Core Product Value
An Industrial Evidence Intelligence & Knowledge Asset Platform for Manufacturing Enterprises
Focused on Evidence Constraints, Knowledge Accumulation, Controlled Execution, and Closed-Loop Optimization to Solve Core Pain Points in Industrial AI Implementation
(1) Evidence World: Making Data Trustworthy & Traceable
The system progressively understands the client's data landscape, relationships between data points, credibility of evidence, and domain gaps. By connecting multiple data sources, learning schemas, mapping semantics, and managing data coverage, it transforms scattered data from PLC, MES, ERP, WMS, quality inspection, and machine vision systems into analyzable assets. When data is incomplete, it clearly defines the boundaries of conclusions, reducing "AI pretending to know." This ensures every analysis is backed by verifiable evidence, enhancing trust in AI outputs among both management and shop floor personnel.
(2) Know-how Engine: Capitalizing on Expertise
The platform transforms terminology, metrics, rules, analysis playbooks, and experiential cases from experts' minds into searchable, approvable, and reusable knowledge assets. It establishes term cards, metric definitions, judgment rules, and data coverage profiles. This makes the system increasingly knowledgeable about the specific enterprise over time, mitigates the risk of knowledge loss due to consultant turnover, reduces reliance on individual experts, and improves the efficiency of replicating best practices across work cells, production lines, and factories. It converts tacit process knowledge into explicit assets, driving continuous organizational improvement.
(3) Controlled Executor: Ensuring AI Safety & Control
All evidence queries and analysis capabilities are executed within strict boundaries of read-only access, user permissions, row limits, time ranges, and audit trails. Free-form SQL is not exposed, and large language models do not directly operate on the database. By planning evidence paths (Evidence Plan), executing them in a controlled manner (Controlled Executor), and organizing the results into evidence chains (Evidence Bundle), the system ensures that AI interactions with industrial data systems remain safe, controlled, and traceable. Final answers are constructed solely based on verified evidence, while also stating any missing information, conflicts, and suggestions for next steps, thereby minimizing the risk of AI hallucinations.
(4) Scenario Closed-Loop & Continuous Operations: From One-Time Analysis to Long-Term Improvement
Focusing on typical scenarios such as OEE and efficiency analysis, downtime and anomaly analysis, quality and yield analysis, equipment health and predictive maintenance, energy consumption and green manufacturing, and on-site intelligent interaction, the platform forms explainable and verifiable analytical closed loops. By leveraging multi-source evidence, it provides root cause clues, causal candidates, and controlled validation paths to help experts make faster and better decisions. Additionally, it establishes a continuous operation mechanism, using feedback from Q&A, execution results, error correction, and governance dashboards to continuously calibrate and improve the system's capabilities. This transforms one-time analysis into a sustainable long-term improvement capability.
(5) Five Layers of Security Boundaries: Building an Industrial-Grade Trust Mechanism
The platform establishes five critical security boundaries to ensure trust and safety:
1) Data Boundary: Business databases are connected in read-only mode by default; business details are not copied by default.
2) Query Boundary: Natural language questions undergo understanding, constraint, audit, and pass through a security gateway; free-form SQL is not open.
3) Knowledge Boundary: Terminology, Playbooks, rules, and cases require scoring, approval, scope definition, and version governance.
4) Answer Boundary: Answers specify time range, metric definition, evidence source, missing data, and uncertainty.
5) Execution Boundary: Optimization suggestions must first be confirmed by an expert and followed by execution feedback; they are not packaged as unconditional autonomous control.
This comprehensive boundary system balances efficiency gains with the paramount safety and compliance requirements of industrial production.
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