Chef Robotics alleviates labor and cost issues in food production with AI assembly system

California-based Chef Robotics has developed a robotic assembly system for the food industry that uses computer vision and AI to handle various ingredients. The system enhances food assembly, which accounts for nearly 60–70% of labor in food production.

An aging workforce, low wages, and migration to other sectors deter new entrants in food production, leading to labor shortages in the agro-food sector, according to the OECD.

Chef Robotics aims to overcome these challenges by reallocating labor for tasks like cutting, cooking, and plating, while managing ingredients such as cheese, leafy greens, and sauces with different textures and properties.

The company received US$43.1 million in funding last month, which it plans to use to speed up the production deployments of its “Robotics-as-a-Service” systems in a highly variable food and foodservice sector.

Food Ingredients First sits down with CEO and founder Rajat Bhageria to understand the emergence of AI and robotics in food production, and the labor challenges they can address in food assembly and preparation.

What trends do you see regarding the use of AI in food manufacturing?

Bhageria: Two macro-trends are impacting the industry. One is simply labor, which is not a new trend, and is perhaps getting worse. Many food plants have harsh working conditions — either very cold (around 34°F/1°C) or hot, like kitchens. Hiring for these requirements is tough, and there are ancillary problems around retaining that labor.

The second trend is the rise of AI. Automation in food production is not a new idea. But traditional automation is hardware-based and lacks smart sensors like cameras or lidar, so it can only handle repetitive tasks and isn’t flexible enough to automate a lot of human tasks today. This is why the labor shortage is so impactful.

Chef Robotics’ ultimate goal is to help companies maintain consistency and reduce food wastage, says Bhageria.

Can you tell us more about your assembly system and its use in food production?

Bhageria: After visiting many food production plants, we realized that the process has three main steps: cutting, cooking, and assembling. Around 60 to 70% of labor is in assembling the food, not cooking it. In food factories, assembly lines are long, with 10-15 people on each side, each adding one ingredient to a tray. It’s repetitive and labor-intensive.

We decided to provide a flexible automation system, with a small, mobile robot we call a “chef module.” It has the same footprint as a person, and can be placed right into the existing line. Using computer vision and AI, it identifies where to pick ingredients from and figures out how much weight it picked up. The ultimate goal is to help companies reallocate that labor, freeing up workers for other tasks.

What were the challenges you faced while designing the system?

Bhageria: On the technical side, handling food with robots is much harder than it seems. For example, a human can easily sprinkle cheese on a meal, but for a robot, that’s complex. Our robot, Chef, needs to handle various ingredients and portion sizes without damaging or spilling anything. Traditional robots are designed to handle solid, predictable objects like boxes. But food is soft, sticky, and wet. There’s very little existing research or tools to help with that. So, we had to develop our own robotics and AI systems and improve them directly for real-world use.

On the business side, most food companies are used to buying equipment through one-time capital expenditure. But Chef runs on software, so our model is based on subscriptions, like “Robotics-as-a-Service.” Instead of paying upfront, customers pay a recurring fee for the software, updates, and support. For Chef, this includes new ingredients, onboarding, and software. But many customers aren’t familiar with it, leading to an uphill battle in the early days, where we had to convince our early customers about why this makes sense. We overcame that by focusing on customer success by proving the return on investment (ROI), and sharing case studies.

What kinds of businesses use your robots?

Bhageria: We currently focus on four main segments: frozen prepared meals, fresh food manufacturing, contract manufacturers, and direct-to-consumer meals. In frozen meals, we work with companies like Amy’s Kitchen, and with fresh food companies working with salads, wraps, sandwiches, and parfaits that don’t need heating. In contract manufacturing, we work with companies that produce food for various brands, and also with brands like Sunbasket or Factor that deliver ready-made meals to consumers. 

We are also exploring other areas like airline catering, meat and fruit packing, and pizza production. The company’s long-term vision is to leverage all that training data about manipulating food to go to smaller-volume kitchens, such as those in fast-casual restaurants, and ghost kitchens. But today, we are focused on the industrial side. Future applications could include food preparation and packaging.

While human workers have varying energy levels throughout a shift, robots work at a steady pace.

How cost-effective is this technology, and what potential does it have for the future?

Bhageria: We look at cost-effectiveness through ROI rather than just upfront costs. Most customers run two shifts daily and spend a lot on wages and benefits. Our robots usually cost less than hiring two workers, depending on the job’s complexity. We also target increased production capacity, since some companies can’t run all their production lines due to labor shortages. Our robots can fill those gaps, helping them increase output and revenue. 

We can also help companies get more consistent output. Human workers can be inconsistent, with varying energy levels throughout a shift. Robots work at a steady pace, which increases the average number of meals produced over time. We also target the food industry’s “giveaway” or food wastage. Humans tend to over-portion to avoid complaints, especially when scooping by hand, leading to extra food costs. Our robots use scales and sensors to be precise, reducing waste and saving money.

What are your thoughts on AI replacing human jobs?

Bhageria: I definitely understand the fear and the reason, especially with how it’s portrayed in movies. But I don’t think it’s the reality of the situation, specifically because of a labor shortage. Many factories can’t run all their production lines because they don’t have enough staff, or rely on temporary workers, which leads to high turnover and instability. Robots can fill gaps where workers are hard to find, especially in challenging conditions like cold environments with repetitive tasks.

In the bigger picture, automation usually leads to more jobs, not fewer. When companies become more successful, the economy booms, costs go down, and more businesses open up, creating new opportunities. That is what the general trend in automation has been like. There’s no replacement, per se, and furthermore, it actually should create more jobs over time as things become more efficient.