TÜV SÜD, Accenture and NVIDIA Unite to Fast‑Track AI Robot Safety Certification in Factories and Warehouses

TÜV SÜD and Accenture have unveiled their new “Physical AI Safety Suite” at NVIDIA GTC Berlin 2026, a framework designed to accelerate certification of autonomous robots and AI-driven systems in factories and warehouses. The suite uses digital twins, AI agents, and simulations to reduce certification timelines by up to 30%, helping manufacturers deploy automation faster and more safely.

Established industry standards form the foundation for ensuring that automated facilities are both safely designed and reliably operated. A key example is ISO 3691‑4, which outlines safety requirements for driverless industrial trucks and systems, including Autonomous Mobile Robots (AMRs). In many industrialized nations, certification against such standards is mandatory before companies can commission automated facilities or deploy new physical AI systems. Even in regions where certification is voluntary, adherence to recognized industry standards remains best practice.

However, navigating this complex landscape of overlapping standards often slows the adoption of physical AI‑enabled automation. At a mid‑sized production plant, the iterative process of coordinating, validating, and gaining approval for safety concepts can extend over several weeks, delaying deployment timelines.

At NVIDIA GTC Berlin, TÜV SÜD introduced the Physical AI Safety Suite, a system that uses digital twins and AI simulations to replace parts of the manual safety checks for factories and equipment. This digital approach can bring production online up to 30% faster. By reducing the need for physical site visits and installed equipment, companies can spot safety and certification issues earlier, avoiding expensive fixes later in operation.

Key Highlights

• Launch Event: Presented at NVIDIA GTC Berlin 2026
• Collaborators: TÜV SÜD and Accenture
• Core Technology: Digital twins, AI agents, and simulation testing
• Target Use Cases: Autonomous Mobile Robots (AMRs), driverless industrial trucks, warehouse automation
• Productivity Impact: 30% faster commissioning of automated facilities
• Standards Basis: Built on ISO 3691-4 safety standards

How the “Physical AI Safety Suite” Works

Using digital twins, AI agents and simulations, TÜV SÜD and Accenture’s Physical AI Safety Suite is designed to speed up the automation of factories and warehouses. (Image - Accenture.com)

• Digital Certification: Automates manual inspection and certification package creation
• Safety Scenarios: Simulates risk scenarios beyond physical testing
• Early Compliance: Validates safety concepts earlier in design cycle
• Continuous Monitoring: Supports ongoing compliance during operations

Benefits for Manufacturers

Benefit	Details
Faster Certification	Cuts commissioning timelines by ~30%
Improved Safety	Simulates scenarios beyond real-world testing limits
Cost Efficiency	Reduces rework and delays in automation rollout
Scalability	Supports deployment across diverse environments

Risks & Challenges

• Complex Standards Navigation: Certification requires compliance with multiple overlapping standards
• Integration Costs: Simulation infrastructure may be expensive for smaller manufacturers
• Human-Robot Interaction Risks: Real-world unpredictability remains a challenge
• Regulatory Variability: Certification requirements differ across countries

Context for India & Global Manufacturing

For manufacturers in India and Asia: TÜV SÜD and Accenture’s framework could help reduce bottlenecks in certifying AMRs and AI-driven logistics systems. This is particularly relevant for semiconductor fabs, automotive plants, and large-scale warehouses in Gurugram and Bengaluru, where AI-enabled automation is expanding rapidly.

Inner Sky Labs Unveils Kavach, Raises ₹300 Cr to Scale Physical AI

Ten years in the making, Inner Sky Labs exits stealth and unveils a full stack of foundation models for physical machines: how they sense the world, how they decide what to do — and Kavach, a safety layer that has the final word before anything moves.

Inner Sky Labs today unveiled a complete stack of foundation models that lets machines sense, decide and act safely around people. Every great leap in human productivity has come from a new kind of machine working alongside people: the loom, the steam engine, the industrial robot. Most of the world's economy, by some estimates as much as 80% of global GDP, is created in the physical world, where language models alone fall short. For a construction project in Delhi, a warehouse in Bhiwandi, a farm in Punjab or a car manufacturer in Stuttgart, physical AI means a smarter machine that follows their lead, intelligently takes the strain out of the job and lets their skill go further. A machine that works this close to people must be one they can trust, because a machine's mistake could hurt someone.

The stack gives a machine three things a human has by instinct: the ability to sense what's happening around it, the ability to decide what to do about it, and — critically — an independent safety check to stop any unsafe move.

Alongside the launch, the startup is closing a ₹300 Cr (about $30.9 Mn) funding round involving existing backers YourNest Venture Capital and Keshav R Murugesh, as well as new global investors.

Teaching machines to see, hear and feel

Before a machine can act safely, it has to understand what's actually happening around it — not just see it, but make sense of it the way a person does. Inner Sky Labs' perception models give a machine that understanding: the Drishti series of vision models reads a scene the way a pair of eyes would, the Shruti series of audio models makes sense of sound — a spoken instruction, the crash of something falling — and the Sparsh models give a machine a working sense of touch, so a robotic hand can tell the difference between gripping an egg and gripping a wrench. Put together, a machine stops being a sensor bolted to a motor and starts being something that actually perceives the room it's in.

Teaching machines to decide, and to think ahead

Understanding a room is only half the job — a machine also has to decide what to do next, and good judgment means thinking a step ahead. Inner Sky Labs' action models — Kriya, Prana and Karma — supply generative action intelligence to plan and carry out physical movement — reaching, gripping, walking, flying — and also to predict what happens next: if this arm moves this way, does the box slip, does the person standing nearby step forward. It's the same kind of anticipation a person uses to catch a glass before it tips, built into the machine's decision-making instead of left to reflex.

Kavach: the part of the brain whose only job is to say no

Sensing and deciding well is still not the same as being safe — a machine can understand a room perfectly and still make the wrong call. That's what Kavach — Sanskrit for "shield" — is for: a system that checks every single action a machine is about to take, and has the power to stop it, no matter what the rest of the machine has decided.

Picture a robotic arm about to swing across a factory floor. Before Kavach lets that motion proceed, it checks: is that space clear of people right now, is the planned speed safe for that area, does this action stay inside the zone it's allowed to work in. If the answer to any of those is no, the arm does not move. Not "slows down." Not "flags it for review afterward." The action is blocked before it starts.

A decade of trust, now scaling up

Inner Sky Labs already knows what it means to be trusted with something fragile. It is the company behind Miko, the companion robot that has spent the past ten years engaging and educating children to improve their social-emotional skills and is now in more than 140 countries. An early version of the same safety thinking that has spent a decade deciding what Miko may say to a child is now the foundation deciding what any machine — an industrial arm, a delivery robot, a drone — is allowed to do. In an independent evaluation by BDO’s cyber security practice, Miko's safety system caught every unsafe prompt across seven public AI-safety benchmarks, ahead of four widely used commercial AI models, and ranked first at flagging unsafe AI responses. Full methodology at www.innerskylabs.ai. A decade spent earning a parent's trust with a robot for their child is now the foundation for earning a factory's, a hospital's, and a government's trust with machines that move.

Why this matters beyond one company

As machines move into hospitals, factories, streets and homes, whoever writes the rules a machine lives by decides what that machine is allowed to do near a citizen — and today, most of those rules are written somewhere else, in a language, a legal system and a set of values that aren't their own. Inner Sky Labs' system lets any end customer or authority write its own rules — its own definition of what is safe, in its own legal language — and have a machine obey them, without depending on another company's judgment call. Built and proven in Mumbai out of more than ten years of research by IIT Bombay alumni, this is a technology that lets any country decide for itself what its own machines are allowed to do.

The full stack already runs today inside factories, classrooms and consumer products in millions of homes.

"Every robot company will eventually have to answer one question: why should anyone trust this machine near a person," said Sneh Vaswani, co-founder and chief executive of Inner Sky Labs. "For ten years, our answer was Miko — a robot trusted enough to be left with a child. Today we're answering that same question for every kind of machine, with technology built, tested and proven out of India."

“I’ve advised Sneh and the team since 2016, when they first shared this multi-decade vision with me—long before ‘Physical AI’ was even a category,” said Keshav R. Murugesh, early investor, advisor to Inner Sky Labs, and former Chairperson of NASSCOM. “Today, Physical AI is being called a $50 trillion opportunity. I am proud to see this built in India for the world. Miko was the proof point. This is the scale-up.”

Availability

The platform is available today to qualified partners by invitation, with cloud, on-premise and on-device deployment, including fully offline, air-gapped environments for operators who cannot depend on an outside network. Each model — perception, action and Kavach's safety layer — is available on its own. The company has raised over $80M to drive frontier research from investors including Chiratae Ventures, IvyCap Ventures, YourNest Venture Capital and 360 ONE.

About Inner Sky Labs

Inner Sky Labs is a Mumbai-headquartered company, with offices in the US and the UK, that created the consumer brand Miko, the children's companion robot sold globally. Founded out of IIT Bombay, Inner Sky Labs has spent over a decade building the technology that lets machines sense, decide and act safely around people, and is now bringing that same thinking to industrial robots, drones and machines everywhere.

More at innerskylabs.ai.

NTT DATA and Hyster-Yale Materials Handling Announce Breakthrough Physical AI Solution in Manufacturing

Representative Image 

• Physical AI embeds intelligence into quality assurance for critical assembly operations
• When compared to legacy techniques, physical AI cuts deployment timelines from months to weeks
• Co-innovation improves product reliability and maintains quality standards at scale

NTT DATA, a global leader in AI, digital business and technology services, and Hyster-Yale Materials Handling, Inc. (HYMH), the manufacturer of Hyster® and Yale® lift trucks, today announced a breakthrough application of physical AI that embeds intelligence directly into manufacturing processes. This approach leverages sensor data to enable machines and systems to perceive, understand and act in real-time within real-world operations.

Bringing this capability into practice introduces AI-driven quality assurance directly into HYMH’s manufacturing operations. This co-developed approach represents a first-of-its-kind use case of how physical AI can be applied in an industrial assembly environment by embedding intelligence into production workflows, helping to safeguard that products are built to consistently high standards.

NTT DATA designed and developed the solution at HYMH’s manufacturing facility in Berea, KY, integrating vision sensors, edge AI that processes data on-site and advanced analytics into a critical assembly workflow.

Together with partner Archetype AI, NTT DATA in collaboration with HYMH, adapted a physical AI model that analyzes assembly activity against expected production steps, validating that all parts are installed and assembly stages are completed, flagging deviations before the product moves to the next stage. By validating quality throughout the assembly process, the solution helps identify and address potential issues before products leave the factory floor.

This initiative demonstrates a step-change in how AI can be applied in manufacturing environments. Combined with edge computing, the solution can run locally so all processing happens on-site, enabling faster rollout and quicker time-to-value. Early results showed that physical AI cuts deployment timelines from months to weeks when compared with legacy techniques, accelerating adoption and iteration across manufacturing operations.

“Our confidence in physical AI continues to grow, and we’re starting to see the countless benefits that AI can bring to our global manufacturing operations,” said Barbara Binda, Director of Global Manufacturing Innovation, Hyster-Yale Materials Handling. “Working with NTT DATA allows us to leverage how physical AI can help our production teams maintain high-quality standards and deliver the most reliable products to our clients.”

"This deployment shows what physical AI looks like in real production environments, not as a concept, but with tangible impact on the factory floor,” said Shahid Ahmed, Global Head of Edge Services, NTT DATA, Inc. "By combining real production data with physical AI models at the edge, we’re helping leading manufacturers like HYMH deliver high quality products, support frontline workers and apply AI in ways that deliver real-world outcomes.”

As manufacturers accelerate automation, demand is rising for physical AI that can operate safely in complex environments, driving efficiency, quality and resilience. NTT DATA is uniquely positioned to deliver this capability at scale, combining industry expertise with end-to-end services to integrate AI across IT and operational technology environments, enabling intelligent, data-driven operations.

Today’s news builds on a longstanding collaborative relationship between NTT DATA and HYMH. Together, the companies are advancing more adaptive and intelligent manufacturing processes and exploring how physical AI can be scaled to drive repeatable, high-quality production outcomes.

About NTT DATA

NTT DATA is a $30+ billion business and technology services leader, serving 75% of the Fortune Global 100. We are committed to accelerating client success and positively impacting society through responsible innovation. We are one of the world’s leading AI and digital infrastructure providers, with unmatched capabilities in enterprise-scale AI, cloud, security, connectivity, data centers and application services. Our consulting and industry solutions help organizations and society move confidently and sustainably into the digital future. As a Global Top Employer, we have experts in more than 70 countries. We also offer clients access to a robust ecosystem of innovation centers as well as established and start-up partners. NTT DATA is part of NTT Group, which invests over $3 billion each year in R&D. Visit us at nttdata.com

About Hyster‑Yale Materials Handling

Hyster-Yale Materials Handling, Inc., designs, engineers, manufactures, sells and services a comprehensive line of lift trucks, parts and technology and energy solutions marketed globally primarily under the Hyster®, Yale®, Nuvera® and Maximal® brand names. The company’s subsidiary, Bolzoni S.p.A., is a leading worldwide producer of attachments, forks, masts and lift tables marketed under the Bolzoni®, Auramo® and Meyer® brand names.

Hyster-Yale Materials Handling is a wholly owned subsidiary of Hyster-Yale, Inc. (NYSE: HY).

Niqo Robotics: Profitable ‘Made in Bharat’ Physical AI for Global Agriculture

• Strong cumulative revenue traction and a path to profitability position Niqo among a new generation of globally competitive Indian deep-tech companies

Niqo Robotics, a Physical AI company building intelligent agricultural robots for global farms, today announced that its core AI enabled weeding business is on track to achieve profitability in its first full year of commercial operations. The company has also built strong cumulative revenue traction, positioning it among the few Indian agricultural robotics companies to demonstrate meaningful commercial scale in this sector. At a time when many global agri-robotics players continue to prioritise scale over sustainable economics, Niqo is charting a different path, one built on capital efficiency, farmer ROI, and commercially viable deployment from the outset.
A rare profitability milestone in agri-robotics/deeptech

As agricultural robotics companies worldwide work to translate innovation into commercially viable businesses, Niqo Robotics’ core AI enabled weeding business is now approaching self-sustainability. The company attributes this to growing commercial traction, repeat demand, and a model built around disciplined economics rather than scale at any cost.

Niqo’s farmer-first model is built around a one-time purchase, with no recurring subscription fees, backed by 24/7 service support and locally stocked spare parts. This allows growers to adopt advanced automation without the burden of hidden costs or ongoing platform charges.

“We built Niqo to prove that Physical AI can be a real business, not just impressive technology that burns cash indefinitely,” said Jaisimha Rao, Founder and CEO of Niqo Robotics. “Our path to profitability is not about cutting corners. It is the result of building a product that delivers real ROI to growers from day one. No subscriptions, no hidden costs, just a machine that pays for itself.”

Made in Bharat, Made for the World

Niqo’s growth story also reflects a larger shift in Indian deep-tech: from research-led prototypes to commercially viable products competing in global markets. While India has seen decades of experimentation in farm automation, Niqo is among the first companies to commercialise and scale AI-powered field robots that are both designed in India and deployed in demanding agricultural environments across international markets.

This strengthens the narrative of India building globally relevant deep-tech products in frontier sectors. Niqo’s systems are built in India and are now being deployed across farms in the United States and India, signalling that agricultural robotics developed in Bharat can solve for productivity, labour, and sustainability challenges well beyond the domestic market.
An intelligent farming platform, not just a single machine

At the core of Niqo’s next phase of growth is Niqo Sense, the company’s proprietary AI camera platform that can be integrated across multiple machine form factors and retrofitted onto existing farm equipment. This platform combines AI at the edge with a dual-tank, twin-nozzle architecture to perform weeding, thinning, and beneficial spraying in a single pass, reducing the need for multiple machines and repeated field operations.

The system processes thousands of plant-level decisions per second in real time, entirely on the edge and with zero cloud dependency. It delivers more than 99% accuracy even in dense and challenging field conditions. This Physical AI approach, where intelligent software is tightly integrated with purpose-built hardware for real-world use, positions it at the forefront of a category attracting increasing investor and customer attention globally.
A broader signal for Indian agri-innovation

Niqo’s progress marks more than a company milestone. It points to a structural shift in Indian agri-innovation, where the focus is moving from pilots and proof-of-concept demonstrations to scalable, revenue-generating climate-tech businesses.

By helping growers reduce input costs, improve precision, lower chemical usage, and maintain soil health without displacing farm labour, Niqo is positioning robotics not as a futuristic concept, but as a practical and profitable tool for agriculture. The company plans to build on this foundation by expanding into new crops and additional applications through its broader Physical AI platform.

About Niqo Robotics

Niqo Robotics is a Physical AI company building intelligent agricultural robots for sustainable farming worldwide. Founded by Jaisimha Rao, a Carnegie Mellon-trained engineer and former BlackRock professional, Niqo develops AI-powered weeding, thinning, and precision spraying systems that help growers reduce costs, cut chemical usage, and address labour challenges in farming.

Powered by Niqo Sense, its proprietary AI camera platform, Niqo’s products are commercially deployed across the United States and India. The company is headquartered in Bengaluru, with U.S. operations in Dover, Delaware, and dealer networks across California and Arizona. Niqo is a Series B company backed by investments from Fulcrum Global Capital, Omnivore, Bidra Innovation Ventures, Blume Ventures, BEENEXT, and FMC Corporation.

NVIDIA's AI Push is Reaching Aircraft, and an Indian eVTOL is Part of the Story

A central theme of Jensen Huang's COMPUTEX keynote this week was Physical AI — the convergence of AI, simulation, robotics and autonomous systems. While much of the AI boom has focused on software, NVIDIA's vision is increasingly centered on intelligent machines operating in the real world. From industrial robots and autonomous systems to digital twins and edge computing, the company is building the technology stack that will power the next generation of physical machines. Increasingly, that vision is extending beyond factories and robotics into sectors such as aerospace.

One of the companies living inside that vision is an Indian aerospace startup called The ePlane Company. Featured during Jensen Huang's keynote, ePlane represents an emerging class of companies using AI, simulation and digital-twin technologies not to build software, but to engineer systems that operate in the physical world.

With this recognition, ePlane becomes only the third eVTOL company in the world to be supported by NVIDIA, and the only one from Asia (for their certification journey).

What distinguishes this partnership from a typical technology endorsement is its depth. ePlane was the first eVTOL company globally to publicly commit to NVIDIA hardware within its onboard avionics architecture, and is now actively pursuing aircraft certification with that hardware integrated into the system. This is a foundational hardware-software co-development programme designed from the ground up to meet aviation-grade certification requirements.

"Being on Jensen's stage at GTC Taipei is not a moment we take lightly," said Prof. Satya Chakraavarthy, Founder and CEO, ePlane. "It is a signal to the world that India is not a follower in this technology cycle. We are co-developing safety-critical systems with the most consequential embedded infrastructure company on the planet, at the standards required for certified flight. That is a different conversation that speaks to the growing maturity and global relevance of India’s aerospace ecosystem.”

For ePlane, the Omniverse integration goes well beyond simulation as a tool. By building physics-accurate digital twin environments that model aircraft behaviour across a comprehensive range of flight conditions, failure modes, and edge cases, the company is able to generate simulation evidence that directly feeds into its DGCA certification pathway. The rigour matches what global aerospace leaders apply, and is now being built indigenously, in Chennai.

"Partnerships like this one do not happen by accident," said Vishnu Ramakrishnan, SVP Customer Strategy & Business Partnerships, ePlane. "They happen when a company's technology is genuinely credible at the global level. Being selected by NVIDIA as a reference for how their platform is applied in certified aviation tells our partners and investors something no pitch deck can: that the world's leading embedded infrastructure company has looked at what we are building and decided it belongs on their stage."

ePlane is currently in the Ground Test Vehicle phase of its first full-scale aircraft, with a Series C fundraise underway.

About ePlane

The ePlane Company (Ubifly Technologies Pvt. Ltd.) is an IIT Madras-incubated eVTOL company building electric air mobility solutions for India and global markets. The company is developing a compact, safety-certified electric aircraft for passenger, cargo, and emergency medical services applications. ePlane holds Design Organisation Approval from the DGCA, with its Type Certification application officially accepted. The company is backed by [key investors] and is currently in advanced stages of its Series C fundraise.

Humyn Labs Commits $20M to Build Human Data Infrastructure for Physical AI

Humyn Labs, an AI company developing human data infrastructure for physical AI firms worldwide, today announced a $20 million commitment to accelerate its objective of organising and validating human intelligence at scale.

The committed capital will be deployed across three key pillars to build the infrastructure powering next-generation Physical AI systems at Humyn Labs, co-founded by Manish Agarwal and Ishank Gupta.

At its core, Humyn Labs is scaling egocentric, source-first data collection, capturing first-person, real-world human activity through visuals and movements across commercial, agricultural, and residential environments in India, Southeast Asia, LATAM, and the Middle East. This includes how humans see, navigate, and physically interact with their surroundings, creating rich, context-aware datasets for training Physical AI systems.

Voice is emerging as a critical interface for Physical AI, powering real-world commands, inputs, and human-robot interactions. To support this, the company is expanding its voice data capabilities across 33 languages, dialects, accents, and code-switching patterns, ensuring that Physical AI systems can understand, interpret, and respond to human instructions with deep contextual and cultural accuracy.

Further strengthening this stack, Humyn Labs will launch its Robotics Labs to build high-fidelity simulation environments and world models seamlessly integrating real-world egocentric data with training frameworks to accelerate the deployment of intelligent, responsive, and globally adaptable Physical AI systems.

Manish Agarwal, Co-Founder, Humyn Labs, said, “Every real-world AI system will need continuous human data to train and validate, making this a foundational, always-on infrastructure layer. The demand is immediate and global. As Physical AI scales, we see Humyn Labs growing rapidly to serve this need at scale and becoming a key enabler of next-generation AI systems.”

Humyn Labs collaborates with frontier technology companies to convert signals from real-world communities into structured human data systems used to train and evaluate next-generation AI models. The diversity of physical environments, operational conditions, and edge cases that Humyn Labs draws from gives its validation infrastructure a distinct edge.

Ishank Gupta, Co-Founder, Humyn Labs, adds, “The world’s best AI models are only as good as the human intelligence behind them. Having operated across five continents, I can tell you - the Global South isn’t just where this data comes from, it’s where the future of AI is being built.”

The global AI training data market is projected to reach $25 billion by 2030, with Physical AI validation emerging as its most technically demanding and fastest-growing segment. Humyn Labs is building at that frontier as the infrastructure company that organises and validates the human data the industry can depend on.

What is Physical AI? Understanding the Age of Intelligent Machines

Artificial intelligence has traditionally been thought of as software—algorithms that recommend what to watch next, chatbots that answer questions, or systems that detect fraud. But a new wave of innovation is pushing AI beyond the digital realm into the physical world. This is Physical AI: intelligence embodied in machines that can move, sense, and act.

At its simplest, Physical AI is about giving AI a body. It integrates sensors, reasoning models, and actuators so that machines can perceive their surroundings, make decisions, and physically interact with objects and people. This marks a shift from digital intelligence to embodied intelligence.

What is Physical AI?

Physical AI refers to AI systems integrated with hardware that can sense, decide, and act in the real world. Unlike digital AI (chatbots, recommendation engines, etc.), physical AI has a “body” that allows it to manipulate objects, move through space, and adapt to changing environments.

Key components include:

• Sensors (cameras, LiDAR, radar, tactile sensors) for perception.
• AI models for reasoning and decision-making.
• Actuators/motors for physical action. 
• Feedback loops for continuous learning.  

Digital AI vs Physical AI

Aspect	Digital AI (Software)	Physical AI (Hardware + AI)
Environment	Virtual, data-driven	Real-world, sensor-driven
Interaction	Text, images, voice	Movement, manipulation, sensing
Examples	ChatGPT, Netflix AI	Robots, drones, autonomous cars
Challenges	Bias, hallucinations	Safety, cost, reliability

Real-World Examples of Physical AI

• Tesla Optimus Robot – Humanoid robots designed to perform repetitive tasks in manufacturing facilities, moving beyond traditional industrial arms.
• Amazon Warehouses – Over 750,000 robots assist in picking, sorting, and moving packages, working alongside human employees to handle massive demand spikes.
• Autonomous Vehicles – Self-driving cars use AI with cameras, LiDAR, and radar to navigate safely in traffic.
• Healthcare Robotics – Surgical robots and robotic exoskeletons help doctors perform precise operations and assist patients with mobility.
• Drones in Logistics – AI-powered drones deliver goods, monitor crops, and assist in disaster relief.

Why It Matters

Physical AI is not just about efficiency—it’s about transformation. It can streamline logistics, reduce human exposure to dangerous environments, and expand accessibility for those with mobility challenges. It promises to reshape how industries operate, how healthcare is delivered, and even how we move through cities.

But with opportunity comes responsibility. Autonomous systems must be safe, reliable, and ethically governed. Questions of accountability—who is responsible when a robot makes a harmful decision—will become central as adoption grows.

The Takeaway

Physical AI is AI with a body. It’s already here, reshaping logistics, healthcare, mobility, and beyond. As machines gain the ability to think and act, society faces both extraordinary opportunities and profound challenges. The age of embodied intelligence has begun, and how we guide its growth will determine whether it becomes a trusted partner in human progress or a source of new risks.

D2C Brand EDT Launches Recipe Ramsay: WhatsApp-Powered AI for Smarter Kitchens

• WhatsApp Bot link - whatsapp.edtworld.com - Users can simply send “Hi” or “Recipe” to start interacting with the bot.

EDT, the new-generation consumer appliances brand reimagining everyday devices for modern homes, today announced the launch of , “Recipe Ramsay” in partnership with SagePilot AI. This is a first-of-its-kind AI-powered Kitchen OS built specifically for LUMA, its intelligent PureGlass Air Fryer with NutriRetainTM Edge. With this launch, EDT becomes one of the first companies to tightly integrate a conversational AI recipe agent directly with an intelligent kitchen appliance, bridging thoughtful hardware with practical, everyday AI.

Built on WhatsApp, Recipe Ramsay addresses a core behavioural insight: the friction in cooking isn’t execution, it’s decision-making. In over 500 consumer interviews across demographics and cities, EDT found that nearly 95% of respondents struggled not just with cooking itself, but with also deciding what to cook each day. Variables such as available ingredients, time, dietary preferences, calorie or protein goals, spice levels, and serving size collectively create daily decision fatigue.

Commenting on the launch, Naiyya Saggi, Co-Founder & CEO, EDT, said, “The next frontier of AI is Physical AI, intelligence embedded directly into the products people use every day. At EDT, the long term vision is to build stable, integrated systems where hardware and AI are conceived together to improve daily use outcomes. Recipe Ramsay is an early expression of that vision. It’s not a generic AI layered onto an appliance, but a tightly integrated intelligence layer that enhances a user’s experience with LUMA’s core performance while reducing everyday cognitive load. By creating an AI-enabled assistive ecosystem around LUMA’s hardware, and delivering it through a familiar interface like WhatsApp, we’re moving beyond novelty toward infrastructure, where technology works quietly and meaningfully inside the home.”

Recipe Ramsay streamlines this complexity into a single assistive, conversational flow. Users input what they have, how much time they have, and their dietary or health preferences or goals. The AI agent responds with a LUMA-optimised recipe, complete with calibrated temperature, time, and cooking mode settings. If ingredients are missing, it enables direct comparison across quick-commerce platforms, compressing the gap between intent and action. By choosing WhatsApp over a standalone app, EDT removes friction points such as downloads, onboarding, and platform switching.

Crucially, this is not a generic AI overlay. The result is an early expression of an hardware augmented by AI experience that reduces cognitive load, improves cooking outcomes, and expands the functional value of the appliance itself.

The launch of Recipe Ramsay reflects EDT’s broader philosophy of integrating meaningful, human-centric technology into everyday devices. The company, which recently announced its $1.4 million pre-seed fundraise led by Sauce VC along with participation from marquee founders and industry leaders, is building a portfolio of contemporary, intelligent home appliances designed around real human behaviour. With LUMA and now the LUMA Recipe Bot, EDT continues to advance its mission of making the everyday extraordinary.

SiMa.ai Advances Physical AI with Modalix™: Compact MLSoC for Robotics, AVs, and Automation

• SiMa.ai Launches Modalix™ to Tackle Power, Performance, and Integration Challenges in Physical AI

SiMa.ai, a leader in Physical AI solutions, today announced the production and immediate availability of its second-generation Machine Learning System-on-Chip (MLSoC™) – Modalix™ – designed to accelerate the scaling of Physical AI across industries.

As sectors such as robotics, autonomous vehicles, industrial automation, and aerospace increasingly push AI to the edge, they face a common challenge: achieving high performance within the strict power, size, and integration constraints of edge devices. Cloud-based AI often falls short due to latency and high energy consumption. Modalix™ addresses this gap by delivering high performance and accuracy under 10 watts, capable of running LLMs, transformers, CNNs, and GenAI workloads efficiently.

Performance, Flexibility, and Low Power

Built on a flexible Arm-based architecture with a native GenAI software stack, Modalix™ supports real-time perception, decision-making, and natural language interaction. Its compatibility with key interfaces such as camera, Ethernet, and PCIe makes it adaptable for use in robotics, automotive, industrial automation, aerospace and defense, smart vision, retail, and healthcare applications.

Complete Platform for Physical AI

Alongside Modalix™, SiMa.ai introduced:

• Pin-Compatible System-on-Module (SoM) – Developed with Enclustra, the compact, power-efficient SoM offers a drop-in replacement for leading GPU SoMs, integrating MIPI, memory, and essential I/O for rapid deployment.
• LLiMa™ Framework – A unified on-device platform for running LLMs, LMMs, and VLMs entirely offline, with features such as curated model zoo access, automated quantization/compilation, and support for agent-to-agent systems, MCP, and RAG.

The integrated Palette™ SDK software, enables developers to move from prototype to production quickly and cost-effectively.

Industry Partnerships Driving Innovation

“SiMa.ai’s Modalix showcases the scale of innovation possible on Arm’s flexible, high-performance, power-efficient compute platform,” said Ami Badani, Chief Marketing Officer, Arm. “By bringing AI and LLM capabilities to Physical AI applications at the edge, SiMa.ai is enabling smarter, faster, and more sustainable systems across industries.”

“The development of Physical AI applications requires validated, purpose-built silicon and software, only possible using advanced design solutions,” said Ravi Subramanian, Chief Product Management Officer, Synopsys. “Achieving a successful first tapeout of MLSoC Modalix illustrates the mission-critical role of Synopsys AI-powered design and IP.”

“This Enclustra–SiMa.ai SoM is more than just a module – it’s a ready-to-deploy Physical AI platform,” added Philipp Baechtold, CEO of Enclustra.

Leadership Perspective

“The era of Physical AI is here,” said Krishna Rangasayee, Founder and CEO of SiMa.ai. “With Modalix™ now in production, we’re accelerating its global adoption and simplifying on-device LLM deployment. Demand for our Modalix SoM is strong, and we’re enabling developers worldwide to bring GenAI to Physical AI systems faster than ever.”

TSMC’s advanced N6 process technology powers Modalix™, ensuring it meets stringent embedded power, thermal, and reliability demands. “TSMC is proud to collaborate with SiMa.ai to deliver advanced SoCs that meet the growing demand for Physical AI,” said Sajiv Dalal, President of TSMC North America.

About SiMa.ai

SiMa.ai is a leader in Physical AI, delivering a purpose-built, software-centric platform that brings best-in-class performance, power efficiency, and ease of use to Physical AI applications. Focused on scaling Physical AI across robotics, automotive, industrial automation, aerospace & defense, smart vision, and healthcare, SiMa.ai is led by seasoned technologists and backed by top-tier investors. Headquartered in San Jose, California. Learn more at www.sima.ai.