What is Foundation Model Selection & Fine Tuning?
Foundation Model Selection & Fine Tuning is the process of identifying and customizing large language models (LLMs) to meet the specific requirements of SAP environments. It involves evaluating different model providers, preparing SAP-specific data, fine-tuning models for domain relevance, and ensuring smooth integration within SAP workflows. This approach enhances accuracy, ensures contextual understanding, and supports efficient automation across business operations.
Why Foundation Model Selection & Fine Tuning?
Standard LLMs often lack contextual understanding of SAP-specific tasks and terminologies. By carefully selecting and fine-tuning models with SAP business data, organizations can improve performance in tasks such as document processing, financial entries, and operational assistance. This approach supports better accuracy, compliance, and operational efficiency while enabling SAP-native AI integration across modules.
Our Foundation Model Selection & Fine Tuning Services:
Model Landscape Analysis
Evaluate leading language models to identify the best-fit architecture tailored to SAP enterprise needs.
Assess each model’s effectiveness in handling real-world SAP use cases to ensure relevant and reliable results.
Choose models that align with operational priorities, balancing speed, legal constraints, and budget.
Data Preparation & Feature Engineering
Prepare SAP business data through normalization and tokenization to ensure compatibility with LLMs.
Generate context-rich embeddings to help models grasp the domain-specific language and workflows of SAP.
Ensure underrepresented yet critical SAP processes are adequately modeled for accurate predictions.
Advanced Fine Tuning
Use lightweight fine-tuning methods that adapt models specifically to SAP business logic and processes.
Train models progressively using increasing complexity, improving domain comprehension over time.
Control overfitting by applying selective data emphasis, preserving model generalization in SAP tasks.
Evaluation & Production Readiness
Test models against real SAP data to confirm consistent and unbiased performance across functions.
Refine models to be lighter and faster without compromising accuracy, ensuring production efficiency.
Simulate real-use scenarios by integrating and testing models in SAP environments before go-live.
Monitoring & Continuous Improvement
Visualize key metrics post-deployment for proactive performance tracking and management.
Detect shifts in model performance over time and trigger retraining to maintain relevance.
Use real-time user inputs to continually enhance model behavior and contextual alignment.
Security & Compliance Safeguards
Ensure data access rules and privacy regulations are upheld throughout the AI lifecycle.
Track data usage and model outputs for auditability and transparency.
Adhere to SAP’s compliance requirements to support secure and trustworthy AI deployments.