RAG Manager

RAG-Manager is a solution that enhances the accuracy and reliability of generative AI by efficiently utilizing a company’s internal knowledge. It reduces hallucination issues and builds RAG pipelines optimized for specific domains, making it applicable in various business areas such as internal chatbots, contract management, and workflow automation. Delivered through AWS infrastructure, it ensures a higher level of performance and security. Continuous performance monitoring, quality assurance through testbeds, and secure infrastructure provisioning within the customer’s account further guarantee complete security.

RAG-Manager supports companies in efficiently utilizing their internal knowledge to enable generative AI to produce more accurate and trustworthy results. By leveraging AWS Bedrock, RAG-Manager supports various pre-trained AI models, allowing companies to choose the best model suited for their domain. It constructs RAG pipelines optimized for the domain knowledge held by the company or department, ensuring that the AI references internal data first when generating information. This flexibility in model selection reduces misinformation or hallucination issues that commonly occur with existing generative AI, paving the way for more effective and reliable utilization of a company’s knowledge assets.

The core of RAG-Manager lies in managing and evaluating data. Beyond simply using generative AI models, it continuously monitors data accuracy and incorporates new knowledge through a testbed to ensure it is reflected in the model. Leveraging AWS Bedrock, RAG-Manager benefits from robust data security and regulatory compliance features, ensuring that sensitive data is handled safely and in accordance with industry standards. This approach enables the creation of optimal RAG pipelines and improves the quality of domain-specific AI services, while maintaining the highest levels of security and compliance.

RAG-Manager is a managed service provided by BSG, where BSG handles the management of application versions, infrastructure stability, and security. It also offers TestBed environments tailored to the specific needs of each customer. Since all processes are managed by BSG, customers can not only achieve better RAG performance through BSG but also request cost and technical support. Additionally, in cases where sensitive customer information is involved, custom service deployments can be provided upon request.

RAG-Manager provides continuous monitoring of system performance, security, and data integrity through logging and anomaly detection features, delivered through AWS infrastructure. This enables the early detection of potential issues, ensuring that the AI system consistently produces accurate and reliable results. The use of AWS infrastructure enhances the scalability and reliability of these monitoring capabilities, playing a crucial role in maintaining regulatory compliance and system stability. Additionally, RAG-Manager offers regular reports on system status, usage trend analysis, and security threat detection, helping customers take proactive measures with the added benefit of AWS’s robust security features.

Cost optimization tailored to customer needs is also a key advantage of RAG-Manager. For example, if a customer needs to reduce AI resource consumption at a specific time or temporarily lower system load, BSG can propose and implement customized resource management and deployment strategies for cost optimization. This cost management functionality helps ensure efficient operations whenever required, maximizing resource utilization while minimizing cost burdens.

Existing large language models (LLMs) demonstrate powerful generative capabilities through diverse text learning, but several issues arise when deploying these models for business purposes

• HallucinationThe AI generates information that does not exist or provides incorrect answers based on inaccurate data, which can be risky in business situations requiring critical decision-making.
• Provision of Incorrect InformationAmbiguous data sources or outdated information can lead to erroneous conclusions.
• Non-optimized PerformanceUsing RAG methods that do not align with a company's domain and usage can degrade search performance or produce inaccurate results.
• Performance Degradation with New Data/UpdatesExisting RAG pipelines using LLMs may not efficiently handle the addition or updating of new data, causing performance drops when incorporating the latest information.

These issues can be addressed with RAG-Manager. By utilizing verified internal knowledge and constructing optimized pipelines for specific purposes, RAG-Manager minimizes the risk of AI generating incorrect information and delivers the best performance tailored to each company's needs.

Internal Chatbot Service

RAG-Manager is used to provide optimized chatbot services within the company. Through RAG pipelines that are continuously updated with the latest data managed by each department, the chatbot delivers accurate and reliable answers. RAG-Manager manages the quality of chatbot responses through periodic evaluations and automatic generation of result reports.

Contract Management Service

In automated systems that review and summarize contract changes using LLM, RAG-Manager creates an optimal environment for evaluating the accuracy of contract content and compliance. By comparing company regulations with each contract clause, RAG-Manager continuously learns and evaluates various contract formats and data, providing optimized RAG pipelines.

Customer Support Service

In customer service, RAG-Manager provides optimized answers to frequently asked questions (FAQs) and other repetitive issues. It regularly updates the data used by customer support AI and adjusts the RAG pipeline to meet the latest customer service demands, maintaining high-quality responses for customers.

• Improved Accuracy: It reduces errors when generative AI relies on external data by utilizing internal knowledge, providing more trustworthy answers.
• Continuous Performance Monitoring and Optimization: RAG-Manager, delivered through AWS infrastructure, continuously monitors AI performance and tests new knowledge additions to optimize performance, ensuring high availability, accuracy, and functionality over time.
• Business-Specific Features: It offers tailored functions for various domains such as contract management and internal chatbots, supporting unique business processes while leveraging AWS Bedrock for diverse generative AI model optimization.
• Quality Assurance via Testbeds: New data or features are verified through testbeds, preventing unexpected issues and ensuring stability and performance.
• Secure and Compliant Operations: With AWS Bedrock’s data security and regulatory compliance features, RAG-Manager ensures safe and secure usage, adhering to industry standards.

BSG provides solutions for all information-related needs, from strategy development to system implementation and operation. With the increasing demand for transitioning from on-premises solutions to cloud environments, significant time is spent on staffing and onboarding, leading to business opportunity costs. To address this, BSG offers an environment where cloud-based solutions can be quickly validated and operated, ensuring swift onboarding through AWS Marketplace.

BSG operates in an AWS-optimized cloud-native manner, provisioning our managed services to customer infrastructures quickly through AWS IaC (Infrastructure as Code) while ensuring high availability. Additionally, BSG plans to leverage AWS Marketplace to support metering and billing functions.

RAG-Manager goes beyond simple application delivery; it is designed to ensure company data is used safely and effectively. The infrastructure is provisioned on AWS, with all environments built within the customer’s account, ensuring complete data security while providing optimized RAG quality based on internal company knowledge. The use of AWS guarantees that security is maintained throughout the process.

This approach guarantees reliable AI responses and continuous performance improvement tailored to business needs. RAG-Manager leverages AWS Bedrock to provide an optimized environment for utilizing various generative AI models, allowing companies to protect their data while continuously maximizing AI accuracy and performance. Additionally, the security and compliance features of AWS Bedrock ensure safe and trustworthy operations.

BSG manages customized infrastructure deployment, performance monitoring, and security upgrades, allowing customers to experience optimal RAG performance with minimal operational overhead. In this way, RAG-Manager offers distinct advantages as a managed service, contributing to the achievement of the customer’s business goals.

If you seek reliable and sustainable AI-driven business innovation, choose RAG-Manager.