Gen AI Tools & Architecture
Generative AI is a field of active research and development. LLMs, RAG architectures, and related tools are new and rapidly changing. The tools you use are almost as important as the problems you choose to solve. Not only for managing accuracy but because the value you create might be overcome by the cost of the solution. Understanding how to leverage your current investments in ML, data, and cloud against tradeoffs in performance is key. The choices are complex, but Ippon has pulled the leading options in the market together into accelerators that help you make the right choice for your business and project. Using our project accelerators, we can lay out your options and help make the selections that best suit your organizational needs.
Challenges
- Constantly Evolving Technology: Generative AI tools, large language models (LLMs), and RAG architectures are advancing rapidly, making it difficult for organizations to keep pace with new developments.
- Performance vs. Cost Tradeoffs: Finding the right balance between model performance and operational costs can be challenging.
- Customizing Large Language Models (LLMs): Implementing and fine-tuning LLMs for specific use cases requires deep technical expertise.
- Integration of Generative AI with Existing Systems: Organizations struggle to integrate new generative AI capabilities with their existing workflows and data pipelines.
Benefits
Expert Tool Selection
Receive expert guidance in choosing generative AI tools that align with your business use cases, platforms, budget, and performance goals.
Optimized Architecture Design
Benefit from scalable and efficient AI architectures that support your data volume, model complexity, and integration requirements.
Performance & Resource Optimization
Using advanced techniques like model compression, quantization, and hardware acceleration, we optimize the performance of your AI models while allocating computational resources efficiently to minimize costs.
Cost Management Strategies
We leverage cloud-based infrastructure and perform cost analyses to manage expenses across different LLMs and resource usage, ensuring long-term sustainability without compromising performance.
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