When DeepSeek reconstructs AI reasoning, how does EgretSoft define intelligent office with a 'decision brain'?

Deepseek, which became popular on the eve of the Spring Festival, differs from previous AI models such as OpenAI's GPT series, Google's Gemini, Meta's Llama, etc. The biggest difference is that it leans more towards inferential AI rather than directive. What is deductive type? It is possible to infer the cause and effect based on the scenario given by the user, analyze the emotions, motivations, and derived needs behind it, and provide answers that are more like those written by humans rather than mechanical 123. On the other hand, the directive type relies more on the user's proficiency in using keywords. Whatever you feed it, it gives back, just like a diligent employee lacking subjective initiative, with no achievements or mistakes. The quality of feedback results depends more on the user's understanding and proficiency in application, and the threshold for trial use is relatively high. After using Deepseek, as a seasoned user who has tried countless AI models, I have some concerns about AI replacing human thinking.

To some extent, Deepseek has propelled the popularization of AI from high-end applications to the general public, firing the first shot of AI intelligence entering the era of universal access. The AI intelligent decision-making function of Kuailu Technology is similar to DeepSeek, which is good at reasoning. In terms of assisting decision-making, Kuailu Technology has transformed each decision-making node from a "passive response" to an "active prediction", marking a qualitative change in AI from an "information transporter" to a "decision-making think tank". Through autonomous logical chain deduction, multi-dimensional data association, and dynamic knowledge graph, EgretSoft has developed a human like decision-making thinking framework, which is also the new paradigm of industrial intelligence written by EgretSoft.

The future of intelligent office is an inevitable trend, and EgretSoft's office system, which focuses on AI intelligence, is undoubtedly a powerful tool for enterprise digital transformation. When the competition in the manufacturing industry shifts from "single factor competition" to "system efficiency war", EgretSoft's intelligent decision-making assistance function, based on past data and analysis of management's past decisions, assists them in making accurate decisions, bidding farewell to the past and making decisions based on evidence. How does EgretSoft's AI assist in decision-making? To help readers better understand, let's imagine a scenario that could happen in reality together.

In the early morning, the production supervisor of a new energy vehicle factory faced a sudden problem. The upstream battery supplier was out of stock due to rainstorm, and the workshop inventory was only enough to maintain production for 8 hours. At this point, the decision-making considerations faced by the management are complex and diverse. Should we urgently switch to a backup supplier? Need to balance cost increases and logistics delay risks; How to adjust the production line schedule? Calculate the impact of 300 types of component inventory on the production of 5 vehicle models; Can we coordinate the production capacity of other factories? It is necessary to predict the carbon emissions and delivery time of cross regional transportation.

The decision-making difficulties in these manufacturing industries are essentially a "dynamic game" of multiple variables intertwined, with real-time fluctuations in supply chain, equipment status, order demand, and cost control, making it difficult for traditional decision-making models that rely on manual experience to make accurate decisions in a short period of time. And Kuailu Technology realizes human-machine collaborative decision-making, from "experience intuition" to "digital intuition", with a knowledge accumulation system that transforms past empirical rules into AI understandable decision rules.

EgretSoft has constructed a closed-loop decision path of "hypothesis verification iteration", which synchronously balances transportation costs, timeliness, and generates Pareto optimal solutions in logistics scheduling scenarios. At the same time, with the help of risk transmission prediction, simulate the cascading impact of supplier interruptions on 100+downstream production nodes, and adjust the scheduling plans of 53 production lines in 4 factories in a coordinated manner. By using intelligent decision-making assistance, based on past project experience data inference and multi-objective optimization capabilities, objective and scientific suggestions can be provided to decision-makers, greatly reducing the losses caused by erroneous decisions.

With the continuous advancement of AI technology, AI intelligent assisted decision-making will play a role in more fields, including finance, healthcare, manufacturing, retail and other industries. EgretSoft's AI intelligent decision-making function, combined with the actual business needs of enterprises, provides more comprehensive and accurate decision support for enterprises. In this era of digital transformation, enterprises should actively embrace AI intelligent decision-making technology as an important means to improve management level and market competitiveness, and achieve more efficient and accurate decision-making management.