expert
system
Digital Masters and Consultants — transforming experience into digital wisdom, continuously refined.
Digital Specialist
How does SYNO Compute implement an expert system?
AI today shows remarkable capabilities in programming, mathematical engineering, and image processing. At SYNO Compute, we're taking it a step further — developing AI that can learn and adapt to specialized domain knowledge.
Infrastructure
Leveraging SYNO Compute’s extensive expertise in heterogeneous communication integration, we build high-quality infrastructure capable of both data acquisition and device control. The system allows flexible adjustment of data granularity, establishing a reliable and high-quality data source.
Rule-Based Knowledge Formation
With extensive cross-domain implementation experience, the SYNO Compute team collaborates closely with field personnel and veteran experts to develop operational logic that can be understood and utilized by AI systems.
Data Collection
The AI system, built on established operational logic, is deployed onsite to work alongside field staff. Through practical collaboration, the system extracts the conclusion-oriented data necessary for model development.
Model Development
Extracted data is pushed to a high-performance computing system for parallel simulation and model construction. Using a data simulation system for validation, the final distilled model is redeployed to the edge.
Edge AI in Operation
The deployed AI model operates at the edge, dynamically adjusting control parameters based on real-time data to achieve optimized performance and control precision.
The Core Benefits of Expert Systems
Unlike traditional automation systems, expert systems are capable of handling complex and ambiguous situations, providing concrete solutions and recommendations—just like a seasoned expert consultant. They require no expensive consulting fees and are free from human fatigue or judgment bias.
Capturing and Sustaining Critical Know-How
By digitizing the extensive experience of senior experts, we prevent the loss of critical knowledge caused by staff turnover—ensuring that invaluable expertise is preserved forever and can be leveraged across the organization.
Accelerate and Improve Decision-Making
Transform hours or even days of analysis and decision-making into responses within seconds — all while preserving expert-level accuracy and quality.
Cut Operational Expenses
By reducing manual analysis time at startup, minimizing decision-making errors, and optimizing resource allocation, typical use cases can achieve 15–40% in cost savings.
Establish Standardized Best Practices
Ensure organizational alignment in decision-making standards, minimizing inconsistencies resulting from personal variation and subjective interpretation.
Mitigate Risk and Human Error
By reducing human error and judgment bias, critical decisions can be made with full consideration of all relevant factors — significantly lowering compliance risks and operational mistakes.
Continuous Adaptation and Optimization
By continuously learning from new data and results, the system refines its decision-making model over time — ensuring ever-improving accuracy and performance.
Implementation Examples and Business Impact
Knowledge Transfer from Seasoned Professionals
A screw manufacturing factory has seen a gradual decline in the number of veteran craftsmen, resulting in the inability to fully operate all production lines. In addition, the modern market demands small-batch, high-variety orders, which has further impacted the company’s profitability. After implementing an expert system, the factory is now able to generate optimized control parameters tailored to different design drawings, significantly reducing setup time during work order changes and machine startups.
Energy optimization for smart buildings
A building’s chiller system previously relied solely on a traditional control system. By implementing a chiller expert system for energy optimization, it now dynamically adjusts HVAC parameters based on factors such as weather forecasts, outdoor temperature and humidity, indoor usage patterns, and the chiller’s energy consumption baseline. This ensures human comfort while reducing energy consumption. The system can also predict and notify maintenance requirements for the chiller in advance.
