Focusing on the cutting-edge trends of industry large models, deeply analyzing the implementation and practice of all-domain artificial intelligence in government and enterprise scenarios
2025 is known as the 'Year of Large Model Industry Landing'. DeepSeek-V3/R1 achieved performance comparable to GPT-4 with extremely low training costs (about $5.57 million), attracting global attention; the open-source Tongyi Qwen3 series has exceeded tens of millions of downloads after being released, and domestic large models have made breakthroughs in inference efficiency and industry knowledge integration. This article discusses how all-domain intelligence moves from single-point applications to full-chain integration in government and enterprise, as well as the three key trends of large models in the industry over the next ten years.
Integrating the four core capabilities of multi-modal large models, intelligent agent clusters, data integration, and intelligent scheduling, JiuTian Technology has launched an AI intelligent central platform that connects the government, enterprises, and the public. The platform uses a 'perception - understanding - decision - action' full-chain architecture, supports private deployment and federated learning, and has been implemented and verified in smart city governance and energy and power intelligent scenarios in many places.
After Anthropic launched MCP (Model Context Protocol) in 2025, the AI intelligent agent ecosystem experienced an explosion. Frameworks like LangGraph, CrewAI, and AutoGen matured rapidly, and multi-agent collaboration moved from experimentation to production. This article shares engineering experience from JiuTian Technology in intelligent agent scheduling architecture: task decomposition uses hierarchical DAG scheduling, conflict resolution introduces a priority arbitration mechanism, and measured scheduling delays are kept under 200ms in scenarios with hundreds of concurrent intelligent agents.
The Zenith-Act large model has significantly improved its inference efficiency and industry knowledge injection capabilities. The new version, based on the MoE (Mixed Expert) architecture, reduces the active parameter count to 37B, triples the inference speed, and improves accuracy in vertical scenarios such as government question answering, energy equipment fault diagnosis, and manufacturing process optimization to over 92%, further consolidating the industry's intelligent foundation.
Urban governance is moving from passive response to proactive prediction. Since 2025, dozens of cities including Beijing, Shanghai, Shenzhen, and Hangzhou in China have integrated large models into the 'One Network Management' platform. Taking Shenzhen as an example, the urban operation command center integrates data from over 1200 perception devices, with the large model analyzing the urban operation situation in real-time, increasing the proactive discovery rate of events to 85%, and shortening the average response time by 60%. This article analyzes the key paths for all-domain AI to connect the entire urban governance chain.
Recently, JiuTian Technology has signed cooperative agreements with many local governments and large enterprises to promote the implementation of all-domain intelligent solutions in areas such as smart city governance, energy and power intelligence, and low-altitude economic supervision. One provincial capital city's 'urban brain' project has completed the first phase delivery, connecting 15 departments and bureaus' business systems, and achieving cross-departmental data integration and intelligent scheduling.
In January 2025, the release of DeepSeek-R1 caused a global shake-up in AI stocks, with NVIDIA's market value evaporating about $589 billion in a single day. However, the deeper impact lies in: DeepSeek has proven that high-quality inference models can be trained at extremely low costs, fundamentally breaking the traditional perception of 'computing power as a barrier'. This article analyzes the profound impact of open-source large models on industry pricing, technical routes, and competitive landscape, as well as the evolution direction of the domestic large model ecosystem.
The API prices of domestic large models in 2025 experienced multiple dives, with some manufacturers' input prices dropping below 1 yuan per million tokens. However, industry research shows that 70% of enterprises face core challenges in large model landing not related to cost, but to data quality, scenario adaptation, and security compliance. This article analyzes the key transitions from 'available' to 'useful' for industry large models from three dimensions: data governance, model fine-tuning, and private deployment.
