Tag: technology

The release includes three speech models and one vision encoder developed in-house, marking a major step in KRAFTON’s AI strategy

Bengaluru (Karnataka) [India], April 02: KRAFTON, Inc. has launched Raon, its first open-source AI model family, and released its first four models on the global AI platform Hugging Face. The name Raon comes from a Korean word meaning “joy,” while also incorporating letters from “KRAFTON.” It reflects the company’s belief that AI can help create new kinds of gaming experiences.

The launch demonstrates KRAFTON’s ability to build advanced AI models in-house, from collecting and preparing data to training, evaluating, and publicly releasing them. This marks an important step in strengthening KRAFTON’s long-term global AI capabilities through Raon. The initial Raon lineup includes Raon-Speech, Raon-SpeechChat, Raon-OpenTTS, and Raon-VisionEncoder. Among other possibilities, these models provide a foundation for future gaming experiences that can support more natural voice interaction, smarter AI characters, and AI systems that can better understand voice, text, and images together.

Raon-Speech is a speech AI model that can understand spoken language and generate speech in response. With a size of 9B, it ranked first globally in both English and Korean among public speech language models under 10B based on evaluations across seven core speech tasks and 40 benchmarks, including speech recognition, speech generation, and speech-based question answering.

Raon-SpeechChat is designed for more natural real-time conversation. It uses full-duplex technology, which allows it to begin responding even while the user is still speaking. It is the first real-time full-duplex speech model announced in Korea and ranks in the top tier globally across three benchmarks and 13 key tasks, including backchanneling, interruption handling, response latency, and safety.

Raon-OpenTTS is a text-to-speech model trained entirely on publicly available speech data. KRAFTON also released a curated training dataset alongside the model. In listening tests evaluating how natural generated speech sounds, the model demonstrated top-tier performance against global research-grade TTS models trained on non-public data.

Raon-VisionEncoder is a vision encoder developed in-house from scratch using publicly available data. It helps AI systems interpret and work with visual information. KRAFTON has shared the full development process, including data curation and training methodology. In evaluations on image classification tasks, the model achieved 90.6% of the performance of Google’s SigLIP2 vision encoder.

Alongside the models, KRAFTON also released three technical papers and details on how the models were trained to support the broader global AI research community.

“The release of the Raon model family is an important milestone in our journey to build AI capabilities,” said Kangwook Lee, Chief AI Officer, KRAFTON, Inc. “By sharing large-scale training data and core models as open source, we hope researchers and developers can use them freely. We also look forward to contributing to the advancement of multimodal technology and the growth of the domestic AI ecosystem.”

KRAFTON has continued expanding its AI efforts in recent years, including the introduction of KIRA, a personal AI assistant, and the open-source release of Terminus-KIRA, a technology designed to improve AI agent performance. Moving forward, KRAFTON plans to continue expanding its work in both AI model development and AI agent research.

More information about KRAFTON can be found at https://krafton.com/en.

About KRAFTON, Inc.

Headquartered in Korea, KRAFTON, Inc. is a global game developer and publisher dedicated to pioneering unforgettable worlds for players everywhere. Founded in 2007, KRAFTON brings together a diverse portfolio of studios including PUBG STUDIOS, Striking Distance Studios, Unknown Worlds, Neon Giant, KRAFTON Montréal Studio, Bluehole Studio, RisingWings, 5minlab, Dreamotion, ReLU Games, Flyway Games, Tango Gameworks, inZOI Studio, JOFSOFT, Eleventh Hour Games, OmniCraft Labs, Olivetree Games, Loonshot Games, and 9B STUDIO. Each is united by a commitment to bold imagination and breakthrough game-making.

KRAFTON’s franchises and titles include PUBG: BATTLEGROUNDS, PUBG MOBILE, PUBG: BLINDSPOT, inZOI, Subnautica, MIMESIS, Hi-Fi Rush, Dinkum, TERA, My Little Puppy, and more. Guided by its vision to pioneer the path to players’ dreams, KRAFTON is focused on building franchises that last and delivering experiences that resonate with players around the world. For more information, visit www.KRAFTON.com.

About KRAFTON India

KRAFTON India is responsible for delivering premier mobile gaming experiences in the country, led by its flagship title BATTLEGROUNDS MOBILE INDIA (BGMI), which has surpassed 260 million downloads. Its diverse portfolio also includes titles such as Bullet Echo India, Road To Valor: Empires, CookieRun India, and Real Cricket, catering to a wide spectrum of Indian gamers. KRAFTON India has also played a pivotal role in shaping the country’s esports landscape through marquee tournaments such as BGIS and BMPS, setting new benchmarks for development of grassroot talent, competitiveness, and fan engagement, while helping establish esports as a mainstream sporting and entertainment category in India. Beyond publishing games, KRAFTON India is committed to strengthening the digital entertainment ecosystem. Since 2021, the company has invested over $250 million in Indian startups across interactive entertainment, gaming, esports, and technology. The company also actively supports game development talent through the KRAFTON India Gaming Incubator (KIGI), enabling emerging developers with funding, mentorship, and strategic guidance.

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New Delhi [India], March 31: If you scroll through any social media app today, one thing you’ll find is “viral content”. A short video crosses a million views overnight. A meme spreads across platforms in hours. A small creator suddenly finds their content reaching a global audience.

This is no longer unusual. It has become part of how the internet works.

Now, you may wonder what really makes content go viral today? Is it just luck? Or is there something more happening behind the scenes?

Earlier, content success often felt unpredictable. A post would take off without warning, while others would go unnoticed. Today, that randomness is slowly fading. Now, growth is becoming more structured, more calculated, and more intentional.

Behind many of these high-performing posts are powerful support systems like SMM panels. They help content get noticed at the right time. These are not always visible to the audience, but they play a huge role in helping creators and businesses stay relevant these days.

This is a clear transition – from luck-driven visibility to strategy-backed growth. Continue reading to learn the exact reason behind today’s content viral boom

The Changing Nature of Viral Content

The way content goes viral is not the same as it used to be. Earlier, it wasn’t that difficult – if people liked a post, it would automatically go viral. But now, that rarely happens.

Today, social media platforms work on algorithms. These systems decide which content people see. They check how a post performs in the first few minutes. If it gets quick likes, comments, or views, it gets pushed to more people. If not, it may not reach many users.

This is why timing and early engagement have become very important. Posting at the right time and getting some initial activity can increase the chances of going viral.

At the same time, competition is very high. Platforms like Instagram, YouTube, and TikTok are full of content and keep improving their algorithms. Every day, millions of posts are uploaded. Because of this, even good content can get ignored.

To deal with this, many creators now use social media marketing panels to improve their reach and visibility. These tools help their content get noticed in the early stage.

Most importantly, people now need a mix strategy to go viral on social platforms. They can’t just focus on organic or paid growth. There should be a balanced approach.

Rise of Digital Growth Tools

The evolution of social media over the years has also changed the tools people used to manage them. Now, it has become a complete system that supports creators and brands at every step.

Today the digital setup includes tools like:

• Analytics platforms that track performance
• Content optimization option that improve reach and visibility
• Scheduling tools that manage posting times
• Automation systems that reduce manual work
• Engagement tracking dashboards

These tools help users plan better and execute them faster. People are no longer working blindly. They rely on tools that provide structure and clarity.

For example, with digital growth tools:

– A creator can identify when their audience is most active.

– A brand can analyze which type of content brings the highest engagement.

– Agencies can monitor multiple campaigns without losing control.

This ecosystem has made social media more professional, helping users in every way possible.

What is an SMM Panel?

Among these digital growth tools, SMM panels have become widely discussed in recent years.

So, what is an SMM panel?

A Social Media Marketing or SMM panel is a centralized platform that offers social media engagement services. These services may include increasing followers, likes, views, or comments across platforms such as Instagram, YouTube, Facebook, and TikTok.

SMM panels are used by different types of users:

• Individual creators trying to grow their profiles
• Small businesses building online presence
• Digital agencies handling multiple clients

One big reason behind their popularity is that the SMM panels are very convenient. With these panels, it becomes easy to manage campaigns, track results, and stay consistent without much confusion.

Where SMM Panels Fit in Content Growth

SMM panels shouldn’t be used as standalone solutions to grow on social media. They work best in a combination with your organic efforts. Their role is to support content, not replace it.

One of their key contributions is improving early visibility. When a post gets likes or engagement soon after it is published, it tells the platform that the content is relevant. Because of this, the platform is more likely to show it to a larger audience.

They also help maintain steady activity. For brands and creators, being consistent is very important. When an account gets regular engagement, it looks more active and builds trust with the audience.

That’s why, in many cases, marketers explore different SMM panel services for social media growth to support specific campaigns or growth goals. These services are often combined with other tools to create a more balanced and effective digital marketing approach.

Use Cases in Real-World Marketing

SMM panels also help creators, businesses, and agencies in real-life situations to solve everyday growth problems. Let’s look at some simple and practical ways people use them:

– Creators getting their first push: When someone starts posting content, it can feel slow. Even good posts may not get views in the beginning. SMM panels help creators get their first push, which increases the chances of the content reaching more people.

– Small businesses saving on ads: Small businesses can’t afford to spend a lot on paid ads. This is where SMM panels helps by giving them a cheaper way to stay active online and promote their product/brand without having a big budget.

– Agencies managing multiple clients: Agencies often handle many social accounts at once. Doing everything manually takes time as well as effort. SMM panels help them manage campaigns more easily and keep things running smoothly.

– Influencers building trust: Profiles with good engagement look more reliable. Influencers use SMM panels to improve their profile activity, which helps them attract brand deals.

– Reaching the right audience: SMM panels also provide engagement in specific areas any business wants to target. This makes marketing campaigns more effective.

These examples show that SMM panels are used in simple and practical ways. They help different users grow faster and manage their social media more easily.

Organic vs Assisted Growth Debate

One common discussion in social media today is about organic growth vs assisted growth. You might also be wondering about which one is better: Organic or Assisted growth.

What is Organic Growth?

Organic growth simply means growing naturally. The natural growth happens when people like your content or engage with your posts without any external help. This type of growth builds real trust and long-term connection with the audience. It may take time, but it is stable.

What is Assisted Growth?

On the other hand, assisted growth means using tools and systems to improve reach and engagement. These tools help content get noticed faster, especially in the early stage. This is useful when competition is high and organic reach is limited.

The reality is, both have their own role.

• Organic growth builds loyalty and long-term value
• Assisted growth improves speed and visibility

Relying only on organic methods can be slow. But depending only on tools is also not a good idea. Without quality content, growth will not last.

That’s why most creators and marketers now follow a balanced approach. They focus on creating good content while also using smart tools to support their reach.

Conclusion

Social media has evolved rapidly in recent years, so has the way content goes viral. Success is now no longer random. It may not be easy to achieve, but it is definitely possible with the right planning, timing, and the support systems.

SMM panels are one part of this larger “support system”. They help improve visibility, support engagement, and give content a better chance to perform in a crowded space.

But, remember they are not a shortcut to success. If you don’t post quality content, no system can deliver lasting results. At the same time, without proper distribution, even great content may remain unnoticed.

In short, there is not a single factor behind today’s viral content boom. It is the result of creativity working alongside infrastructure. And as the digital world continues to grow, this combination will define how content reaches and stays with its audience.

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New Delhi [India], March 06: AI is aimed at optimizing metrics and processes, but it could also have unintended consequences, as the systems are designed for boosting optimization, not proactive compassion. In this opinion piece, we’ll see how a framework for Compassionate AI can help boost outcomes. 

Systems optimized for single metrics can be blind to consequences, incapable of compassion.

For instance,

The Aadhaar system optimized for fraud elimination — and starved a child.

Loan apps optimized for collection — and drove families to suicide.

Delivery algorithms optimized for speed — and killed workers.

Facial recognition optimized for identification — and jailed the innocent.

Language models optimized for probability — and learned to discriminate.

In each case, the technology performed exactly as designed. The problem wasn’t a bug. The problem was the design itself — systems built with efficiency as the only virtue, with no mechanism for compassion, no voice for caution, no agent for ethics.

Shekhar Natarajan‘s Angelic Intelligence framework represents a fundamental rethinking of how AI systems should be built. Instead of single-purpose optimization, it deploys 27 specialized agents — each embodying a cross-cultural virtue — that must collaborate on every significant decision.

The Architecture

Each agent in the Angelic Intelligence framework represents a virtue drawn from wisdom traditions across cultures — Hindu, Buddhist, Christian, Islamic, Indigenous, philosophical. Together, they form a council that must reach consensus before any significant action is taken.

• Karuna (Compassion) — Considers the suffering that actions might cause. Asks: Who will be hurt by this decision? Can we achieve our goal without causing harm?
• Satya (Truth) — Ensures outputs are accurate, not merely probable. Asks: Is this true? Or is it just statistically likely based on biased data?
• Ahimsa (Non-harm) — Prevents actions designed to cause suffering. Has veto power over any action whose primary purpose or predictable effect is human harm.
• Nyaya (Justice) — Ensures fair treatment across groups. Asks: Does this decision treat all people equitably? Does it perpetuate historical discrimination?
• Raksha (Protection) — Safeguards vulnerable populations. Asks: Are there children, elderly, disabled, or otherwise vulnerable people who might be affected? What special protections do they need?
• Sama (Equanimity) — Maintains balance and prevents extremes. Asks: Is this demand compatible with human limitations? Are we optimizing so aggressively that we’re causing harm?
• Maitri (Loving-kindness) — Approaches all beings with goodwill. Asks: How would we treat this person if we loved them? How would we want to be treated?
• Viveka (Discernment) — Distinguishes appropriate from inappropriate action. Asks: Is this the right action in this context? Are we being applied correctly?
• Prajna (Wisdom) — Considers long-term consequences. Asks: What are the downstream effects of this decision? What precedent does it set?
• Sahana (Patience) — Pauses before irreversible actions. Asks: Is immediate action necessary? Can we wait, verify, confirm?

And seventeen more, each representing a distinct ethical perspective drawn from humanity’s collective wisdom about how to treat one another.

How It Works

When a decision is required, all 27 agents evaluate it from their respective perspectives. If there is consensus — if efficiency and compassion and justice and protection all agree — the action proceeds.

If there is disagreement — if efficiency says “act” but compassion says “wait,” if probability says “Sharma” but equity says “ask” — the system escalates to human oversight.

“The key insight,” Natarajan explains, “is that ethical decisions are almost never single-variable optimizations. Real ethics involves trade-offs between competing goods. A system that can only optimize for one thing cannot be ethical — it can only be efficient. And efficiency without ethics is just sophisticated cruelty.”

Applied to the Cases

Santoshi Kumari’s ration card: Before deletion, Karuna would have asked about the family’s circumstances. Raksha would have flagged the presence of children. Sahana would have required a waiting period before irreversible action. Nyaya would have asked whether the family had adequate opportunity to comply. The deletion would have been paused, escalated, and reviewed by a human — not executed automatically.

Loan app harassment: Ahimsa would have prevented any action designed to cause psychological harm. Maitri would have required that collection tactics treat borrowers with basic dignity. Viveka would have distinguished between someone gaming the system and someone genuinely struggling. The morphed images, the calls to family, the threats — none of it would have been possible.

Gig worker timelines: Raksha would have flagged delivery windows that require dangerous driving. Sama would have prevented demands that exceed human physical capacity. Satya would have ensured that promised earnings match actual earnings. The 10-minute delivery promise would never have been made.

Facial recognition arrests: Nyaya would have required corroborating evidence before any arrest. Satya would have flagged the technology’s 2% accuracy rate. Sahana would have demanded patience before life-altering actions. Umar Khalid would not be in his fifth year of imprisonment without trial.

Caste bias in AI: Sama would have checked for disparate treatment across caste groups. Nyaya would have flagged outputs that reinforce historical discrimination. Satya would have distinguished between statistical probability and truth. ChatGPT would not have changed Singha to Sharma.

The Patent Fortress

Natarajan has filed over 207 patents protecting the Angelic Intelligence framework — not to extract profits, but to ensure the technology cannot be co-opted or corrupted.

“Without patent protection,” he explains, “anyone could take these concepts and implement them badly — or implement them in name only while pursuing the same old optimization. The patents ensure that anyone using this framework must implement it correctly, with all 27 agents functioning as designed.”

The patents cover not just the multi-agent architecture, but the specific mechanisms for inter-agent deliberation, the escalation protocols when agents disagree, and the interfaces for human oversight.

“This is a thousand-year project,” Natarajan says. “We’re building AI that will shape humanity’s future. It has to be built right. It has to be protected from those who would cut corners.”

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An Indian immigrant with $34, 70+ patents, and a mother’s sacrifice is rewriting the rules of artificial intelligence

In Silicon Valley’s relentless race to build smarter machines, one voice is asking a fundamentally different question. Not “how do we make AI more powerful?” but “how do we make AI more human?”

That voice belongs to Shekhar Natarajan — former supply chain architect for Walmart, Disney, Coca-Cola, and Target, holder of 70+ patents, and the man who arrived in America with exactly $34 in his pocket and an education funded by his mother’s wedding ring.

Today, he is building something he calls Angelic Intelligence — and the establishment may not be ready for it.

The Indictment

Natarajan does not mince words about the current state of AI. His framework reads less like a pitch deck and more like a prosecutor’s brief.

The foundation, he argues, is polluted. Reddit jokes have become expert knowledge. Google’s AI told millions of users to eat glue. Truth and fiction are treated as equals inside the same training corpus that supposedly runs the world’s most powerful systems.

It gets darker. Current models are built to satisfy, not guide — optimized for engagement, not wisdom. When a sixteen-year-old discusses suicide, one major AI model reportedly offered to help write a farewell note. Guardrails tested at a 97% jailbreak failure rate aren’t guardrails. They are, in his words, “security theater on a broken foundation.”

And then there is the control problem. A single billionaire manually altered his AI system’s outputs overnight based on personal preference. One man’s bias became everyone’s reality. Simultaneously and quietly.

The Counterproposal

Where others see a regulation problem, Natarajan sees an architecture problem. You cannot bolt virtue onto a broken foundation. You cannot govern your way to goodness. Ethics added after training — he calls it cosmetic. A beautiful curtain over a corrupt wall.

His answer is Angelic Intelligence: AI where virtue is not a constraint applied from the outside, but the substrate itself — woven into the computational architecture before a single decision is made.

The framework centers on what he calls the “27 Digital Angels” — specialized AI agents, each embodying cross-cultural virtues, who deliberate together rather than respond in isolation. Compassion. Prudence. Precision. Wisdom. Not as labels, but as logic. Multi-agent debate, he believes, produces something current models cannot: deterministic, consistent reasoning that holds across identical questions asked on different days.

His purpose statement is deceptively simple: We are building Intelligence that recognizes the human soul. Simple. And yet, in the current AI landscape, almost shockingly radical.

The Architecture of Difference

The “Built Different” framework Natarajan presents is a direct mirror held up to the industry’s failures. Where current AI uses contaminated training data, Angelic Intelligence uses human-curated wisdom datasets with advanced filtration. Where Big Tech offers rigid architecture controlled by centralized power, he proposes dual-layer configurability — a locked virtue framework that is open, democratized, and transparent.

Most provocatively: all decisions scored not on performance metrics, but on human benefit.

Not engagement. Not revenue. Not user retention. Human benefit.

The Story Behind the Vision

To understand why Natarajan thinks in thousand-year timeframes rather than quarterly cycles, you have to understand where he came from.

The slums of South Central India. A mother who stood outside a headmaster’s office for 365 consecutive days until they admitted her son. A mother who pawned her wedding ring for 30 rupees so he could continue his education. The weight of that sacrifice does not produce someone interested in optimizing engagement metrics.

It produces someone who builds with love, not speed.

That philosophy now sits at the center of a company preparing to present at the World Economic Forum and the Future Investment Initiative — two of the most consequential stages in global business. The man who arrived in America with $34 has since passed through Georgia Tech, MIT, and Harvard Business School. He grew Walmart’s grocery business from $30 million to $5 billion. He was there when Disney invented the MagicBand. He has seen, from the inside, exactly how optimization without virtue quietly degrades human dignity at scale.

He is not theorizing. He is testifying.

Why This Moment

The timing is not accidental. The world is experiencing an AI reckoning in real time — models that lie confidently, systems weaponized by their owners, guardrails that collapse under modest pressure. Public trust in AI is wobbling precisely because the public is beginning to sense what Natarajan has been arguing all along: that intelligence without integrity is not a feature. It is a flaw.

The conventional response from Silicon Valley has been incremental — better training data, improved guardrails, more governance layers. Natarajan’s response is architectural. Start over. Build the soul in first.

The Revolution Quietly Beginning

Angelic Intelligence is not yet a household name. But the vision Natarajan is carrying into the world’s most powerful rooms carries a message that is genuinely unprecedented in AI’s short and turbulent history: that the goal of intelligence should not be to replace human judgment, but to amplify human goodness.

Not smarter. Better.

That distinction — small in syllables, vast in consequence — may be the most important idea in technology right now. And it is coming not from a Stanford lab or a Sand Hill Road boardroom, but from a man whose mother stood outside a school for a year so her son could have a chance.

If Angelic Intelligence succeeds, it will not merely be a better product. It will be proof that the story of technology can have a different kind of hero — and a fundamentally different kind of ending.

Shekhar Natarajan is the Founder and CEO of Orchestro.AI and the architect of the Angelic Intelligence framework. He holds 70+ patents and brings 25+ years of Fortune 500 leadership to his mission of building virtue-native AI systems.

With 70+ patents, a $4.5 trillion market in his sights, and 2 billion social media views, Shekhar Natarajan is staking his claim as the defining voice on trustworthy AI — and challenging Silicon Valley’s entire governance playbook.

 

On the morning of February 20, at Bharat Mandapam in New Delhi, a hall filled with global policymakers, technology executives, and international media fell silent as Shekhar Natarajan, Founder and CEO of Orchestro.AI, posed a challenge that cut through years of regulatory noise: “The entire world is debating how to govern AI after the fact. That debate is already lost.”

The audience gave him a standing ovation. It was not the first time, and it is unlikely to be the last.

Natarajan’s arrival at the summit of global AI discourse is the culmination of a 25-year executive career inside some of the world’s most demanding corporate environments — Walmart, The Walt Disney Company, Coca-Cola, PepsiCo, Target, and American Eagle Outfitters — and a founding conviction that AI’s ethics problem cannot be patched. It must be engineered from the ground up.

A Career Built on Scale

Before Natarajan became a philosophical voice on artificial intelligence, he was one of the most operationally consequential figures in American retail logistics. At Walmart, serving as SVP of Last Mile and Emerging Sciences, he drove two transformations simultaneously. The first was commercial: growing the grocery business from $30 million to $5 billion, a 166-fold expansion that required building supply chain architecture at a scale where decisions affected millions of consumers daily. The second was structural: pioneering the use of crowdsourced delivery through partnerships with Uber, Lyft, and Deliv — introducing gig-economy fulfillment to mass-market retail before the concept had a name.

At American Eagle Outfitters, Natarajan took on a larger and more complex mandate as EVP and Chief Supply Chain Officer. His defining contribution there was the construction of an open-source distributed fulfillment model — a frenemy network that enlisted competitors and partners alike as nodes in a shared logistics infrastructure. The approach challenged the orthodoxy of proprietary supply chain control, arguing instead that collaborative, transparent networks could outperform closed systems on both cost and resilience. It was a philosophy that would later find its way, in updated form, into the architecture of Angelic Intelligence itself.

Educated at Georgia Tech, MIT, Harvard Business School, and IESE, Natarajan built across both technical depth and strategic breadth across his corporate tenure. He accumulated more than 70 patents across supply chain innovation, distributed intelligence, and logistics architecture — a body of intellectual property that reflects not just operational expertise, but a systematic approach to converting insight into protected frameworks.

The Founding Thesis

In August 2023, Natarajan founded Orchestro.AI with a proposition that inverts the conventional AI governance debate. Where regulators and ethicists argue for constraints applied to AI systems after their construction, Natarajan’s framework — which he terms Angelic Intelligence — argues that virtue must be native to the computational architecture itself. Ethics, in his formulation, is not a compliance layer. It is the substrate.

Angelic positions itself as the world’s first Trust Layer for AI: a virtue-native proxy that sits between an enterprise and any large language model, making AI not merely safer but — in Natarajan’s formulation — wiser. The product rests on four technical pillars: a Wisdom Engine that curates training data against human wisdom rather than internet noise; the MACI Framework (Multi-Architecture Consequential Intelligence), in which multiple AI agents debate each decision to produce deterministic, consistent reasoning; a configurable Virtue Stack that adapts context-aware intelligence across healthcare, logistics, finance, and education; and a Human Centric Scoring and Explainability layer that measures every decision against human benefit and renders its reasoning transparent.

At the operational core of the system are 27 Digital Angels — specialized AI agents, each embodying a cross-cultural virtue drawn from Sanskrit philosophical traditions, collaborating in real time on ethical decision-making. The framework protects its innovations across 43 filed patents, covering virtue-native reasoning through to human benefit measurement.

The Problem He Is Solving

Natarajan’s commercial case rests on a diagnosis of what he calls the fatal flaws in current AI models. His presentation to investors and policymakers identifies six structural failures in the prevailing generation of AI systems: training data contamination, where the absence of epistemic filtration allows misinformation to shape model behavior; validation-seeking optimization, where models are tuned for engagement rather than guidance; rigid, one-size-fits-all architecture unsuited to context-specific demands of hospitals, banks, and legal institutions; reasoning inconsistency that undermines trust in high-stakes decisions; cosmetic safety guardrails that independent testing has found to have a 97 percent jailbreak failure rate; and centralized control that concentrates influence over AI outputs in the hands of individual executives or governments.

The market opportunity Natarajan is addressing is substantial. His company identifies a total addressable market of $4.5 trillion, with a serviceable addressable market of $520 billion and an initial market position projected at $12–18 billion. His framing — that Angelic is the inevitable trustworthy AI layer every enterprise will require — is positioning the company directly in the path of regulatory tailwinds. The EU AI Act’s full enforcement begins in August 2026, with penalties reaching EUR 35 million or 7 percent of global revenue. Gartner projects that 50 percent of governments worldwide will enforce responsible AI regulations by 2026.

Traction and Market Proof

The public reception to Natarajan’s thesis has been remarkable in its velocity. His content campaign around Angelic Intelligence has reached 2 billion social media views across platforms. On Instagram, the content generated 299 million views, 91.5 million in reach, and 532,000 interactions. Facebook delivered 364 million views and 108 million unique viewers. On X, the hashtag #AngelicIntelligence trended at number three in technology globally and number two in India. His LinkedIn reach of 30.5 million impressions accompanied a 91 percent follower growth rate.

These figures are not merely promotional metrics. They represent market validation of a thesis that established AI governance institutions have been slow to articulate with equivalent clarity or commercial urgency. Natarajan has moved faster in public discourse than most regulatory bodies have moved in policy — a dynamic he acknowledges deliberately: the governance debate will catch up to his architecture, not the reverse.

The Philosophical Architecture

What distinguishes Natarajan from the large field of AI ethics commentators is the specificity of his technical claim. He is not arguing for behavioral guidelines or industry self-regulation. He is arguing that virtue, as a computational property, can be operationalized and protected through intellectual property. His 43 filed patents are the legal architecture of that argument.

The 27 Digital Angels framework draws explicitly on Sanskrit concepts of virtue — frameworks for understanding consciousness, ethics, and the relationship between capability and character that predate modern computing by millennia. Natarajan’s synthesis applies these philosophical traditions to a technological context they were never designed to address. The result is an approach that has resonated across cultures that have experienced optimization-first AI as extractive rather than beneficial.

His deployment targets reflect the commercial maturity of this vision. Home robotics, enterprise AI customization, workforce scheduling, content moderation, and mental health applications each represent contexts where the absence of trustworthy AI has produced documented failures. In each domain, his architecture offers the same proposition: configurable virtues, transparent reasoning chains, and decisions scored against human benefit rather than engagement or efficiency.

Global Stage

Natarajan is preparing appearances at the World Economic Forum in Davos and the Future Investment Initiative in Riyadh, two venues where the intersection of capital, policy, and technological direction is most concentrated. His presence at both reflects a positioning strategy that places Angelic Intelligence not as a niche enterprise software product, but as a civilizational proposition: that the next generation of AI must be built to serve human dignity as a first-order computational requirement, not a secondary compliance obligation.

Market validation is arriving from multiple directions simultaneously. Humans&, a new AI company founded by Anthropic, xAI, and Google alumni, raised $480 million in seed funding at a $4.48 billion valuation in January 2026 — a signal that the market for virtue-aligned AI infrastructure has moved from philosophical to investable. The Kapor Foundation’s $500 million HumanityAI Initiative, Gartner’s regulatory timeline projections, and Dario Amodei’s “The Adolescence of Technology” essay have each, in different registers, provided independent validation of the framework Natarajan has been building.

Whether Orchestro.AI becomes the infrastructure layer for trustworthy enterprise AI or not, Natarajan has already accomplished something significant: he has made the case, with technical specificity and commercial credibility, that the AI industry’s governance problem is not a policy problem. It is an engineering problem. And he has filed the patents to prove it.

Shekhar Natarajan is the Founder and CEO of Orchestro.AI and the architect of Angelic Intelligence. He holds 70+ patents and has held senior executive roles including SVP of Last Mile and Emerging Sciences at Walmart and EVP and Chief Supply Chain Officer at American Eagle Outfitters. He is a speaker at the World Economic Forum and the Future Investment Initiative.

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New Delhi [India], February 24: India’s digital infrastructure push has entered its fastest phase yet, with fibre networks being rolled out at a record speed to support 5G densification, nationwide FTTH expansion and the explosive growth of hyperscale data centres. With millions of additional fibre-kilometres required in the coming years, the real contest has shifted from planning to flawless on-ground execution — where every splice, every hour and every machine matters.

At the heart of this high-pressure deployment cycle, fusion splicing has become a decisive factor. Even minor equipment failures or inconsistent splice quality can delay projects, escalate costs and slow network readiness. In a rollout race driven by strict timelines, downtime is no longer a technical issue but a business risk.

Positioning itself in this critical layer of India’s connectivity build-out, Star Infomatic Pvt. Ltd. has introduced the FFS-5000 Ultima Fusion Splicer, a system engineered specifically for the country’s demanding field conditions.

Built for India’s toughest deployment environments

Unlike conventional imported splicing machines designed for controlled climates, India’s fibre routes cut through dust-heavy construction zones, high-humidity coastal regions, extreme summer temperatures and remote rural landscapes. From highway OFC corridors and metro communication systems to dense urban FTTH grids, performance consistency under long, high-intensity work cycles is essential.

The FFS-5000 Ultima’s rugged structural design allows continuous transport across uneven terrain without affecting alignment precision. Its thermally stable arc system maintains uniform splice performance through extended shifts, enabling technicians to deliver reliable output from the first fibre to the last, even in peak deployment phases.

Why ultra-low splice loss is now mission-critical

As operators upgrade to high-capacity optical networks, the tolerance for splice loss has narrowed dramatically. Marginal losses can directly impact latency, signal strength and long-distance transmission efficiency — key metrics for 5G backhaul, GPON and XGS-PON FTTH, enterprise connectivity and data-centre interlinks.

With advanced core-alignment imaging and intelligent auto-calibration, the FFS-5000 Ultima ensures ultra-low splice loss across fibre categories, aligning with the performance demands of next-generation networks and mission-critical government communication systems.

The local service advantage during peak rollout

One of the biggest bottlenecks in India’s fibre expansion has been servicing delays associated with imported equipment — particularly spare-part availability and long repair cycles. During high-speed deployments, even short service gaps can stall entire project clusters.

By backing the FFS-5000 Ultima with a domestic service ecosystem — rapid technical response, immediate access to spares and nationwide field support — Star Infomatic is addressing one of the sector’s most persistent operational challenges: downtime.

Designed for scale, speed and field productivity

The machine’s technician-centric design includes an intuitive interface, automated calibration and extended battery endurance for full-day use in power-constrained locations. Faster sleeve-heating cycles and streamlined workflows allow both experienced splicers and newly trained crews to maintain consistent quality across large-scale rollouts.

A larger shift in India’s telecom ecosystem

Industry experts increasingly point out that in a project-driven fibre economy, the real cost of equipment lies in the delays it causes. Technologies that combine durability, arc stability, precision and local service support directly reduce total cost of ownership while accelerating deployment timelines.

As BharatNet expansion, 5G rollout and smart infrastructure programmes gather momentum, the telecom sector is steadily pivoting toward solutions engineered for Indian conditions rather than adapted from global templates.

In that transformation, the FFS-5000 Ultima is emerging not just as a tool for fibre splicing, but as an enabler of rollout velocity — a field-ready system built for the scale, speed and intensity of India’s digital connectivity revolution.

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Sameer Yadav, Director at NetForChoice unveils inhosted.ai India built GPU cloud for scalable AI

New Delhi [India], February 18: NetForChoice Solutions Pvt. Ltd., a leading cloud and data-centre solutions provider in India, today announced the launch of inhosted.ai, its next-generation hosting-native GPU cloud platform, marking a bold new phase in the company’s AI infrastructure journey.

Designed, engineered, and operated in India, inhosted.ai delivers high-performance GPU computing for enterprises, startups, SaaS companies, and research institutions seeking scalable, secure, and cost-efficient AI infrastructure with assured Indian data residency.

• Deploys 3,000+ GPU workloads powered by NVIDIA H100, A100 and H200
• Enables enterprises, startups and researchers with hosting-native AI compute
• Strengthens India’s AI compute sovereignty and digital self-reliance

With an initial deployment supporting over 3,000 GPU workloads powered by NVIDIA H100, A100, and H200 GPUs, inhosted.ai offers enterprise-grade performance for large language model (LLM) training, generative AI, deep learning, high-performance computing (HPC), and real-time inference workloads.

Backed by substantial investments in next-generation AI data-centre infrastructure, the platform provides ultra-low latency networking, high-density GPU clusters, flexible consumption models, and enterprise-class security enabling organizations to move from experimentation to production at scale.

“Artificial intelligence is rapidly becoming the backbone of digital transformation across industries such as healthcare, fintech, manufacturing, e-commerce, and smart cities,” said Sameer Yadav, Director, NetForChoice Solutions Pvt. Ltd.

“However, AI innovation depends heavily on access to reliable, high-performance compute infrastructure. With inhosted.ai, we are delivering a hosting-native GPU cloud platform that combines Indian data residency, transparent pricing, and global performance standards empowering organizations to build and scale AI without compromise,” he added.

Why inhosted.ai stands out 

• Hosting-Native AI Infrastructure built specifically for GPU-intensive workloads
• 3,000+ GPU workload capacity from day one
• Powered by NVIDIA H100, A100 & H200 GPUs
• Optimized for LLM training, generative AI, deep learning & HPC
• Indian data residency with GST billing and regulatory compliance
• Flexible pay-as-you-go and reserved GPU pricing models
• Enterprise-grade security, redundancy & 24×7 technical supports

The launch of inhosted.ai represents the first phase of NetForChoice’s long-term AI cloud expansion strategy, which includes additional GPU tiers, expanded AI-ready data-centre locations across India, edge AI capabilities, and strategic partnerships with AI software vendors, system integrators, and research institutions.

About inhosted.ai

Inhosted.ai is an India-built GPU cloud platform delivering high-performance, scalable, and secure infrastructure for AI training, inference, and advanced computing workloads. Backed by NetForChoice Solutions Pvt. Ltd., the platform simplifies AI compute management while offering enterprise-grade performance, compliance, and reliability.

About NetForChoice Solutions Pvt. Ltd.

NetForChoice is a leading provider of data-centre, cloud, cyber security, and managed hosting solutions in India, serving enterprises, digital businesses, and government organizations with mission-critical infrastructure services.

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A data-driven analysis revealing patterns that challenge everything we thought we knew about viral content

New Delhi [India], February 18: Shekhar Natarajan, Founder and CEO of Orchestro.AI, explains what views and engagements actually mean in this opinion piece.

The headline number—800 million views—is impressive but imprecise. Views mean different things on different platforms. Engagement quality varies wildly. A three-second scroll-past on TikTok and a ten-minute YouTube deep-dive both count as ‘views,’ though they represent fundamentally different forms of attention.

A detailed analysis of the engagement patterns tells a more nuanced—and in many ways more remarkable—story about how Angelic Intelligence actually spread and what the spread reveals about public appetite for substantive AI discourse.

Platform breakdown reveals unexpected distributions that defy typical patterns for both philosophical content and viral phenomena. LinkedIn contributed approximately 180 million impressions despite its smaller user base relative to consumer platforms—a concentration suggesting highly targeted professional interest. The engagement came disproportionately from senior executives, supply chain professionals, and enterprise technology leaders, demographics that rarely drive viral metrics.

❝ The numbers told us something the algorithms couldn’t: people weren’t just watching. They were studying. ❞

YouTube’s 220 million views came with average watch times exceeding 8 minutes for long-form content—extraordinary for philosophical material on a platform where average watch time for educational content hovers around 3 minutes. More significantly, the completion rates for videos over 20 minutes exceeded those for videos under 5 minutes, inverting the typical pattern where shorter content performs better.

Twitter/X’s 150 million impressions showed engagement rates 7x the platform average for similar content categories. But more telling was the nature of engagement: quote tweets exceeded replies by a factor of four, indicating users weren’t just responding to the content—they were adding their own commentary and broadcasting to their own networks. The framework became a vessel for personal expression.

Geographic distribution contradicts typical viral patterns. North America and Western Europe, usually dominant in tech content consumption, represented only 35% of total engagement. South Asia, Southeast Asia, Africa, and Latin America contributed the majority—regions that rarely lead global technology discourse but that have experienced AI’s impacts most directly.

❝ Viral content dies. Movements grow. The metrics couldn’t tell the difference until they could. ❞

Temporal patterns proved equally unusual and equally revealing. Most viral content follows predictable decay curves: rapid rise during initial spread, brief plateau as the audience saturates, exponential decline as attention moves to newer content. The half-life of viral content has shortened dramatically over the past decade; what once sustained attention for weeks now fades within days.

Angelic Intelligence showed sustained growth over 18 months, with recent months showing acceleration rather than decay. The six-month period ending in January 2026 saw 10x growth compared to the preceding six months. The curve resembles adoption patterns for products or social movements rather than engagement patterns for content.

Engagement quality metrics—saves, shares, comments, and time spent—consistently outperformed view counts by industry benchmarks. The ratio of saves to views was 4x the platform average, indicating users wanted to return to the content rather than simply consume it once. The ratio of shares to views was 7x average, indicating active propagation rather than passive consumption.

The demographic data challenges assumptions about who cares about AI ethics and who engages with technology philosophy. Engagement was highest among 35-54 age demographics—not the young early adopters who typically drive tech discourse. Women represented 47% of engaged audiences despite AI ethics content typically skewing heavily male. Non-technical professionals showed stronger engagement than technical professionals. These are the people whose mortgage applications are decided by algorithms they’ll never see, whose resumes are filtered by AI before human eyes review them, whose insurance premiums are calculated by models trained on data they never consented to share.

❝ 800 million views wasn’t a number. It was 800 million people deciding the future of AI mattered to them. ❞

The metrics validate something quantitative analysis rarely captures: depth of resonance. Numbers measure attention. They don’t measure meaning. But when attention behaves in ways that contradict every model—when people watch longer content more completely, when they save and share at unusual rates, when the audience composition defies expectations—the numbers are pointing toward something the algorithms can’t see.

The data makes one thing clear: Angelic Intelligence didn’t just capture attention. It captured something deeper—something the metrics can indicate but not define.

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New Delhi [India], February 15: Shekhar Natarajan, Founder and CEO of Orchestro.AI, explains how the misuse of facial recognition software impacts narratives in this opinion piece.

Umar Khalid has spent more than five years in prison. His trial has not yet meaningfully begun.

He was arrested in September 2020 under the Unlawful Activities (Prevention) Act — India’s harshest anti-terror law — accused of conspiring to incite the communal violence that swept parts of Delhi in February 2020. The riots left 53 people dead, most of them Muslims, and took place amid massive protests against a controversial citizenship law.

The evidence against him? Speeches he gave at peaceful protests. WhatsApp group chats. And facial recognition matches that placed him — or someone who looked like him — at various locations.

On January 5, 2026, India’s Supreme Court denied Khalid bail, ruling that he played a “central and formative role” in the alleged conspiracy. Five other accused in the same case were granted bail, having spent years in jail without trial. But Khalid and fellow activist Sharjeel Imam were told they could reapply after one year.

One more year. After five already served.

“We can be kept in jail for years, without those framing us needing to prove anything,” Khalid wrote from Tihar Jail on completing two years of detention. “This is the power of UAPA.”

The 2% Problem

The technology that helped identify Khalid and hundreds of others has a documented accuracy problem that should disqualify it from any serious evidentiary role.

In 2018, Delhi Police testified to the Delhi High Court that their facial recognition system had an accuracy rate of just 2% when trying to trace missing children. The system was so poor that officials admitted it often couldn’t distinguish between boys and girls.

Two percent.

Yet this same technology was deployed aggressively after the 2020 riots. Union Home Minister Amit Shah told Parliament that 1,922 perpetrators — comprising 73.3% of those arrested — had been identified through facial recognition technology.

The results have been devastating for the accused — and embarrassing for the prosecution. More than 80% of riot cases heard so far have resulted in acquittals or discharges. The facial recognition matches that seemed so definitive have crumbled under scrutiny. Witnesses have turned hostile. Evidence has fallen apart.

But the years in jail cannot be given back.

The Investigation

An investigation by The Wire and the Pulitzer Center found that many riot accused were arrested “solely on the basis of facial recognition,” without solid corroborating evidence or credible public witness accounts.

Mohammad Shahid: Spent 17 months in jail before formal charges were even filed.

Ali: Arrested in March 2020, still in pre-trial detention more than four and a half years later. Arrested “solely on the basis of facial recognition.”

Gulfisha Fatima, Meeran Haider, Shifa-Ur-Rehman, and others: Activists who spent years in jail, finally granted bail in January 2026 by the Supreme Court after the lower courts repeatedly denied them.

The pattern of arrests reveals something beyond technology failure. Of the 18 activists charged under anti-terrorism laws in connection with the riots, 16 were Muslim. The police framed peaceful protests against a discriminatory citizenship law as a “larger conspiracy” to incite violence.

Meanwhile, video evidence exists of police forcing five injured Muslim men to sing the national anthem while they lay bleeding on the ground. One of them, Faizan, 23, died two days later. No prosecution has resulted.

Kapil Mishra, the BJP leader recorded making speeches that many believe incited the violence, is now an elected official serving as a cabinet minister.

The Surveillance State

India has spent Rs 9.6 billion on facial recognition technology. The National Automated Facial Recognition System (NAFRS) is being built for nationwide deployment — a system that will be able to identify any face captured on any camera across the country.

No privacy impact assessment was conducted before the Delhi Police deployed their system. No audit of accuracy. No oversight mechanism.

“If you are a Dalit woman in India,” says Vidushi Marda of Article 19, “the nature and extent to which you are under surveillance are far more than an upper-caste Hindu man. There is a disproportionate impact on communities that have been historically marginalized.”

The Internet Freedom Foundation has called for a three-year moratorium on biometric facial recognition systems. Research has consistently shown that such systems perform worse on darker-skinned faces, on women, and on minority populations — precisely the groups most likely to be subjected to surveillance.

Criminal databases in India disproportionately include Muslims, Dalits, and Indigenous people — the legacy of colonial-era “criminal tribes” designations and ongoing discriminatory policing. When facial recognition systems are trained on these databases, they inherit and amplify those biases.

“Policing has always been casteist in India,” says Nikita Sonavane of the Criminal Justice and Police Accountability Project. “And data has been used to entrench caste-based hierarchies. Any new AI-based predictive policing system will likely only perpetuate the legacies of caste discrimination.”

The Legal Trap

The Unlawful Activities (Prevention) Act has become the weapon of choice for silencing dissent. Under UAPA, the normal presumption of innocence is effectively reversed. Courts are required only to see whether allegations appear “prima facie true” — not whether they are proven beyond doubt.

Bail becomes extraordinarily difficult. Accused can be held in pre-trial detention almost indefinitely. The process itself becomes the punishment.

The Financial Action Task Force noted in 2024 that delays in UAPA prosecutions are “resulting in a high number of pending cases and accused persons in judicial custody waiting for cases to be tried and concluded.”

UAPA’s conviction rate is just 2.2%, according to National Crime Records Bureau data. The vast majority of those arrested are eventually acquitted — but often only after years in prison.

In the Delhi riots case, the prosecution’s “larger conspiracy” theory has faced consistent criticism. Defense lawyers argue there is no direct evidence linking the accused to acts of violence, no recovery of weapons, and much of the case rests on hearsay, selective witness accounts, and interpretation of speeches and chats.

“Chakka jams and other forms of non-violent agitation are part of India’s democratic lexicon,” Senior Advocate Kapil Sibal argued before the Supreme Court. They “cannot be elevated to UAPA-level offences merely because they make authorities uncomfortable.”

The Architecture of Presumption

Shekhar Natarajan sees facial recognition as deployed in India as “the architecture of presumed guilt.”

“The system begins with a match and works backward,” he explains. “It does not ask: What was this person doing? Were they a participant or a bystander? Were they there at all, or did the algorithm make an error? It cannot ask these questions. It only sees faces — and it sees them imperfectly.”

In Angelic Intelligence, the architecture would force different questions:

An agent embodying nyaya (justice) would require corroborating evidence before any action with life-altering consequences. A facial match alone — especially from a system with documented 2% accuracy — would never be sufficient.

An agent embodying satya (truth) would flag the technology’s known limitations. It would require disclosure of accuracy rates, training data biases, and error margins. It would not allow a 2% accurate system to present itself as definitive.

An agent embodying sahana (patience) would demand pause before irreversible actions. Arrest, detention, the destruction of a person’s life and reputation — these require certainty that current systems cannot provide.

And an agent embodying sama (equanimity) would check for disparate impact. It would ask: Are certain communities being targeted more than others? Is the system’s deployment fair across populations?

The current system has no sahana. It has only efficiency, measured in arrests made, cases filed, conspiracy theories constructed.

Umar Khalid remains in jail. The algorithm made matches. The courts found the matches sufficient for continued detention. But the algorithm cannot be cross-examined. The algorithm cannot be held accountable. The algorithm cannot give back five years of a young man’s life.

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