Ecosystem R&D

AI Value Chain

Model Size Considerations: Evaluating and optimizing model sizes for specific use cases, balancing performance and resource requirements.

Pre-Training Costs: Analyzing and managing costs associated with initial model training to ensure cost-effective AI development.

Fine-Tuning vs. PFT: Guiding decisions between fine-tuning existing models and parameter-efficient fine-tuning (PFT) techniques, optimizing for performance and efficiency.

Token-based Costs: Providing strategies for managing and optimizing token-based costs in language models, enhancing cost-effectiveness in AI operations.

Hosting Options: Offering expertise in selecting and implementing suitable hosting solutions for AI models, considering factors like scalability and performance.

SaaS vs. On-Premise: Assisting in evaluating and choosing between Software-as-a-Service and on-premise deployment options, aligning with organizational needs and constraints.

AI Development

Use Case Definition: Clearly defining and refining AI use cases, ensuring alignment with business objectives.

Model Selection: Guiding the process of choosing appropriate AI models based on use case requirements and constraints.

Pre-Training: Supporting efficient pre-training processes, leveraging industry best practices and cutting-edge techniques.

Tuning: Offering expertise in model tuning, optimizing performance for specific applications and datasets.

Inferencing: Helping in designing and implementing efficient inferencing strategies, balancing speed and accuracy.

Hosting: Providing guidance on hosting solutions that meet performance, scalability, and security requirements.

Deployment: Assisting in smooth and effective deployment of AI models into production environments.