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The golden age of AI models,
Too many problems
Large Language Models (LLMs), Multimodal Language Models (MMLMs), and Domain-Specific Small Models (SLMs) are at the heart of AI innovation. Many companies are adopting these models for a variety of tasks, including customer response automation, document summarization, and decision support. However, they face numerous challenges in practical implementation. Key examples include difficulties in model selection, performance evaluation and optimization issues, and the construction of RAGs.
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Challenge
Key challenges
DIA NEXUS focuses on.
Difficulty in choosing a model that suits your purpose
Companies need to select the optimal AI model based on their tasks, budget, and infrastructure. However, the variety of model types (LLM, SLM, MMLM) and features makes it difficult to determine the optimal model.
It's difficult to establish criteria when considering various factors such as performance vs. cost, cloud vs. on-premises, and versatility vs. domain specificity.
Performance issues
in LLM/RAG deployment.
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Growing adoption of RAG architectures to leverage enterprise data and external knowledge
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Emerging challenges across multiple areas, including data preparation, retrieval quality, indexing, and performance optimization
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The need to improve RAG system performance through continuous research and development as well as ongoing technology investment
Difficulties in performance evaluation and optimization
After model selection, objective performance evaluation and optimization are crucial, but it is not easy to systematize the evaluation of unique data scenarios within a company.
Achieving maximum performance within budget/hardware constraints requires engineering skills and know-how, but few companies have this internalized.
Service
Optimization services
DIA NEXUS focuses on.
Enterprise-tailored model proposals
By thoroughly analyzing enterprise objectives and use cases, we begin by establishing an optimal model strategy. We then provide detailed guidance on the strengths, limitations, and cost structures of various model types—including LLMs, SLMs, and MMLMs—and support enterprise-specific model selection by proposing domain-specialized pre-trained models and fine-tuning use cases.
Performance evaluation and benchmarking
We establish a performance evaluation framework, support the design of customized evaluation metrics and the implementation of automated evaluation systems, validate models through simulations of core enterprise business scenarios, and provide guidance on model performance monitoring and improvement.
AI infrastructure design
We support hardware specification sizing and GPU cluster configuration, apply model serving optimization techniques, build and optimize RAG architectures, optimize vector databases and embedding models, construct data pipelines, and optimize prompt design.