Customersuccess stories

Better inventory positioning. Higher turnover velocity. More precise decision-making.

AI AgentCase Study

Client: FortuneTechgroup

No unified standard - risk assessment relies on individual experience. No structured, quantifiable rules.
Siloed data, bogged down by manual data work- data is siloed across systems. Excessive time spent on manual gathering and calculation -instead of strategic sourcing.
Procurement findings trapped- analysis and cross-functional discussions stay in chat logs and personal notes. No reuse.

Automated Intelligence Preparation - automatically harmonizes inventory, demand forecasts, and purchase orders. Eliminates manual reconciliation and data cleansing.
Expert-Level Decision Support - applies predictive models and business guardrails to identify sourcing risks. Delivers explainable, auditable approval decisions - not black-box answers.
Continuous Learning Loop-every employee interaction refines the rule logic and retrains the model. The system gets smarter with each exception handled and every decision reviewed.

30%-50% Sourcing Decision Precision
Minimizes procurement mismatches (overbuying / underbuying)
20%-30% Capital Efficiency
Accelerates inventory turnover velocity, lowers cash tied up in excess inventory

Beyond understanding: Enabling autonomous business through executable intelligence.

Client: SITC International Holdings SITC Logo

Data Silos & Semantic Fragmentation: Inconsistent data standards across multiple systems and a lack of a unified logic framework.
Decision Latency: Heavy reliance on manual queries and expert analysis leads to delayed responses.
Rigid Reporting: Static analysis tools lack the flexibility required for complex, real-time business scenarios.
Hidden Losses: Inability to identify risks in a timely manner, resulting in lost opportunities and increased costs.

Core Engine: A USL Semantic Engine that serves as the "brain", unifying business logic mapping and modeling tools.
Intelligent Interaction: Supports three proactive modes: Active Inquiry (Natural Language Processing), Real-time Insights (Agent-led monitoring), and Active Alerting (Smart push notifications).
End-to-End Workflow: A complete pipeline including Multi-source Data Integration → Deep Insight Generation → AI-driven Recommendations → Actionable Tasks → Human-Machine Interaction.

Faster Decision-Making: Boosts decision speed by 70%+, capturing market opportunities faster.
Revenue Growth: Enables precise performance improvements, uncovering 3%–8% in potential revenue.
Risk Mitigation: Provides early warnings for 70%+ of abnormal events, significantly reducing losses.
Customer Retention: Reduces lead attrition by 10%+, enhancing overall customer value.
Operational Efficiency: Achieves cost reduction and efficiency gains, with a 50%+ increase in labor productivity.

Continuous production. Full-takt operation. Intelligent MES delivers determinism.

Client: CATL (Battery manufacturing company) CATL Logo

Data & stability: 2B record limit causes crashes. Zero downtime tolerance in battery manufacturing.
Global orchestration: Fragmented operations across regions, no unified control.
Architecture rigidity: Monolithic systems cannot scale or evolve fast enough.

Rebuilt legacy MES into an Industrial Intelligent Management platform using composable architecture.