Enterprise LLMs Application Cases

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Enterprise Applications with External Data

Easily integrate LangChain and Flowise application frameworks.
Quickly build enterprise GenAI applications.

FFM can be integrated with Langchain or Flowise to leverage framework functionalities to facilitate interactions with large language models, and help build [enterprise-specific large language models] and data (such as Stripe, SQL, PDF, CSV...) into other workflow applications or integrate with Google web searches. This makes it easier for enterprises to create, test, and deploy custom large language models using Taiwan AI Cloud AFS.

Enterprise Knowledge Management, Intelligent Search and Fast Summarization

Using Question-and-Answer Approach Based on Text Similarity Search.
To Quickly Generate the Best Response.

By indexing data documents (e.g., a 50-page PDF file), you can structure text data: create a text database (Document), split it into segments (Splits), store vectors (VectorStores), and use Retrievers. This allows you to match the current query with similar single or multiple answers, and then, with the summarization capabilities of a large language model, quickly provide the best solution. This helps businesses access the information they need more quickly and accurately and reduces unnecessary search time and labor costs. It also facilitates smoother and more efficient knowledge sharing and enables innovative knowledge management for enterprises.

*The application scenario diagram is for reference only; please refer to the actual project and services.

More Accurate Corporate Financial, Marketing and Operational Decisions

View Historical Data and Past Patterns.
Make Structured or Unstructured Numerical Predictions.

Enterprises can use AFS to train and optimize historical data and past patterns (e.g., procurement records, customer purchase history, or equipment maintenance records) to develop an [enterprise-specific large language model]. This model can be used for future demand and trend forecasting and recommendations (e.g., procurement item forecasting/recommendation, consumer purchase behavior prediction/recommendation, or equipment maintenance cost/time forecasting). It assists personnel in managing operations, resource control, and costs more effectively, enhancing sales revenue and contributing to corporate operational decisions and growth.

*The application scenario diagram is for reference only; please refer to the actual project and services.

Application Scenarios

Intelligent AI Customer Service/ Assistant

Respond from canned messages → Fluent responses as if talking with real people

AFS is used to train on vast amounts of internal enterprise data and information to reducing manual operation time and continually optimize the [enterprise-specific large language model], thus transforming the previous system that served as a basic welcoming business introduction or data query tool into specialized intelligent AI customer service/assistants tailored to various industry domains. These AI systems can naturally and fluently address user queries, enhancing service efficiency and user satisfaction, speak to the hearts of more potential customers, and significantly reduce labor and time costs.

*The application scenario diagram is for reference only; please refer to the actual project and services.

Online Outbound Sales System

From after-sales response service → Real-time predictive shopping guide

Through real-time collaboration of conversations and internal/external data targeting consumer needs, it helps businesses perform faster and more accurate data analysis and exploration, dynamically predicts user profiles, generates real-time and customized responses, eliminates human bias or errors, and creates a more personalized customer service experience. This will help maximize marketing effectiveness and business value, create an exceptional customer experience, increase customer satisfaction and loyalty, and enhance the competitiveness of the enterprise.

*The application scenario diagram is for reference only; please refer to the actual project and services.

OMO Domain Applications for Enterprise Groups

OMO omnichannel business model → AI 2.0 new retail service ecosystem

By using AFS to assist large enterprises in integrating the extensive database of their OMO new retail system, various domains and physical-digital channels are connected to effectively grasp the core of omnichannel consumer profiles and demands. The [enterprise-specific large language model] can serve as a collaborative AI brain for marketing, operations, customer service, sales, and product generation in real-time. This comprehensive approach enhances customer experience and brand value and reduces the manpower costs resulting from business expansion. It allows personnel to focus on high-value creative tasks, create entirely new business models and help double the company's return on investment.

*The application scenario diagram is for reference only; please refer to the actual project and services.

Enterprise Web Applications Integrating LLMs

Making API Calls through the AFS Inference Service

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