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描述： 1. We are building an offline-first AI Digital Doctor Platform — a real-time interactive avatar system that runs entirely on local hardware.
This is not a chatbot project.
This is not a website.
This is a product-level AI system designed to operate without cloud dependency
2. We are building an offline-first AI Digital Doctor Platform — a real-time interactive avatar system that runs entirely on local hardware.
This is not a chatbot project.
This is not a website.
This is a product-level AI system designed to operate without cloud dependency
3. We are building an offline-first AI Digital Doctor Platform — a real-time interactive avatar system that runs entirely on local hardware.
This is not a chatbot project.
This is not a website.
This is a product-level AI system designed to operate without cloud dependency, with future SaaS scalability.
Vision
Create a single-node, fully offline AI system capable of:
• Real-time doctor-avatar interaction
• Low-latency speech-to-speech response
• WebRTC-based video interface (Meet-like UX)
• Modular LLM / STT / TTS architecture
• Local knowledge ingestion & daily sync
• Deployable on high-performance laptops or edge machines
Phase 1 is a commercial MVP.
Phase 2 expands into multi-clinic deployment and enterprise orchestration.
⸻
Technical Scope
We are looking for engineers comfortable with:
• Local LLM inference (7B–13B class models)
• CUDA optimization & VRAM management
• Quantization strategies
• Streaming STT + TTS pipelines
• WebRTC video + data channel integration
• Audio-driven avatar rendering
• Docker-based modular architecture
• Offline RAG and versioned data ingestion
This system must:
• Run fully offline
• Maintain ~1–2s response latency
• Be modular and replaceable at each layer
• Deliver full source code and deployment documentation
⸻
Who We Want
Engineers who:
• Have deployed local inference systems before
• Understand performance bottlenecks
• Can design scalable architecture from day one
• Think in systems, not scripts
This is an opportunity to build a foundational AI platform — not a contract task.
All IP and derivative rights belong to 弛雅有限公司.
⸻
4. We are building an offline-first AI Digital Doctor Platform — a real-time interactive avatar system that runs entirely on local hardware.
This is not a chatbot project.
This is not a website.
This is a product-level AI system designed to operate without cloud dependency, with future SaaS scalability.
Vision
Create a single-node, fully offline AI system capable of:
• Real-time doctor-avatar interaction
• Low-latency speech-to-speech response
• WebRTC-based video interface (Meet-like UX)
• Modular LLM / STT / TTS architecture
• Local knowledge ingestion & daily sync
• Deployable on high-performance laptops or edge machines
Phase 1 is a commercial MVP.
Phase 2 expands into multi-clinic deployment and enterprise orchestration.
⸻
Technical Scope
We are looking for engineers comfortable with:
• Local LLM inference (7B–13B class models)
• CUDA optimization & VRAM management
• Quantization strategies
• Streaming STT + TTS pipelines
• WebRTC video + data channel integration
• Audio-driven avatar rendering
• Docker-based modular architecture
• Offline RAG and versioned data ingestion
This system must:
• Run fully offline
• Maintain ~1–2s response latency
• Be modular and replaceable at each layer
• Deliver full source code and deployment documentation
⸻
Who We Want
Engineers who:
• Have deployed local inference systems before
• Understand performance bottlenecks
• Can design scalable architecture from day one
• Think in systems, not scripts
This is an opportunity to build a foundational AI platform — not a contract task.
All IP and derivative rights belong to 弛雅有限公司.
⸻
5. We are building an offline-first AI Digital Doctor Platform — a real-time interactive avatar system that runs entirely on local hardware.
This is not a chatbot project.
This is not a website.
This is a product-level AI system designed to operate without cloud dependency
6. We are building an offline-first AI Digital Doctor Platform — a real-time interactive avatar system that runs entirely on local hardware.
This is not a chatbot project.
This is not a website.
This is a product-level AI system designed to operate without cloud dependency, with future SaaS scalability.
Vision
Create a single-node, fully offline AI system capable of:
• Real-time doctor-avatar interaction
• Low-latency speech-to-speech response
• WebRTC-based video interface (Meet-like UX)
• Modular LLM / STT / TTS architecture
• Local knowledge ingestion & daily sync
• Deployable on high-performance laptops or edge machines
Phase 1 is a commercial MVP.
Phase 2 expands into multi-clinic deployment and enterprise orchestration.
⸻
Technical Scope
We are looking for engineers comfortable with:
• Local LLM inference (7B–13B class models)
• CUDA optimization & VRAM management
• Quantization strategies
• Streaming STT + TTS pipelines
• WebRTC video + data channel integration
• Audio-driven avatar rendering
• Docker-based modular architecture
• Offline RAG and versioned data ingestion
This system must:
• Run fully offline
• Maintain ~1–2s response latency
• Be modular and replaceable at each layer
• Deliver full source code and deployment documentation
⸻
Who We Want
Engineers who:
• Have deployed local inference systems before
• Understand performance bottlenecks
• Can design scalable architecture from day one
• Think in systems, not scripts
This is an opportunity to build a foundational AI platform — not a contract task.
All IP and derivative rights belong to 弛雅有限公司.
7. We are building an offline-first AI Digital Doctor Platform — a real-time interactive avatar system that runs entirely on local hardware.
This is not a chatbot project.
This is not a website.
This is a product-level AI system designed to operate without cloud dependency, with future SaaS scalability.
Vision
Create a single-node, fully offline AI system capable of:
• Real-time doctor-avatar interaction
• Low-latency speech-to-speech response
• WebRTC-based video interface (Meet-like UX)
• Modular LLM / STT / TTS architecture
• Local knowledge ingestion & daily sync
• Deployable on high-performance laptops or edge machines
Phase 1 is a commercial MVP.
Phase 2 expands into multi-clinic deployment and enterprise orchestration.
⸻
Technical Scope
We are looking for engineers comfortable with:
• Local LLM inference (7B–13B class models)
• CUDA optimization & VRAM management
• Quantization strategies
• Streaming STT + TTS pipelines
• WebRTC video + data channel integration
• Audio-driven avatar rendering
• Docker-based modular architecture
• Offline RAG and versioned data ingestion
This system must:
• Run fully offline
• Maintain ~1–2s response latency
• Be modular and replaceable at each layer
• Deliver full source code and deployment documentation
⸻
Who We Want
Engineers who:
• Have deployed local inference systems before
• Understand performance bottlenecks
• Can design scalable architecture from day one
• Think in systems, not scripts
This is an opportunity to build a foundational AI platform — not a contract task.
All IP and derivative rights belong to 弛雅有限公司.
新北市 板橋區 
組織性質：公司行號 
行銷預算：沒有預算概念 