台北市中正區段〇〇光委託:軟體開發需求

網站
後台管理系統
電腦應用程式
電腦遊戲
其他, 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.

其他, 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. ⸻

其他, 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. ⸻

其他, 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. ⸻

其他, 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. ⸻

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.

急件, 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.

五十到一百萬

請專家到指定地點洽談
透過電話或網路進行

沒有

台北市,中正區