LLM Engineering & Fine-Tuning
Why LLM Engineering & Fine-Tuning Matters Now
The AI market is shifting from large, generic models to smaller, specialized ones that deliver superior performance at lower cost. Generic LLM APIs cannot handle domain-specific, mission-critical tasks reliably without expert customization. Encora bridges this gap by fine-tuning models specifically for your business needs and operational requirements.
From Challenge to Competitive Advantage
Catastrophic Forgetting
Fine-tuning causes models to lose pre-trained knowledge during customization, resulting in degraded performance on general tasks while optimizing for specific use cases.
Data Quality Requirements
Effective model training requires high-quality curated datasets that are properly labeled, representative, and free from bias or inconsistencies.
Prohibitive Computational Costs
Fine-tuning demands expensive GPU resources and extensive compute time, making it financially unfeasible for many organizations without proper infrastructure.
Model Overfitting
Models perform poorly on new data outside the training set, limiting real-world applicability and creating unreliable AI systems for production environments.
What AI Business Model Redesign
can do for you
Strategic Capabilities and Expertise
Define specific business applications and select optimal base models for fine-tuning based on your requirements and performance objectives.
Build high-quality, curated datasets from proprietary data sources with proper labeling, cleaning, and bias mitigation processes.
Implement cost-effective fine-tuning techniques to customize model behavior while preserving computational efficiency and reducing training costs.
Align model outputs with human preferences and organizational values through advanced training methodologies and feedback optimization.
Comprehensive testing and validation to ensure model safety, performance, and reliability across diverse scenarios and edge cases.
Ongoing monitoring and improvement of deployed models to maintain performance standards and adapt to evolving business needs.
Deploy fine-tuned models with optimized performance and cost efficiency for your specific infrastructure and scaling requirements.