AI-Driven Systems & Data Services — Turn Data into Intelligence, Decisions into Results

Core Service Types

3 Core Service Directions for AI & Data

• AI-Driven Business System Development：• Scenarios: Intelligent Quality Inspection, Precision Marketing, AI Customer Service, Supply Chain Forecasting, Energy Optimization• Key Capabilities: - Custom AI Models (computer vision inspection, user portrait recommendation, NLP intelligent dialogue) - Integration with existing systems (ERP/CRM/MES) for seamless "AI decision + business execution"• Case: AI visual inspection system for an auto parts enterprise—replaced manual inspection, 99.2% defect detection rate, 60% higher efficiency, \$120k annual labor cost savings
• End-to-End Data Governance：• Core Stages: Data Collection (multi-source access: IoT devices/business systems/3rd-party data) → Data Cleansing (deduplication/completion/standardization) → Data Modeling (data warehouse/data lake construction) → Data Services (API output/visual dashboards)• Tech Highlights: Supports real-time data processing (Flink/Kafka) and batch processing (Spark) for "real-time available, historically traceable" data• Case: Omnichannel data lake for a retail chain—integrated POS, e-commerce, and member behavior data, 70% higher data processing efficiency, enabling subsequent AI marketing
• AI Model Iteration & System Maintenance：• Services: - Model Optimization: Regular AI model updates (e.g., recommendation algorithm, risk control model) based on business data changes, ensuring model accuracy >90% - System Maintenance: 24/7 AI system monitoring (model performance/data quality/system stability), emergency response, and version upgrade support• Case: AI credit risk control system maintenance for a financial institution—quarterly model iterations, 15% lower non-performing loan rate, 100% model stability compliance

Core Service Advantages

4 Core Guarantees for AI Implementation

• Business-First, No Tech Waste："AI Algorithm Engineer + Industry Consultant" team—first identify business pain points (e.g., "how to reduce inspection costs", "how to boost marketing ROI") then design AI solutions, ensuring tech aligns with business goals;
• Data-AI Integration：End-to-end control from data governance to AI model development, avoiding "poor data quality leading to AI failure" and ensuring "data-model-system" closed-loop;
• Low-Barrier Implementation：Lightweight deployment options (cloud-based AI models, existing system plugins)—no IT architecture reconstruction, 1-3 month implementation cycle;
• Measurable Results：AI value evaluation system—quantify impact from "cost reduction (labor)", "efficiency improvement (processing speed)", "business growth (sales)" to avoid "invisible AI effects"

Service Process

5-Step Standardized Process for AI Success

Business Pain Point Diagnosis → Data & AI Solution Design (data planning + model selection + system architecture) → Data Governance & Model Development → System Deployment & Debugging (integration with existing systems) → Result Verification & Iteration (quantified value report)

Client Success Stories

Industry AI Case Studies

• 👉Manufacturing: AI visual inspection for an electronic component manufacturer—60% higher efficiency, defect miss rate from 5% to 0.8%, \$180k annual cost savings;
• 👉Retail: AI intelligent replenishment for a fresh food chain—predicts stock based on sales, weather, holidays—40% higher inventory turnover, 25% lower stockouts;
• 👉Finance: AI credit risk control for a bank—50% higher automated approval rate, 98% risk control accuracy, approval time from 24h to 10min;
• 👉Healthcare: AI auxiliary diagnosis (lung CT) for a hospital—30% higher diagnosis efficiency, 20% higher early lesion detection rate