The embodied AI brain company backed by HSG and Alibaba secures new financing, with investors including Shanghai Jiao Tong University participating in a deal worth hundreds of millions of yuan | Exclusive by Hard Krypton

Author | Huang Nan

Editor | Yuan Silai

Yingke has learned that the embodied intelligence enterprise Noematrix recently completed a new round of financing worth hundreds of millions of yuan. This round of financing was led by Wuxi Data Group, and the investors include the AI Future Fund (Venture Fund) of Shanghai Jiao Tong University, Shanghai Chuangzhizhi Technology Co., Ltd. (a wholly - owned subsidiary of Shanghai Chuangzhi College), Yicun Capital, etc.

This is also another round of financing completed by the company in the past six months. Previously, Noematrix had received support from multiple institutions, including Prosperity7 Ventures, Sequoia China, C Capital, Alibaba, and Sea Limited.

Noematrix is an enterprise that Yingke has been following for a long time. The company was established in November 2023 and has long been focusing on the independent research and development of the basic models and systems of embodied intelligence. It has released its core product, "Noematrix Brain", and built a software - hardware product system covering the entire process from data collection, model training to deployment verification and embodied robot applications around it.

The industry narrative of embodied intelligence is undergoing a silent shift. In the past two years, the highly - sought - after ability in the field was "completing an action". Tasks such as grasping, moving, and walking were repeatedly verified in the laboratory or on the demonstration platform. However, since this year, a new measure has emerged: whether the robot can work continuously and stably in a real physical environment, rather than completing a one - time demonstration under controllable conditions.

The essence of this shift is the migration of the focus from "action ability" to "engineering stability". To enable the robot to deeply understand the operating laws of the real physical world and adapt to the uncertainties of complex environments autonomously is exactly the problem that the current world model is trying to solve.

In terms of data strategy, Noematrix chooses to incorporate real data and simulation data into the training system in parallel. The "accompanying data collection" solution it proposed earliest can expand data collection to environments such as homes, offices, and industries in a lightweight and low - cost way through self - developed data collection devices such as the exoskeleton CoMiner and the portable RoboPocket, gradually building a database covering diverse physical scenarios.

Noematrix believes that real data will make the model more stable and robust after entering the model because it comes from real physical scenarios; simulation data uses its scalability advantage to expand the ability boundary. The two cooperate rather than replace each other. At the same time, the company has also built a closed - loop system integrating AI Agents, which is responsible for analyzing tasks, issuing instructions, optimizing collection behaviors, and then dynamically adjusting subsequent collection tasks according to the data distribution, greatly improving the efficiency of obtaining high - quality data.

Noematrix's "accompanying data collection" solution (Source/Enterprise)

This data can provide fuel for the training and iteration of Noematrix Brain. Noematrix adheres to the self - research route of the general embodied intelligence large model, conducts model pre - training based on a large amount of real - scene data, and enables the system to establish a basic understanding of the physical world before being put into actual use; supplemented by force - position hybrid post - training, it further calibrates the model's understanding accuracy of contact states and force - sense information, making the action instructions it outputs not only reasonable at the semantic level but also executable at the physical level.

Through this training process, the general embodied brain Noematrix Brain for physical scenarios is continuously refined, providing the robot with a complete decision - making closed - loop ability from instruction understanding, task planning to environmental perception and execution feedback. At the same time, Yingke has learned that Noematrix also plans to officially release a new generation of self - developed embodied intelligence world model in the near future.

In specific task scenarios, Noematrix's robots have been deployed in batches in pharmacies. The "embedded upgrade" route it adopts does not require the transformation of the original shelf structure. It can be deployed in a 2.5 - square - meter space and directly connected to the existing order system of the store, and can operate stably in the original passage environment.

Navigation path planning (Source/Enterprise)

For a long time, the commercial value of the pharmacy scenario lies in solving the long - standing structural pain points of manpower in the industry. Offline pharmacies generally face the dilemma of scattered night orders but must have a dedicated person on duty. Hiring a person for night duty has high costs and extremely low cost - effectiveness, which is a long - term pure cost item for the store. The fulfillment work of online pharmacy orders is highly standardized, requiring no complex customer communication and sales promotion, and only needs accurate and repetitive picking actions, which is exactly the work scenario that robots are best at.

Meanwhile, there are thousands of SKUs in a pharmacy. The products are messed up due to searching, have different packaging forms, and the shelf arrangement changes at any time. All kinds of small - probability situations in complex tasks are normal in the real operation state. Even though the adaptation scenario is simple and the operation process is standardized, it is still full of complex variables that are difficult to reproduce in the laboratory.

The Noematrix team said that what really needs to be dealt with in the pharmacy deployment is more the accumulation of corner cases rather than a fundamental breakthrough in the technical paradigm. "A tube of erythromycin eye ointment is difficult to handle stably with a gripper or a suction cup because of its small packaging and non - standard placement angle; the grabbing logic of a thermometer also needs to be readjusted because it is hung for display rather than packed in a box. These special products account for a small proportion of the entire SKU, but it is precisely them that determine whether the system can be migrated from the laboratory to the real scenario."

Pharmacy packing (Source/Enterprise)

Currently, Noematrix has reached cooperation with several leading chain pharmacies and entered the commercial delivery stage, with the order scale reaching the level of thousands of units.

After this round of financing, Noematrix will continue to promote the research and development and iteration of the embodied intelligence large model with strong generalization ability and high autonomous decision - making ability, and accelerate the implementation of embodied intelligence in real scenarios such as general retail and hotel services.

The following is an excerpt from the interview between Yingke and Noematrix (slightly edited):

Yingke: What mature capabilities does a deployable physical AI embodied brain need to have at this stage?

Noematrix team: For the embodied brain to truly run through offline scenarios, three core capabilities are indispensable.

First is the mature and stable algorithmic ability. Noematrix released a general grasping model as early as 2021 and has continued to iterate in the following years. The success rate of grasping operations is relatively leading internationally. This set of algorithms forms the technical foundation of our pharmacy solution, enabling the robot to maintain a relatively high operation success rate when facing thousands of SKUs.

Second is the engineering implementation ability. The laboratory demonstration environment is relatively ideal, but in real stores, the site layout and product placement are constantly changing, which can easily affect the robot's operation effect. This requires not only optimizing the algorithm but also doing a good job in on - site debugging and hardware adaptation. Relying on the delivery experience of serving hundreds of customers and implementing thousands of scenarios in the past, we have explored a mature method that can make almost zero changes to the store and allow the robot to quickly enter the site and put into use.

In addition, there is also support at the data level. There are a large number of edge scenarios (corner cases) in the real physical environment that are difficult to simulate in the laboratory and need to be polished. For example, a tube of erythromycin eye ointment is difficult to handle stably with a gripper or a suction cup because of its small packaging and non - standard placement angle; the grabbing logic of a thermometer also needs to be adjusted because it is hung for display rather than packed in a box. These special products account for a small proportion of the entire SKU, but it is precisely them that determine whether the system can be migrated from the laboratory to the real scenario. The premise of solving these problems is to have enough real physical world data.

Application of Noematrix's embodied robot in the pharmacy scenario (Source/Enterprise)

Yingke: What new demands and trends are presented by clients in the retail pharmacy scenario? How to measure the return on investment?

Noematrix team: Noematrix currently focuses on the chain pharmacy scenario. The profit margin of offline pharmacies is already tight, and labor is the second - largest cost after rent. Therefore, it can be seen that the clients' demand for cost reduction is very clear. Although the night orders in pharmacies are scattered, someone must be on duty. Hiring a person has extremely low cost - effectiveness, and the robot can just take over the online picking work at night. It can also assist in processing orders during the day. On average, each store can reduce 1.5 manpower.

This value is reflected in the return on investment. The ROI calculation logic is very straightforward, which is to compare with the labor salary. According to the implementation situation, the pay - back period for our customers after purchasing the robot is about one and a half to two years. For chain pharmacies, this pay - back period already has strong implementation value. Currently, the implemented stores are mainly concentrated in first - and second - tier cities, and there are projects running in cities such as Guangzhou, Shenyang, and Nantong.

Comments from investors:

Shanghai Jiao Tong University and Shanghai Chuangzhi College have long maintained close cooperation with Noematrix in areas such as joint laboratories and scientific research. This time, the investment platforms under them have simultaneously invested in Noematrix, marking a new stage of in - depth "technology + capital" cooperation. Among them, the AI Future Fund of Shanghai Jiao Tong University was initiated by the School of Artificial Intelligence of Shanghai Jiao Tong University, which aggregates the alumni and industry forces of Shanghai Jiao Tong University to support the "Shanghai Jiao Tong University - affiliated" artificial intelligence innovation projects with significant industrialization prospects. This not only provides a solid backing for Noematrix to continuously introduce high - end scientific research talents and explore cutting - edge technologies but also consolidates its "moat" in the scientific research of embodied intelligence. In the future, Noematrix will jointly tackle key problems in the cutting - edge technology field of embodied intelligence models with Shanghai Jiao Tong University and Shanghai Chuangzhi College, and accelerate the transformation of embodied intelligence technology achievements into industrial applications.

Wuxi Data Group, as the core force promoting the development of the digital economy and the marketization of data elements in Wuxi City, will join hands with ecological partners such as Noematrix to jointly launch the city - level all - domain "Thousands of Enterprises, Millions of Hours" high - quality dataset consortium action for embodied intelligence and officially release the construction results of the first stage. Next, the two sides will rely on Wuxi Data Group's industrial resources and scenario governance capabilities, combined with Noematrix's technical accumulation in cutting - edge models and embodied brains, to deeply promote the construction of industrial datasets. The two sides will give full play to their respective advantages in technology, data, and industrial resources to promote the implementation of embodied intelligence in real production lines.