Internal Knowledge Search System-enabled Large Language Model Implementation Support Service - ABeam LLM Partner

With the advent of ChatGPT (OpenAI), the way companies utilize AI has changed dramatically. In addition to having AI replace specific, regular tasks as in the past, the introduction of large language models (LLMs) is being promoted in order to have AI assist with non-regular tasks as well. However, while large language models are effective for general-purpose tasks such as translation and summarization, they are not well-suited to tasks that take into account internal circumstances and, as a result, are not being used to their full potential. 
ABeam Consulting uses an LLM system (ABeam LLM Partner) based on technology that enhances search-based generation functions (RAG: Retrieval-Augmented Generation) to build an AI that can execute tasks taking into account internal circumstances. This promotes the efficient use of internal documents for business purposes, and contributes to improving the productivity of the entire organization.

Typically, LLM learning data does not include the internal information of a specific organization or industry, making it impossible to generate correct responses to questions related to specific domains, such as internal company documents.

As a result, in use cases like those mentioned above, appropriate responses cannot be provided, and sufficient implementation effects are not being achieved.

The ABeam LLM Partner system is an LLM system that generates responses after inputting the information required for the operation of each department. Unlike a conventional LLM system, each department can build a custom database to suit their own needs and use the optimal large language model environment, allowing quick and efficient specialized information access, text generation, and knowledge sharing. The system can also handle tasks such as question answering, automatic summarization, and text generation, contributing to improving productivity throughout the organization.

This sales assistant system extracts similar cases from past proposal materials and generates advice by extracting insights from multiple proposal documents. Seamless access to specialized knowledge enables the sophistication of sales activities and the elimination of dependency on individual expertise.

This recommendation system searches a product database according to the content of a natural language query and recommends the most suitable product for the customer. The system accesses the latest product database, elicits the customer's needs through natural language, and proposes the most suitable product.

When considering how to utilize LLM systems and test introduction effectiveness, the challenge lies in sharing insights about LLM performance and limitations and conducting accuracy verification using appropriate methods. ABeam Consulting shares LLM insights and departmental needs through interactive discussions, conduct studies on utilization methods based on our findings, and then execute accuracy verification and improvements through a trial period. In addition, we offer support for the digitization of analog information and user training.