We have moved beyond Digital Transformation (DX) and into the era of AI Transformation (AX). However, data security regulations, IT infrastructures, and budgets vary vastly from enterprise to enterprise.
VWAY does not enforce a "one-size-fits-all" solution. We propose two AI deployment models tailored to your specific environment: "On-Premise" and "API Integration." Whether engineering data security is paramount or cost-efficiency is the priority, VWAY has the answer.
Type 1. On-Premise AI
"Zero Data Leakage, Specialized AI for Your Company Alone"
This is a solution tailored for industries where security is critical, such as defense, automotive, and semiconductors. We build a customer-specific sLLM (small Large Language Model) based on proven open-source LLMs on internal servers that are completely isolated from external networks.
1. Core Technology: Domain-Specific Fine-tuning
General AI cannot understand your company's complex "requirements specifications" or "legacy code." VWAY bases its solutions on performance-proven open-source models and trains (fine-tunes) them using the customer's engineering data. Through this, we create AI that delivers expert-level performance for specific purposes, such as Safety Analysis, requirements verification, test case generation, and code reviews.
2. Accuracy Enhancement: RAG (Retrieval-Augmented Generation) System
AI "hallucinations," where false information is presented as fact, are critical failures in engineering. To address this, we have implemented RAG (Retrieval-Augmented Generation) architecture.
Operating Principle: Before generating an answer, the AI first searches (Retrieves) relevant manuals or regulations from your in-house technical document repository (Vector DB) and generates answers based on this information.
Effect: It prevents baseless fabrication and provides exact sources, such as "According to page 34 of the reference document..."
Reference Tech: https://www.nvidia.com/en-us/glossary/retrieval-augmented-generation/
Type 2. API Integration AI
"Maximizing Existing Resources, The Fastest and Most Efficient Functional Expansion"
This model is designed for companies that have already introduced or are currently using high-performance AI models like GPT-4, Azure OpenAI, or Claude internally. Instead of building a new model, we connect VWAY's "Engineering Prompt Module" to your existing AI via API to immediately implement necessary functions.
(Image Placeholder: API Integration Conceptual Diagram)
1. Balance of Cost Reduction and High Performance
Utilize your existing AI infrastructure as-is, without model training or server setup costs (CAPEX). VWAY provides optimized prompt engineering and logic in API form to best fulfill your requirements (SRS verification, automated testing, etc.).
2. Speed and Efficiency Optimization Technology: Semantic Caching
To drastically reduce "latency" and "token costs," which are downsides of the API integration model, we apply Semantic Caching technology.
We have moved beyond Digital Transformation (DX) and into the era of AI Transformation (AX). However, data security regulations, IT infrastructures, and budgets vary vastly from enterprise to enterprise.
VWAY does not enforce a "one-size-fits-all" solution. We propose two AI deployment models tailored to your specific environment: "On-Premise" and "API Integration." Whether engineering data security is paramount or cost-efficiency is the priority, VWAY has the answer.
Type 1. On-Premise AI
"Zero Data Leakage, Specialized AI for Your Company Alone"
This is a solution tailored for industries where security is critical, such as defense, automotive, and semiconductors. We build a customer-specific sLLM (small Large Language Model) based on proven open-source LLMs on internal servers that are completely isolated from external networks.
1. Core Technology: Domain-Specific Fine-tuning
General AI cannot understand your company's complex "requirements specifications" or "legacy code." VWAY bases its solutions on performance-proven open-source models and trains (fine-tunes) them using the customer's engineering data. Through this, we create AI that delivers expert-level performance for specific purposes, such as Safety Analysis, requirements verification, test case generation, and code reviews.
2. Accuracy Enhancement: RAG (Retrieval-Augmented Generation) System
AI "hallucinations," where false information is presented as fact, are critical failures in engineering. To address this, we have implemented RAG (Retrieval-Augmented Generation) architecture.
Operating Principle: Before generating an answer, the AI first searches (Retrieves) relevant manuals or regulations from your in-house technical document repository (Vector DB) and generates answers based on this information.
Effect: It prevents baseless fabrication and provides exact sources, such as "According to page 34 of the reference document..."
Reference Tech: https://www.nvidia.com/en-us/glossary/retrieval-augmented-generation/
Type 2. API Integration AI
"Maximizing Existing Resources, The Fastest and Most Efficient Functional Expansion"
This model is designed for companies that have already introduced or are currently using high-performance AI models like GPT-4, Azure OpenAI, or Claude internally. Instead of building a new model, we connect VWAY's "Engineering Prompt Module" to your existing AI via API to immediately implement necessary functions.
(Image Placeholder: API Integration Conceptual Diagram)
1. Balance of Cost Reduction and High Performance
Utilize your existing AI infrastructure as-is, without model training or server setup costs (CAPEX). VWAY provides optimized prompt engineering and logic in API form to best fulfill your requirements (SRS verification, automated testing, etc.).
2. Speed and Efficiency Optimization Technology: Semantic Caching
To drastically reduce "latency" and "token costs," which are downsides of the API integration model, we apply Semantic Caching technology.
Tech Introduction: User queries are converted into vectors and stored. If a question like "Classify the ASIL grade according to ISO 26262" comes in, and a similar (semantic) question and answer exist in the history, the answer is immediately returned from cash memory without bypassing the AI.
Effect: Response speed increases dramatically, and unnecessary API call costs are significantly reduced.
Reference Tech: https://learn.microsoft.com/en-us/azure/ai-foundry/openai/concepts/prompt-engineering?view=foundry-classic