The 2026 World Artificial Intelligence Conference (WAIC) and the High-Level Meeting on Global AI Governance are currently taking place in Shanghai from July 17th to 20th.

During the event, WENGE AI introduced the industry's first complete AI decision-making product system, structured around the five pillars of "Foundation, Hub, Core, Brain, and Edge." This system is built upon the DOMA architecture as its technical foundation, covering the entire chain from data governance and business modeling to model inference and agent execution.

WENGE AI is the industry's first provider to fully integrate the four-layer chain of "data-model-agent-decision" and achieve large-scale implementation of a complete AI decision-making framework.

According to data from CIC, WENGE AI ranked first in China's enterprise-level large model-driven decision intelligence market in 2024, holding a market share of 11.4%.

The system can be summarized by its five components: the TokSea Token platform for unified measurement, scheduling, and governance of computing power, models, and intelligent assets; the DIP ontology data platform, which transforms fragmented data into business-ontology "digital twins" understandable by large models; the ScienceOne and YaYi engines for scientific and general-purpose applications; the Decitron decision engine focused on deep analysis, simulation, and solution comparison; and the Claworks platform for injecting intelligence precisely into frontline roles and business processes.

These capabilities are supported by the underlying DOMA architecture, progressing step-by-step from data governance and ontology modeling to model inference and agent execution, ultimately leading to actionable decisions.

At the launch event, the CEO of WENGE AI, Luo Yin, demonstrated the system's ability to support decision-making at various corporate levels.

For micro-level daily production, using a knitting and dyeing enterprise as an example, the system can detect equipment anomalies in real-time, trace causes through order, machine, and process relationships, predict production progress, compare equipment efficiency, and integrate maintenance, notification, and scheduling instructions into the execution workflow.

At the meso-level of business operations, the system integrates multiple data sources and over 200 business relationships, compressing operational analysis that previously took 2 to 3 days down to under 10 minutes.

For macro-level strategic decisions, the system combines internal operational data with external signals from policy, market, industry, and public sentiment to conduct scenario simulations, path comparisons, and risk assessments for issues like market entry, capacity planning, major investments, and strategic adjustments.

At the industrial R&D level, the ScienceOne engine further supports scientific data comprehension, experimental design, and solution optimization, shifting R&D in fields like materials science from trial-and-error based on experience to a model driven by data, knowledge, and models working in concert.

The WENGE AI decision AI product suite can reduce enterprise intelligence construction and operational costs by up to 90% or more and increase decision response speed by up to 192 times.

To date, WENGE AI has served over 1,000 enterprises globally, covering key areas such as data intelligence, operational intelligence, industrial intelligence, and strategic intelligence.

In enterprise decision-making scenarios, the WENGE AI decision intelligence system has enabled companies to reduce construction and operational costs by up to 90% and improve decision response speed by up to 192 times, completing analyses that previously took an average of 32 hours in just 10 minutes.