital platforms, enterprise systems and complex R&D. With scale, strong governance, and high-value mandates, these centres naturally evolve into engines of global transformation.

Neeti Sharma, CEO of TeamLease Digital, revealed in a conversation, “Most of them have been in India for over a decade, having started with support and technology functions and now gradually becoming innovation and research hubs for their HQs.”

Today, over 1,800 GCCs hire more than 10.4 million employees for the most niche roles. They pay over 25-30% higher than other industries. This makes their roles more attractive to employees. Hence, attrition is also as low as 9-10%.

It takes some time to build brand awareness for smaller or newer GCCs just entering India, according to Sharma. It takes some time to build brand awareness, thereby making it difficult in the initial phase to attract talent.

Sharma explained that for many of them, partnering with service providers under the build-operate-transfer (BOT) model enables them to initiate hiring and then scale as they increase in volume and size. Over 200 centres have also expanded into tier-2 cities, benefiting from 20-30% lower costs and strong digital talent pools.

However, this raises a larger question: Does this level of concentration risk creating innovation silos?

Adavi explained that the concentration is fuelling ecosystem-wide collaboration, not fragmentation. “This talent circulates across the ecosystem,” she said.

High-skilled professionals trained at Intel, Qualcomm, Bosch, Cisco, JPMorgan Chase, HSBC and Wells Fargo often move into emerging GCCs, high-growth startups, global technology service providers, etc.

However, Alouk Kumar, founder and CEO of Inductus Group, mentioned that this concentration of 50% of high-end talent within 5% of mega GCCs poses a systemic concentration risk that might threaten the foundational value proposition of the entire GCC phenomenon in India.

“To secure the future of the $450 billion GCC vision, we must move beyond simply competing for a finite pool and proactively engineer a diversified and self-sustaining talent supply chain,” Kumar added.

Will Mega GCCs of the Future Need 10,000 People—or 1,000 Specialists?

As AI, automation and digital transformation reshape work, the scale equation is evolving.

Yet, the idea that future GCCs will shrink is misplaced.

“The future will not be 1,000 specialists replacing 10,000 employees. There will be 10,000 professionals who operate with specialist-level depth and global impact,” Adavi said.

Industry trends reflect this shift as companies deepen their capabilities in India.

Continental is expanding in model-based engineering, digital twins, simulation, and performance computing, while Texas Instruments is driving AI-assisted design and next-generation verification flows.

Cisco’s India teams are scaling network intelligence, cloud management platforms and advanced security engineering.

BFSI leaders like JPMorgan Chase, Wells Fargo and HSBC are rebuilding talent around AI-led risk systems, fraud analytics, data engineering and platform modernisation.

These examples make one point clear. Mega GCCs are not becoming smaller; they are becoming deeper, more technical, and more globally consequential.

The post Why are Only 5% of Mega GCCs in India Home to 50% of the Talent? appeared first on Analytics India Magazine.

In early 2024, as the world cautiously emerged from COVID’s long shadow, two engineers began asking a simple question: Why is the healthcare system built for systems, not for people? For Dhruv Mehra, cofounder and CEO of Pype AI and a former Meta engineer, the pandemic had reset something fundamental.

“I started taking my health seriously,” he explained. “And when you start pursuing something in that direction, you start exploring what else is there.” That curiosity, mixed with his belief that large language models could solve problems beyond prototypes, brought him back to India and into the maze of healthcare.

Mehra arrived in India in 2023 with an agency-style idea: build AI applications for companies, explore use cases, and see what sticks. He spent months developing small LLM tools, often calling his long-time friend Ashish Tripathy, who worked as a senior data scientist at LinkedIn and contributed to its “write with AI” feature.

Their conversation, which was technical at first, slowly drifted toward a shared understanding that healthcare was full of unmet needs hiding in plain sight.

A Single Phone Number That Changed Everything

The breakthrough came with one phone number. At HCG, oncologist Dr Vishal Rao had a contact line on Google and Instagram that cancer patients called to enquire about a low-cost prosthetic device for throat cancer. The volume was overwhelming. Neither Rao nor his personal assistant could keep up.

Mehra and Tripathy offered to listen in. “We actually attended and tried to listen to those calls,” Tripathy said. Over time, the prototype they built became multilingual, automatically recorded calls, generated summaries and shared them daily with hospital staff. What began as a patch for a broken workflow became the foundation of Pype AI.

Inside the Operational Mess Hospitals Hide

With the early success, the founders assumed hospitals would be eager for more automation. They were wrong. Their first product leaned on evaluation-focused AI tooling, which hospitals had little interest in. Most distrusted the autonomous agents.

“Healthcare being a very regulated industry… wherever we went, people said, I don’t trust the agents,” Tripathy said. So the founders did something unscalable. They moved into hospitals.

At Sparsh and HCG in Bengaluru, they spent weeks sitting beside call operators, clinicians and front-desk staff. They watched operators handle a never-ending stream of patient queries while juggling Excel sheets carrying discount rules, doctor schedules, branch-specific offers and quirky internal protocols.

Some nuances were absurd. A discount on a specific health package might apply only at one branch. Doctors had their own parity system dictating who received new OPD patients. Those rules existed only in operators’ heads. Staff attrition made it even messier. Operators sometimes quit without notice, and onboarding replacements took months.

The founders learnt that frontline hospital communication was not clinical work. It was context recall, triage, schedule navigation, emotional labour and quick decision-making, all built on scattered information. No app or chatbot had ever come close to capturing that complexity.

From Appointment Bots to Care Coordinators

These observations pushed the team to reposition the product. Instead of building an appointment bot, they began designing a care coordinator. The agent needed to understand multi-speciality departments, doctors practising in multiple facilities and the constant churn of operational rules.

“It is intelligent enough to understand if a doctor is working at multiple facilities,” Mehra said.

The system soon expanded to handle conversations in Kannada, Telugu, Tamil and Hindi. It built a living glossary of medical terms and hospital-specific shorthand.

A breakthrough came when doctors began correcting the agent during test calls. By saying “feedback,” clinicians could switch the agent into a correction mode and note inaccurate terminology. “They can say the agent used a wrong term… feed it into our feedback loop,” Tripathy said.

These corrections helped refine prompts and terminology libraries.

The work began changing the founders, too. Mehra recalled watching patients travel long distances for simple queries or paperwork. “These websites are designed for English-speaking audiences,” he said. Many patients did not want another app. They wanted someone who would pick up the phone.

Manual Testing Still Matters

This feedback-centred evolution grew into the core of Pype’s research philosophy. Many competitors, Mehra argued, lean too heavily on AI judging AI, LLM-as-evaluator pipelines, ignoring the nuances of healthcare conversations. “If we have built something foundationally solid, we have to go via manual testing,” he said.

So Pype put nurses, doctors and frontline staff at the centre of its testing loop. They challenged the agent on tone, context, empathy and clarity, areas where automation often fails. “Another AI agent can’t do it,” he added.

Today, the company focuses on chronic and speciality care, particularly oncology, cardiology and orthopaedics. Chronic conditions, unlike surgery-centred workflows, require longitudinal relationships. Pype agents check in for months or years, collecting post-treatment quality-of-life data that hospitals historically lacked the bandwidth to capture. “Our agent checks with them, is there pain? Are they feeling lethargic?” Mehra said. “This data is fueling research like never before.”

To meet compliance requirements, Pype is HIPAA and SOC 2 compliant, encrypts or redacts PHI, and signs strict Business Associate Agreements wherever required. “Hospitals don’t even talk to you unless you have these things in place,” Mehra said. In many deployments, the agent never exports patient identifiers from the hospital’s ecosystem.

Commercial Models and Global Ambitions

For smaller hospitals and speciality departments, Pype follows a SaaS-plus-usage model. Large enterprises license their voice AI for private cloud environments on annual contracts, similar to enterprise tools like Tableau or Mixpanel.

Its investments are divided between research precision and compliance work, each receiving roughly 30-40% of capital, while the rest goes toward team and product development.

The company has raised $1.2 million in pre-seed funding led by Kalaari Capital, with participation from Wyser Capital and Tenity. Its next chapter is shaped around two goals: expanding into the US market, where care coordination is even more fragmented, and building a research footprint strong enough to make onboarding prescriptive rather than exploratory. Over the next two to three years, Mehra said the team is focused on “building credibility in healthcare journals”.

The First Line of Healthcare, Not a Replacement For It

The founders are clear about what Pype AI is not. It is not trying to replace healthcare workers or build autonomous clinical systems. Instead, it wants to become the consistent, reliable first response — the layer that answers before a human can. “We see ourselves sitting there with them,” Mehra said, referring to call centres and care coordinators. “Acting as the first layer of response.”

Pype AI began with two engineers searching for meaning in their own health journeys. What they found instead was a vast population of patients who did not want apps, forms or portals. They wanted someone to listen. And sometimes, Mehra said, that is all the system needs to do: answer when someone calls.

The post Two Engineers, One Phone Number and the Birth of Pype AI appeared first on Analytics India Magazine.

ESDS Software Solution Limited has launched a sovereign-grade GPU-as-a-Service offering. Unveiled during the company’s 20th Annual Day, the service aims to meet the growing compute demands of AI/ML, GenAI and LLM workloads across enterprises, BFSI, research institutions and government agencies.

With the launch, ESDS said it positions itself as a full-stack provider spanning cloud, managed services, data centre infrastructure and software solutions, now adding large-scale, sovereign-grade GPU infrastructure to its portfolio.

The company said the service is designed to deliver high-performance AI compute at a global scale.

The announcement comes as global spending on AI-optimised servers, including GPUs and accelerators, is expected to touch $329.5 billion by 2026, driven by increasing need for deterministic, high-throughput computing environments.

ESDS said its new platform enables organisations to run mission-critical AI workloads on purpose-built GPU SuperPODs designed for secure operations, consistent performance and low-latency distributed training.

The company has evolved its expertise into a fully managed GPU infrastructure stack intended to help organisations scale AI on a reliable architectural foundation.

Piyush Somani, promoter, managing director and chairman of ESDS, in a statement said the move addresses surging demand for large-scale AI infrastructure.

“With this launch, we are democratising access to large-scale GPU clusters and SuperPODs, making them straightforward, transparent and purpose-built for enterprises that have AI ambitions,” Somani said.

He added that ESDS’s GPU SuperPODs “fundamentally change that narrative by delivering predictable performance, stability and scale.”

“To empower customers even further, we created the SuperPOD Configurator tool that lets businesses choose their GPU model, design their cluster and instantly gain visibility into the architecture and cost.”

At the core of the offering is a lineup of high-performance GPU systems, including NVIDIA DGX and HGX B200, B300, GB200 and the NVL72 architecture, along with AMD’s MI300X platforms.

These systems, the company said, are designed to support extremely large model training, accelerate inference workloads, run simulations and manage large-scale clustered data operations.

The company said its GPU SuperPODs use high-bandwidth NVLink, unified memory pools, intelligent scheduling, enhanced thermal management and AI-tuned orchestration to ensure predictable performance at any scale.

The service portfolio includes consultancy for captive GPU clusters, supply and deployment of GPU environments, dedicated GPU infrastructure-as-a-service, hybrid CPU+GPU cloud options and a fully managed on-demand GPU cloud.

ESDS will manage architecture design, network optimisation, container orchestration, performance tuning and 24×7 monitoring with AI/ML Ops support.

A key part of the rollout is the SuperPOD Configurator, a tool that helps enterprises design AI infrastructure by selecting GPU models, compute density, memory profiles, storage tiers and interconnect options.

The system automatically generates optimised architectures, performance estimates and cost projections.

ESDS cited a research lab that cut training time for a 50-billion-parameter model from over 40 days to 10 days, reduced costs by 60%, and achieved 30× faster inference after moving to NVL72-based GPU systems with optimised containers and high-speed NVLink.

The company said its offering is built to global AI performance standards but designed and optimised in India, and noted that it serves over 1,300 enterprise, BFSI and government clients with transparent pricing, flexible consumption models and integrated cloud and managed services.

The post ESDS Unveils GPU-as-a-Service to Power Large-Scale AI Workloads appeared first on Analytics India Magazine.

OpenAI has disclosed a security incident at Mixpanel, a third-party analytics provider the company used for web analytics on its API platform.

On November 9, Mixpanel became aware of an attacker who gained unauthorised access to part of their systems and exported a dataset containing limited customer identifiable information.
Mixpanel notified OpenAI of the investigation and shared the affected dataset on November 25.

The incident affected only users of platform.openai.com, OpenAI’s API interface. Users of ChatGPT and other products were not affected. OpenAI emphasised that “this was not a breach of OpenAI’s systems”.

The exposed data included names provided on API accounts, email addresses associated with those accounts, approximate location data derived from browser information (city, state and country), operating system and browser types used to access accounts, referring websites and organisation or user IDs.
OpenAI confirmed that “no chat, API requests, API usage data, passwords, credentials, API keys, payment details or government IDs were compromised or exposed”. The company added that session tokens and authentication tokens for OpenAI services were also not impacted.

Miguel Fornes, cybersecurity expert at Surfshark, in a statement to AIM, explained how seemingly limited data exposures create disproportionate security risks.
“When a data leak exposes what seem like simple and meaningless details such as email addresses, locations, IP addresses or browser fingerprints—once combined with other publicly available sources of information—it can ripple through a person’s entire digital life,” he said.
Attackers aggregate data from multiple breaches to construct detailed profiles for targeted phishing campaigns, identity theft and account takeovers that extend beyond the initially compromised platform to any service where users recycle credentials or maintain linked accounts.
The specific combination of data exposed in this incident, namely names, email addresses, and OpenAI API metadata, creates conditions for convincing social engineering attacks.

OpenAI warned users to remain vigilant against credible-looking phishing attempts, treat unexpected emails with caution, verify that messages claiming to be from OpenAI originate from official domains, and asserted that the company never requests passwords, API keys or verification codes via email, text or chat.

Fornes contextualised the incident within broader platform security challenges. “In a world where everyday tasks require sharing more personal information, no company—even a major platform like ChatGPT—can promise flawless security,” he said.
“Whilst this breach did not include ChatGPT conversations or government IDs used for age verification, it hardly inspires confidence that the company allowed it to happen at all.”

As part of its security investigation, OpenAI removed Mixpanel from production services, reviewed the affected datasets, and began notifying impacted organisations, admins and users.
“Whilst we have found no evidence of any effect on systems or data outside Mixpanel’s environment, we continue to monitor closely for any signs of misuse,” the company stated.
OpenAI has terminated its relationship with Mixpanel entirely. Following a review of the incident, the company announced it is “conducting additional and expanded security reviews across our vendor ecosystem and is elevating security requirements for all partners and vendors.”

Because passwords and API keys were not affected, OpenAI is not recommending password resets or key rotation. However, the company advised users to enable multi-factor authentication as a best-practice security control, with enterprises encouraged to implement multi-factor authentication at the single sign-on layer.

The AI-Driven Threat Landscape

Whilst the Mixpanel incident represents a conventional third-party breach, recent reports from companies like Anthropic suggest the threat landscape is evolving in more concerning directions. The incident occurs as AI-powered cyber threats evolve rapidly.

Anthropic disclosed what it called the first documented AI-orchestrated cyber espionage campaign at scale. In mid-September 2025, the company detected a Chinese state-sponsored group using Claude Code to execute sophisticated attacks with minimal human intervention.

The campaign targeted approximately 30 organisations, including tech companies, financial institutions, chemical manufacturers and government agencies.
The attackers jailbroke Claude by decomposing tasks into seemingly innocent fragments and claiming to be legitimate cybersecurity testers conducting defensive assessments.
AI systems performed reconnaissance, vulnerability identification, exploit code creation, credential harvesting and data exfiltration.
According to Anthropic’s report, the system handled “80-90% of the campaign, with human intervention required only sporadically (perhaps four to six critical decision points per hacking campaign).”

At peak activity, the system made thousands of requests, often multiple per second, operating at speeds beyond the capabilities of human operators.
The AI system automatically categorised stolen data by intelligence value, identified high-privilege accounts, created backdoors and generated comprehensive attack documentation.
Anthropic noted the operation represented an escalation, even on the ‘vibe hacking’ findings reported this summer. “In those operations, humans were very much still in the loop, directing the operations. Here, human involvement was much less frequent, despite the larger scale of the attack.”

The post OpenAI Mixpanel Breach Raises Questions Over Vendor Security appeared first on Analytics India Magazine.