AI Project Implementation
Project Lifecycle & Technology Applications
On-Premise LLM + RAG System Architecture
Contract Review, HR Policies, Equipment Replacement Assessment, Customer Service Knowledge
Voice Customer Service System Integration
STT, TTS, SIP, VAD, RAG
Cloud-Based AI Healthcare System
STT, LLM, RAG
Power Equipment Failure Prediction System
Time-series analysis across 8 device types and 120 key parameters to predict feeder line failures.
AI Teaching Assistant for Online Education
WebRTC, LLM
Project Experience
Project Scale
System Integration Types
WMS/EAP/ERP/APS/MPS/MDM/SCADA
Project scales
Ranging from teams of 5 to over 120 members.
Integration Technologies
Web API, RFC, WCF, SECS, gRPC and TCP/IP
Project Deployment
On-Premise, Cloud-Native, or Hybrid Cloud
Device Integration
SMT, AOI, DA, WB, MD, Raspberry Pi, etc.
Application Layer Technical Scenarios
LLM Chat
Generated Content and Conversational Summaries
Avatar
Virtual Avatars and Digital Humans
RAG-Powered Customer Service
Automated Responses and Knowledge Base Retrieval
STT/TTS
Speech-to-text and text-to-speech
RAG Knowledge Retrieval
Semantic Search for Large-Scale Document Collections
VLM Image Recognition
Computer Vision and Object Detection
RAG-Based Contract Review
High-Frequency Matching and Rule-Based Comparison
Built on proven deployments to create a scalable AI growth engine.
AI Project Deployment
Cross-Domain Implementation · Proven Results
Healthcare AI Platform
Integrating AI, standardized data, and cloud infrastructure to support multi-site deployment and sustained business scalability.
User Interaction Layer
Core Infrastructure Layer
Cloud Advantages
Intelligent Service Layer
Web Portal, Mobile App, LINE Bot, Telemedicine Platform, Voice IVR, Community Health Navigation Platform
LLM Service Clusters, RAG Knowledge Retrieval Engine, Speech Processing Services, NLP
API Gateway, Authentication & Authorization, FHIR-Compliant Data Lake, Data Storage Layer
Azure provides a comprehensive cloud platform with scalability and enterprise-grade security.
AI Compute Power Dispatch Hub
Through dynamic compute scheduling and multi-model routing, GPU resources and AI models are automatically allocated based on task demands—balancing high-performance processing with operational cost efficiency to ensure scalable and stable enterprise AI operations.
Publicly Listed Electronics Company
aiDAPTIV+ | High-Performance Enterprise AI Architecture
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KV-Cache reuse under large-scale data workloads to significantly reduce inference costs and improve response efficiency.
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Supports large-context processing and high-frequency RAG calls on lower hardware specifications, balancing performance and cost.
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Optimized for enterprise scenarios with massive data volumes, large files, and frequent access requirements.
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Standard Server + Middleware architecture for rapid deployment, stable operation, and enterprise readiness.
