Food manufacturers shouldn’t have to choose between automation and food safety. Chef’s AI-enabled robots are designed specifically for food-production environments, where equipment must be easy to inspect, clean, and sanitize across frequent ingredient and SKU changeovers. Our latest blog explains how Chef robots support food safety, sanitation, and allergen control through: → 𝗡𝗦𝗙-𝗰𝗲𝗿𝘁𝗶𝗳𝗶𝗲𝗱 𝗵𝗮𝗿𝗱𝘄𝗮𝗿𝗲: Chef’s robotic module C-001748 is certified under NSF/ANSI 169 — Special Purpose Food Equipment and Devices. → 𝗠𝗶𝗻𝗶𝗺𝗮𝗹 𝗳𝗼𝗼𝗱-𝗰𝗼𝗻𝘁𝗮𝗰𝘁 𝗽𝗼𝗶𝗻𝘁𝘀: Only Chef’s interchangeable utensils and stainless-steel hotel pans come into direct contact with ingredients. → 𝗙𝗼𝗼𝗱-𝘀𝗮𝗳𝗲 𝘂𝘁𝗲𝗻𝘀𝗶𝗹𝘀: Our utensils are made using 306 stainless steel and food-grade blue Delrin. → 𝗧𝗼𝗼𝗹-𝗳𝗿𝗲𝗲 𝗿𝗲𝗺𝗼𝘃𝗮𝗹: Line workers can remove and replace utensils without any tools in less than a minute and clean them in an industrial dishwasher. → 𝗦𝗮𝗻𝗶𝘁𝗮𝘁𝗶𝗼𝗻-𝗳𝗿𝗶𝗲𝗻𝗱𝗹𝘆 𝗰𝗼𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻: An IP67-rated design, angled surfaces, sanitary welds, an open angle-iron frame, removable scale plates, and built-in cleaning gaps make our system straightforward to inspect and clean. Read the full blog to learn how food safety and cleanability are built into every layer of Chef robots: chefrobotics.ai/post/how-che… #FoodSafety #FoodManufacturing #PhysicalAI #Automation #Foodsanitation

We’re heading to PACK EXPO International in Chicago. Stop by booth LL-10401 to meet the Chef team, see our robot in action, and learn how flexible automation can solve real-world challenges on the production floor. We’re also hosting a happy hour for food manufacturers at our booth: 𝗠𝗼𝗻𝗱𝗮𝘆, 𝗢𝗰𝘁𝗼𝗯𝗲𝗿 𝟭𝟵 | 𝟰–𝟲 𝗣𝗠 𝗖𝗧 We’re keeping this one small and focused, so if you’re a food manufacturer, co-packer, packaging equipment manufacturer, or automation partner, apply for a spot here: luma.com/hlydxfa0 #PACKEXPO #FoodManufacturing #Automation #ChefRobotics

Are you a food manufacturer who is still staffing 8-10 workers on every meal assembly line to hit your production targets? One of our customers now runs the same line with 3-4 workers and produces 2-3x more meals. The reason? Flexible automation. High-mix production has historically been too variable for traditional depositors and dispensers. Food manufacturers run different SKUs, ingredients, portions, tray sizes, and placement requirements, often on the same line. Chef robots are built for exactly this. Our AI-enabled robots adapt to real-world production variability, and changeovers between SKUs take under a minute, allowing manufacturers to increase throughput while moving workers away from repetitive, hard-to-staff tasks. If your meal assembly lines still rely heavily on manual labor, comment “Demo” below and we'll reach out, or contact us here: chefrobotics.ai/contact-us

We’re at the @allinpod's Summit in LA today and tomorrow! Stop by the Frontier Tech Hub anytime to see a live demo of a Chef robot and chat with our team. #AllInSummit

We're featured on S3! Big thanks to @jasonjoyride and his team for this excellent story! Check out the full video: youtube.com/watch?v=XPcfqsAe…

Chef Robotics is heading to the All-In Summit! 🤖 📅 Sept. 13-15, 2026 📍 Los Angeles, CA Our team - Rajat Bhageria, Jay Beversdorf, and Charlotte Kosche will show a Chef robot in action and answer your questions about physical AI, food manipulation, and what it takes to scale robotics in production. If you're attending the Summit, come find us. We'd love to connect! #allinsummit #physicalai #foodautomation #foodproduction

Chef robots run in production across North America and Europe today. Each robot needs to stay up, get fixed fast, and keep improving over time, no matter the time zone. 𝗧𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 A robot in Europe can go down while our US team is asleep. Our robots combine hardware and software, so when something breaks, our engineers aren't just debugging code; they're often diagnosing physical wear on a real machine too. Spare parts crossing borders and language differences with local facility teams add friction on top of that. 𝗧𝗵𝗲 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻 Here's how we keep 100+ robots running across three countries: 1. Customer requests flow into an integrated portal for triage. 2. Simple issues get resolved right away. 3. Complex ones escalate to our support engineers, who use tools like Foxglove and in-house tools to debug live systems from thousands of miles away. 4. For hands-on repairs, a network of certified local technicians handles routine maintenance, and our own engineers travel on-site for the bigger integrations. 𝗧𝗵𝗲 𝗿𝗲𝘀𝘂𝗹𝘁 Customers expand with us. One customer grew from 2 robots to 36. Another went from 2 to 22. And we didn't need to add more headcount to our support team to make that happen. Read the full story on our blog: chefrobotics.ai/post/how-che…

What if a robot could predict the outcome of an action before it starts moving? Chef robots have made over 130 million servings in production. Each serving generates real-world data about the robot’s action and its measured outcome. We’re using this data to train a Food World Model, a neural simulator that predicts both how much food a robot will pick and the surface it will leave behind. In an initial run, the model: 1. Improved overall weight-prediction error from 13.6 g to 10.3 g 2. Reduced post-pick surface errors by 42% 3. Learned from measurements collected during normal production without additional manual labeling This is an early step toward our robots simulating candidate actions, choosing the one most likely to hit the target weight, and adapting faster to new ingredients. Read part 1 of our Food World Model blog series and explore the interactive pick visualizer: chefrobotics.ai/post/buildin…

AI is changing not only software, but also how physical products are designed and built. In robotics, we’re seeing shorter development cycles, fewer physical prototypes, and the ability to test more ideas before committing to one. Of course, AI can’t replace every hardware-dependent process in an industry built around the physical world. But it can give us better tools to solve complex engineering problems faster. Rajat Bhageria spoke with Packaging Insights about what this looks like at Chef and where AI-enabled manufacturing is headed: packaginginsights.com/specia…

You've probably already eaten food made by one of our robots; you just didn't know it. → The wrap you grabbed on your way to work → The frozen meal at Whole Foods → The salad from Trader Joe's → The meal on your last flight → The tray at a hospital → The lunch at a school cafeteria Today, Chef has: → 100+ robots in the field → 130,000,000 meals in production - more than everyone else combined by an order of magnitude → 170,000 hours of production data → Customers across the US, Canada, and Europe To learn more about how automation is reshaping the food industry, watch Rajat's full conversation with NYSE theCUBE: youtube.com/watch?v=eT6GaO2i…

How many auger fillers does it take to fill a three-ingredient tray? Three, as each filler is calibrated for one ingredient. High-mix meal assembly requires equipment to handle many ingredients, constant SKU changes, quick changeovers, and zero tolerance for giveaway. 𝗛𝗼𝘄 𝗮𝘂𝗴𝗲𝗿 𝗳𝗶𝗹𝗹𝗲𝗿𝘀 𝗯𝗲𝗵𝗮𝘃𝗲 𝗼𝗻 𝗮 𝗵𝗶𝗴𝗵-𝗺𝗶𝘅 𝗹𝗶𝗻𝗲 → Each filler has custom hardware for one ingredient; running dozens of SKUs means dozens of single-purpose fillers sitting idle between runs → Changeovers take hours, cleaning and recalibrating between every SKU; Allergen switches take even longer, since fine powder residue can become airborne 𝗪𝗵𝗮𝘁 𝗮𝘂𝗴𝗲𝗿 𝗳𝗶𝗹𝗹𝗲𝗿𝘀 𝗮𝗿𝗲 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗯𝘂𝗶𝗹𝘁 𝗳𝗼𝗿 → Built to run one dry, free-flowing ingredient, like spices, flour, or protein powder, at high volume, reliably 𝗪𝗵𝘆 𝘄𝗲 𝗯𝘂𝗶𝗹𝘁 𝗖𝗵𝗲𝗳 𝗿𝗼𝗯𝗼𝘁𝘀 → Chef's AI-enabled meal assembly robots pick and place ingredients by weight, not volume, so weight never drifts the way a rotation count would → Changeovers take minutes, not hours, because there's no hopper to clear or screw to recalibrate → One robot handles every ingredient in a SKU, wet or dry, that would otherwise need a separate filler, or a person, for each one Full comparison, including where an auger filler is still the right call: chefrobotics.ai/guides/chef-… #FoodManufacturing #FoodRobotics #Automation

What if robots could imagine what happens next before they act? That's the promise of world models: AI systems that learn to predict possible futures, plan, learn, and generate new experience before taking an action in the physical world. In our latest survey paper, we map the rapidly evolving field of world models and world-action models. We cover over 220 research contributions and look at 160 systems across six modeling approaches and six application domains, from robotics and autonomous driving to reinforcement learning, gaming, and general-purpose simulation. One of the biggest open questions is whether models that generate a convincing future actually understand the physical world. That distinction is critical for robotics: a future that looks plausible but violates physics isn't good enough when a robot needs to act on it. Read our paper for an accessible overview of the field, the major approaches, and the research challenges that need to be solved before we can bring world models into the physical world: chefrobotics.ai/post/world-m… #robotics #worldmodels #physicalai

Most companies raise capital to find product-market fit. At Chef Robotics, we did it the other way around. From day one, our CEO, Rajat Bhageria, insisted on this: don't go to investors with a vision alone. Bring evidence from customers. Signed commitments. Willingness to pay. Proof that the problem is real, not just interesting. That sequencing runs against the standard playbook, where most companies raise first and look for proof after. We wanted proof first, so that when we did raise, investors weren't asked to believe in a hypothesis. Proving traction first is slower in the short term in a category as capital-intensive and unforgiving as robotics. But eventually it compounds: Chef robots have now completed over 127 million servings in production, with customers seeing 2-3x output increases and changeovers under 10 minutes. More of this in the TechCrunch Disrupt conversation: youtube.com/watch?v=02hqvJb6… #techcrunchdisrupt #aiandrobotics #foodautomation #physicalAI

What does it take to build a lasting food business? In Episode 2 of our Food Builders podcast, Rajat sits down with Sameer Malhotra, Co-Founder and CEO of Cafe Spice, to discuss the company's journey from a family-run restaurant business in New York City to a leading producer of ready-to-eat meals. Sameer shares how he and his father, Sushil, scaled Cafe Spice by expanding into new markets, launching new product lines—including Latin American meals under the Cantina Latina brand—and building a strong team along the way. They also discuss leadership, hiring, company culture, and what the future holds for Cafe Spice and the food industry. 🎙️ Watch or listen to the full episode on YouTube: youtube.com/watch?v=83sWTh3e… or Spotify: open.spotify.com/episode/2gn…

We just published a new survey paper on vision-language-action models (VLAs) for bimanual manipulation. VLAs have gained popularity in robotics research, but while they've demonstrated impressive capabilities, it can be difficult to understand how different architectures compare and which ones are best suited for real-world deployment, especially for bimanual manipulation. To answer these questions, we reviewed more than 200 papers and compared 31 VLA methods. We examined their architectures, training strategies, action representations, and real-world applications. Our key takeaway is that not all bimanual tasks require the same level of coordination, which impacts the best VLA method and architecture to use. For tasks that require two robot arms to remain tightly synchronized, architectures that generate both arms' actions jointly are better suited than approaches that generate them sequentially. Among today's methods, flow-based action generation offers one of the strongest reported combinations of coordinated action generation and the speed required for real-time control. The survey also examines where the field needs to go next, including: 1. Standardized benchmarks for bimanual manipulation 2. Better force, tactile, and multimodal sensing 3. The safety and reliability needed for large-scale commercial deployment Read the full survey paper: chefrobotics.ai/post/vision-… #robotics #physicalai

A food depositor deposits one ingredient. A Chef robot assembles an entire meal. Here's when each one is the right fit. Depositors (also called dispensers or fillers) are excellent at depositing one flowable ingredient like sauces or dressings at high speed. But they fall short for high-mix meal assembly for two reasons: → A depositor is unable to handle many ingredients such as diced proteins, vegetables, grains, and mixed foods, as they don't flow well through a nozzle. → Even for ingredients a depositor can handle, like sauces, changeover time makes it impractical. Switching ingredients means cleaning the full ingredient contact path, which takes hours when SKUs switch several times a day. We built Chef robots to solve this. Here's a buying guide comparing the two in detail: chefrobotics.ai/guides/chef-… #foodmanufacturing #foodautomation #mealassembly #foodrobotics

How should robots learn when things don't go as planned? Our latest engineering blog introduces ValueFormer, a lightweight critic model that helps our Food Foundation Model learn from mistakes, not just successes. ValueFormer watches the same camera feeds as our robot, evaluates progress in real time, and provides feedback that behavior cloning alone can't. By incorporating human interventions and critic-generated feedback into training, we improved successful burger assembly from 70% to 85% on a challenging consecutive assembly task. This work builds on our broader vision for physical AI: combining production data, human expertise, and continuous learning to create robots that improve over time. Read the full tech blog: chefrobotics.ai/post/buildin…

Every meal makes the next one better. Chef robots have assembled more than 120 million servings in production, generating one of the world's largest datasets for food manipulation along the way. Every serving adds production data that improves our physical AI models—helping Chef robots handle more ingredients, more tray types, and more edge cases, all on the same hardware. This data flywheel has been turning since our first deployment. Today, it helps customers: - Reduce giveaway by up to 88% - Increase output by 2–3× - Improve labor productivity by up to 60% - Reach full production faster than earlier deployments Unlike language AI, there is no internet-scale dataset for food manipulation. The only way to build these models is through years of production experience on real manufacturing lines. In our latest blog, we explain how this flywheel works, why it compounds over time, and why we believe production data is one of the strongest moats in physical AI. Read more: chefrobotics.ai/post/the-dat…