Enterprise Large Language Model Application Cases
Empowering enterprises with AI 2.0 to generate production, business opportunities, and create user value.
Accelerating innovation in smart scenarios and moving towards a new blue ocean for industry
Build enterprise-specific large language models using AFS (AI Foundry Service).
from Training optimization arrive Deployment and launch
Assisting businesses with one-stop implementation[Enterprise-Specific Large Language Model]
AFS Pipeline Solution Flowchart
Enterprise applications connect to external data
easyIntegrating LangChain and Flowise application frameworksDevelopment Quickly build enterprise-specific applications
FFM (Formosa Foundation Model) can be integrated with Langchain or Flowise, leveraging framework functionality to make interaction with large language models more convenient. It also helps to build enterprise-specific large language models and data (such as Stripe, SQL, PDF, CSV, etc.) into other work applications or connect to Google web search, assisting enterprises in easily creating, testing, and deploying their own large language models through Taizhi Cloud's AFS (AI Foundry Service).
Enterprise knowledge management intelligent search and rapid summarization
ThroughText similarity searchQuestion and answer format Quickly output the best response
By using indexes (Indexes) to structure documents (e.g., a 50-page PDF file) into text: establishing a text database (Document), text splitting (Splits), vector storage (VectorStores), and retrieval (Retrievers), comparing similar single or multiple answers to the current query, and then using the summarizing capabilities of a large language model to quickly provide the best solution, it helps enterprises obtain the information they need more quickly and accurately, reducing unnecessary search time and manpower costs, making knowledge sharing smoother and more efficient, and realizing innovation in enterprise knowledge management.
*Application scenario diagrams are for reference only.pleaseSubject to the actual situation and service provided by the project
More precise corporate financial, marketing, and operational decisions
Examining historical data and past patterns To perform structured or unstructuredNumerical prediction
Enterprises can use AFS training to optimize historical data and past patterns (e.g., procurement records, customer purchase records, or equipment maintenance records) to develop [enterprise-specific large language models] for future demand and trend prediction and recommendations (e.g., procurement project prediction/recommendation, consumer purchase behavior prediction/recommendation, or equipment maintenance cost/time prediction). This helps personnel manage operations more effectively, control resources and costs, increase sales revenue, and contribute to enterprise operational decisions and growth.
*Application scenario diagrams are for reference only.pleaseSubject to the actual situation and service provided by the project
Application Cases
Intelligent AI Customer Service/Assistant
From the response to the canned goods message → Simulated smooth dialogue
By using AFS to train a large amount of internal data, the time spent on manual operations can be reduced. Through continuous optimization of the [enterprise-specific large language model], the system that was previously used for greeting, basic business introduction or data query can be upgraded into a smart AI customer service/assistant for each industry sector. This can solve user problems more naturally and smoothly, improve service processing efficiency and user satisfaction, capture the hearts of more potential customers, and significantly reduce the cost of manpower and time.
*Application scenario diagrams are for reference only.pleaseSubject to the actual situation and service provided by the project
Online Outbound Sales System
From after-sales response service → Real-time predictive shopping guide
By collaborating on dialogue content and internal/external data in a timely manner, businesses can conduct faster and more accurate data analysis and discovery based on consumer needs, dynamically predict user profiles, generate immediate and personalized responses, eliminate human bias or errors, create more personalized customer service, maximize marketing effectiveness and business value, create a more exceptional customer experience, improve customer satisfaction and loyalty, and strengthen corporate competitiveness.
*Application scenario diagrams are for reference only.pleaseSubject to the actual situation and service provided by the project
OMO (Online-Merge-Offline) Applications in Enterprise Groups
OMO (Omni-channel Business Model) → AI 2.0 New Retail Service Ecosystem
By leveraging AFS to assist large enterprises in integrating the vast database of their new retail OMO system, connecting various venues and online and offline channels, and effectively grasping the profiles and core needs of consumers across all channels, the [enterprise-specific big language model] can also serve as a collaborative brain for real-time generation of marketing/operations/customer service/sales/products, comprehensively enhancing customer experience and brand value, solving the problem of increased human resource costs due to business expansion, allowing personnel to focus on high-value pioneering tasks, creating new business models, and accelerating the doubling of enterprise return on investment.
*Application scenario diagrams are for reference only.pleaseSubject to the actual situation and service provided by the project
Enterprise Web Application Integration of Large Language Models
Through AFS inference serviceAPI call
"AFS (AI Foundry Service)" provides a one-stop integrated service to help enterprises develop their own enterprise-grade generative AI models.
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