Generative AI Applications with MongoDB

Harness the power of MongoDB Atlas and Amazon Bedrock Foundation Models (FMs) to revolutionise your organisation’s use of Generative AI. Whether you’re developing custom virtual assistants or text generation capabilities to improve employee productivity, our extensive experience in developing Retrieval Augmented Generation (RAG) applications will allow you to leverage your investment in MongoDB.

Key Features:

• Seamless Integration: MongoDB Atlas will be established as the vector store within Knowledge Bases for Amazon Bedrock, allowing for quick and cost-effective improvements to model outputs.
• Managed Workflow: From ingestion to retrieval and prompt augmentation, the entire RAG workflow can be managed without the need for custom integrations or enhanced according to your data flow management requirements.
• Prompt Augmentation: Automatically augment FM prompts with contextual information from your knowledge base.
• Scalable Solutions: RAG applications that scale with your needs, utilising MongoDB Atlas’ powerful vectorSearch index type for high-performance queries.

Benefits:

• Domain-Specific Accuracy: By referencing your organisation’s authoritative knowledge bases, our RAG solutions ensure relevant and accurate responses tailored to your specific domain.
• Cost-Effective: Avoid the expenses associated with retraining models by leveraging RAG to extend FM capabilities to your organisation’s unique context.
• Secure Data Connection: Connect your private data sources to FMs with confidence, knowing that MongoDB Atlas ensures the security and integrity of your data.

This offering provides a comprehensive solution for businesses looking to enhance their AI models with domain-specific knowledge, ensuring accurate and useful outputs for various applications.

Get started today and leverage the potential of Generative AI. Upon placing an order, our Client Engagement team will contact you. You will receive an email confirming your order, identifying prerequisites, attendees, and a proposed schedule for initial discovery workshops.