AI Use case
LLM Fine Tuning Agent
Application: Webapp, React Frontend
AI Frameworks: CrewAI Agents, RAG Pipeline (Chroma + GCS)
AI Category: Employee Onboarding Automation, Conversational AI
AI Platform: AWS ECS Fargate, AWS ECR, ALB Routing, CloudWatch
Upload Training Dataset
The agent allows teams to securely upload domain-specific datasets (CSV, JSON, or documents) for fine-tuning preparation.
Data Cleaning & Preprocessing
The agent automatically validates, cleans, and tokenizes data, removing duplicates, formatting inconsistencies, and sensitive information.
Data Cleaning & Preprocessing
The agent automatically validates, cleans, and tokenizes data, removing duplicates, formatting inconsistencies, and sensitive information.
Configure Fine-Tuning Parameters
Business teams can define training objectives, choose base models, and set parameters (epochs, learning rate, validation split) through an intuitive interface.
Launch Fine-Tuning Workflow
With one click, the workflow triggers the fine-tuning job on the selected AI platform (Azure Foundry, GCP Vertex AI, or AWS Bedrock).
Launch Fine-Tuning Workflow
With one click, the workflow triggers the fine-tuning job on the selected AI platform (Azure Foundry, GCP Vertex AI, or AWS Bedrock).
Track Training Progress
The dashboard provides live updates on loss metrics, accuracy, and validation results, helping teams identify overfitting and performance trends early.
Evaluate Model Performance
Once fine-tuning completes, the agent generates a model evaluation report with benchmarks, accuracy scores, and comparison against baseline models.
Evaluate Model Performance
Once fine-tuning completes, the agent generates a model evaluation report with benchmarks, accuracy scores, and comparison against baseline models.
Deploy & Integrate the Fine-Tuned Model
The final model can be deployed into production environments and connected with downstream applications (chatbots, workflow agents, or enterprise apps).
