Custom AI LLM for an Affiliate Marketing Company
Challenge
Our client realized that public AI tools didn’t do much due to data privacy risks and limited customization. Without automation, repetitive tasks like legal review, data summarization, and PowerPoint generation delayed the clients' internal processes.
They needed a custom solution to regain full control over sensitive data and customizations, along with easy onboarding for their non-technical staff, who required a simple way to access AI capabilities.
Our client realized that public AI tools didn’t do much due to data privacy risks and limited customization. Without automation, repetitive tasks like legal review, data summarization, and PowerPoint generation delayed the clients' internal processes.
They needed a custom solution to regain full control over sensitive data and customizations, along with easy onboarding for their non-technical staff, who required a simple way to access AI capabilities.
Solution
Our Approach
1. LLM Hosting & Fine-Tuning on AWS
We deployed and fine-tuned Meta’s Llama 3.1 8B model using AWS SageMaker to allow AP to host their own large language model. Each client instance has its own tailored model for personalized context windows and interaction clarity.
The fine-tuning process was automated via:
- SQS-triggered Lambda functions to pull datasets from S3 and initiate SageMaker training.
- CloudWatch monitoring to detect job completion and trigger model deployment to AWS Bedrock.
2. RAG Integration & Prompt Engineering
To deliver precise and context-rich outputs, we implemented Retrieval-Augmented Generation. This allowed the model to pull data from external sources and adapt responses accordingly—ideal for contract analysis, financial summaries, and content automation.
3. OpenWebUI for Mini GPTs and Workflow Automation
We installed OpenWebUI, making it easy for non-technical users to engage with the AI.
Key features include:
- Pre-built mini GPTs for specific tasks (e.g., financial insight extraction, legal flagging).
- Pipelines that trigger custom scripts or automate AI interactions within larger workflows.
Our Approach
1. LLM Hosting & Fine-Tuning on AWS
We deployed and fine-tuned Meta’s Llama 3.1 8B model using AWS SageMaker to allow AP to host their own large language model. Each client instance has its own tailored model for personalized context windows and interaction clarity.
The fine-tuning process was automated via:
- SQS-triggered Lambda functions to pull datasets from S3 and initiate SageMaker training.
- CloudWatch monitoring to detect job completion and trigger model deployment to AWS Bedrock.
2. RAG Integration & Prompt Engineering
To deliver precise and context-rich outputs, we implemented Retrieval-Augmented Generation. This allowed the model to pull data from external sources and adapt responses accordingly—ideal for contract analysis, financial summaries, and content automation.
3. OpenWebUI for Mini GPTs and Workflow Automation
We installed OpenWebUI, making it easy for non-technical users to engage with the AI.
Key features include:
- Pre-built mini GPTs for specific tasks (e.g., financial insight extraction, legal flagging).
- Pipelines that trigger custom scripts or automate AI interactions within larger workflows.
Results
While the project is still evolving, our client has already seen substantial benefits:
- Data Security & Ownership: A self-hosted AI setup ensures complete control over sensitive client data and model behavior.
- Time Savings Across Teams: Teams now generate custom emails, analyze reports, and summarize legal documents in minutes—not hours.
- UX for Everyone: OpenWebUI empowers non-technical users to interact with advanced AI in a simple and intuitive way.
- Scalable AI Foundation: Each client-specific model is ready to scale as new workflows are identified, paving the way for even deeper automation and personalization.
While the project is still evolving, our client has already seen substantial benefits:
- Data Security & Ownership: A self-hosted AI setup ensures complete control over sensitive client data and model behavior.
- Time Savings Across Teams: Teams now generate custom emails, analyze reports, and summarize legal documents in minutes—not hours.
- UX for Everyone: OpenWebUI empowers non-technical users to interact with advanced AI in a simple and intuitive way.
- Scalable AI Foundation: Each client-specific model is ready to scale as new workflows are identified, paving the way for even deeper automation and personalization.