Feixian Intelligence uses intelligent evaluation and scheduling technology to solve the pain points of enterprise large model implementation and launches an angel round of financing.
In the critical period when the artificial intelligence industry is moving from "model innovation" to "large-scale implementation", although large model technologies have achieved rapid iterations, enterprises still face severe challenges in the "last mile" of large-scale application. Nonlinear Intelligence emerged in response to this situation, focusing on intelligent evaluation, scheduling, and performance amplification of large models. By integrating heterogeneous model resources through core technologies, it helps enterprises reduce the cost of AI usage and improve application efficiency. Currently, the project has completed the seed round of financing and is advancing the angel round of financing process.
Prominent Pain Points: Obstacles in Enterprise Large Model Implementation and Urgent Market Demand
Currently, the large model market is experiencing explosive growth, with 10 - 15 new models launched every month. However, when enterprises introduce large model capabilities, they generally fall into multiple dilemmas, creating a significant market demand gap. According to industry research, 70% of small and medium-sized enterprises' AI projects return to traditional rule engines due to insufficient computing power. Even enterprises with certain capabilities face three core pain points, which are also the core opportunities for Nonlinear Intelligence to enter the market.
Firstly, the dilemma of model selection is prominent. There are a large number of scattered heterogeneous models in the market. Enterprises lack the professional ability to judge the performance, cost, and applicable scenarios of different models, and are prone to "selection mistakes", resulting in a mismatch between technology and business requirements and the inability to realize the actual value of large models. Secondly, the usage cost remains high. The large model calls adopt a Token-based billing model, and the inference cost accounts for 60% of the total enterprise AI investment. For example, an e-commerce intelligent customer service calls the API 100,000 times a day, with an annual cost as high as 1.2 million yuan. The high cost deters many enterprises. Thirdly, the performance stability is insufficient. A single model supplier is prone to performance fluctuations, and general large models have problems such as hallucination generation and knowledge lag. In professional scenarios such as finance and healthcare, the error rate fails to meet enterprise requirements. At the same time, there is a lack of efficient and feasible standards for model performance evaluation.
As the application of large models penetrates into various industries, enterprises' demand for efficient, low-cost, and stable model scheduling and management services is growing exponentially, which also provides broad market space for the track that Nonlinear Intelligence is in.
Technical Breakthrough: Centered on Intelligent Scheduling to Build Differentiated Solutions
In response to the core pain points of enterprise large model implementation, Nonlinear Intelligence positions itself as an intelligent scheduling center and performance amplifier for large models. Through a unique technical system, it creates standardized AI services like "water, electricity, and gas", making it as simple, stable, and economical for enterprises to obtain AI capabilities as using public facilities. Its solution is supported by two core technologies, forming distinct differentiated advantages.
One of the core technologies is intelligent evaluation and routing technology. Nonlinear Intelligence has built an efficient model evaluation system. Referring to the core results of the paper "ReLE: A Scalable System and Structured Benchmark for Diagnosing Capability Anisotropy in Chinese LLMs" jointly published by it and top institutions, this system can achieve multi-dimensional real-time evaluation of different models in response to the problem of capability anisotropy of Chinese large models. The dynamic variance-aware scheduling mechanism proposed in the paper can reduce the computing cost by 70% while maintaining a ranking correlation of 0.96, ensuring the accuracy of the evaluation results. On this basis, the intelligent routing technology can analyze the performance and call cost of different models in real-time and automatically match the most cost-effective model for each user request, avoiding the performance fluctuation risk caused by relying on a single model.
The other core technology is Token compression technology. Combining the research results of institutions such as Harvard and MIT, the Token compression technology adopted by Nonlinear Intelligence is not a simple text truncation. Instead, it screens redundant Tokens based on semantic importance. Through an attention pruning idea similar to VisPruner, it removes meaningless redundant information and retains more than 98% of the key semantics, thereby significantly reducing the enterprise's Token consumption and achieving extreme cost reduction. At the same time, this technology can also alleviate the problem of Token attention weight dilution in long context scenarios and improve the model inference accuracy.
In addition, Nonlinear Intelligence integrates scattered heterogeneous model resources into standardized and easy-to-use basic services through a unified API interface. Enterprises do not need to invest a large amount of manpower to build a complex technology stack, which helps enterprises quickly implement large models and focus on their own business innovation.
Steady Progress: Supported by Industry-University-Research Cooperation, Parallel in Financing and Market Expansion
The rapid development of Nonlinear Intelligence is inseparable from its profound team background, strong industry-university-research cooperation resources, and the steady progress of product implementation and financing. It has formed a certain competitiveness in the market.
In terms of team and technology reserve, the Nonlinear Intelligence team has a profound technical background and more than 12 years of experience in AI industry technology implementation. Team members graduated from institutions such as the Massachusetts Institute of Technology and the University of Michigan in the United States. They were once students of Professor Zhu Songchun, a global leader in general artificial intelligence and the winner of the Marr Prize. They are the full - fledged team of an AI unicorn. Some were the first employees of an AI unicorn and once led an AI team of hundreds of people at Ping An Group. The team has dozens of core invention patents, and the global citation times of its technical papers in the field of artificial intelligence have reached more than 7,000. The ReLE-related papers jointly published by the team with top institutions such as Sun Yat-sen University, the Hong Kong University of Science and Technology (Guangzhou), Huawei, Ping An of China, and NSFocus Technology have further laid a solid theoretical foundation for the R & D of the platform's intelligent evaluation and scheduling algorithms. The relevant code of this project open - sourced on GitHub has formed a strong community influence among domestic enterprises in the same field, further strengthening its technical barrier.
In terms of product implementation and market expansion, Nonlinear Intelligence launched the public beta version at the end of 2025, introducing core functions such as an intelligent model supermarket, model selection, and online observation, achieving the initial verification of the product. Currently, the platform has successfully attracted Fortune Global 500 enterprises and top scientific research institutions as benchmark customers, and has an exposure advantage in the top three globally on Google and Bing searches, further enhancing its brand influence. It is worth noting that Nonlinear Intelligence is currently the only large model infrastructure service provider in China with both in - depth evaluation capabilities and neutral scheduling attributes, and this neutrality has also become an important advantage in attracting enterprise customers.
In terms of business model and market space, Nonlinear Intelligence adopts a Model as a Service (MaaS) business model, providing standardized model scheduling, evaluation, and performance optimization services for enterprise - level customers, top scientific research institutions, and a large number of AI developers, and achieving profitability through service fees. According to industry reports, the scale of the Chinese AI inference computing power market reached 43.83 billion yuan in 2025, with an average annual compound growth rate of 66.3%. In 2026, the proportion of AI server inference workload will rise to 70.5%. The model scheduling and optimization track that Nonlinear Intelligence is in is in a period of rapid growth and has huge market potential.
In terms of financing, Nonlinear Intelligence has successfully completed the seed round of financing invested by the Shouzheng Capital team. The funds raised are mainly used for technology R & D iteration and initial product promotion. Currently, the project is actively advancing the angel round of financing process. The subsequent financing funds will further focus on the optimization of core product algorithms and the improvement of enterprise - level functions, accelerating the commercial implementation of the product.
Looking forward to the future, Nonlinear Intelligence plans to continue to deepen the core competitiveness of intelligent evaluation and scheduling algorithms, improve the platform service system, expand more benchmark customers and partners, and strive to become a trusted large model application infrastructure for enterprises, playing an important role in promoting the inclusive implementation of artificial intelligence technology.