Support Assistant
Handles common questions, guides forms, organizes tickets and hands over to human service when needed.
AI Agent Development
AI Agent Delivery for Global Chinese Businesses
If you want to turn FAQs, customer support content, knowledge bases, reports, image and voice data or internal SOPs into coordinated AI workflows, we can design LLM + RAG + Tools and multimodal solutions so AI contributes to business execution, knowledge management and operational efficiency instead of staying at the level of basic chat responses.
RAG Knowledge Base
Workflow Automation
Private Deployment
Scenarios
Common AI Agent Scenarios
Knowledge Assistant
Combines SOPs, documents, FAQs, contracts and product data to improve internal retrieval efficiency.
Operations Assistant
Supports data summaries, reporting output, to-do generation and cross-system task triggering.
Sales and Advisory Assistant
Helps classify early requirements, recommend solutions and prepare proposal materials.
Knowledge Preparation
We organize FAQs, documents, SOPs, product descriptions and historical conversations into a searchable foundation.
Agent Workflow Design
We define triggers, tool abilities, role boundaries and human takeover rules so the agent stays within a controlled scope.
Continuous Optimization
Prompt logic and workflow design are refined based on usage records, hit rate and cost performance.
Architecture
How We Plan Reliable and Maintainable AI Agent Delivery
LLM Selection
We choose the right model base according to cost, language ability, speed and deployment conditions.
RAG Retrieval
Document sources, chunking strategy and recall logic are organized to reduce hallucinations and answer drift.
Tool Integration
The agent can call CRM, ERP, databases, email services or internal APIs so it can execute real tasks.
Governance and Operations
Permissions, logs, prompt management, evaluation and future optimization are planned into the delivery from the start.
The success of an AI project depends on defining task boundaries and data responsibility before model selection
We first identify what the agent can answer, what it can execute and what must be handed to a human, then choose the right models, tools and data flows.
AI must map to actual roles and operating processes before it can create stable value
Support, sales, advisory, operations and knowledge management each have different data sources, handoff rules and risk-control requirements.
Use Cases
Which Types of Businesses Benefit Most
01
Service Businesses
Suitable for teams that need inquiry triage, knowledge-based answers and structured customer communication.
02
Content and Education Businesses
Suitable for document retrieval, course Q&A, content organization and summary generation workflows.
03
Operations-intensive Teams
Suitable for reporting summaries, data lookup, workflow triggering and internal SOP assistance.
What We Usually Build
Typical AI Module Combinations
Real delivery projects usually combine retrieval, conversation, tool integration and tracking logic into a complete AI application structure.
FAQ + Knowledge Base
Answers common questions with cited document content and reduces repetitive manual replies.
Support Triage + Tickets
Classifies issues and gathers data before passing requests to human support or creating tickets.
Operations Summary + Reports
Consolidates data from multiple sources to produce daily summaries, weekly reviews and metric explanations.
Advisory Assistant + Proposal Support
Helps organize requirements, compare service modules and draft advisory-style responses.
Related Topics
Related AI Service Topics
If your requirement is already focused on support automation or knowledge retrieval, the topic pages below provide a faster path for comparing implementation approaches.
AI Customer Service
Focused on FAQ automation, support triage, human handoff and information collection workflows.
Learn More →RAG Knowledge Base
Focused on document retrieval, citation-based answers, knowledge governance and permission control.
View Solution →Explore Service Areas
Review more AI and software delivery directions and find the most relevant content entry point.
View Details →
FAQ
Common Questions About AI Agent Delivery
Is private deployment always required?
Not always. It depends on data sensitivity, cost and speed requirements, but internal documents and customer data usually justify private or dedicated deployment first.
Can AI agents connect to existing systems?
Yes. As long as the system exposes APIs, databases or a suitable middleware layer, the interaction workflow can be planned.
How do you avoid ending up with a simple chatbot?
The key is to define task boundaries, data sources and tool abilities first, then design workflows and human takeover rules around them.
If you already have support history, SOPs or FAQs, you likely have enough material to begin a first AI agent release.
We can review usable data, suitable automation workflows and whether the project should be delivered together with an existing website or system upgrade.