Enterprise knowledge assistant
Use retrieval-augmented generation over policies, product material, manuals and project documents for sourced internal answers.
Enterprise AI agents and AI enablement in Hangzhou
Bring AI into real workflows, not just another chat window
Assess and build controlled, integrated AI agents for enterprise knowledge, customer service, operations and internal workflows.
Practical AI enablement scenarios
Start with frequent, measurable and controlled tasks, connecting AI output with business data, workflows and human review.
Customer service and sales copilot
Help classify intent, summarize context, draft responses and organize leads while retaining human review.
Workflow and operations agent
Connect tickets, content, orders, projects or office systems within explicit permission and approval boundaries.
Analysis and content assistance
Summarize structured and unstructured information to support reporting, content and operating decisions.
Moving an AI project from demo to production
- 01
Use-case assessment
Define users, task frequency, current cost, available data, accuracy needs and unacceptable risk.
- 02
Data and system boundaries
Map knowledge, permissions, interfaces and sensitive data to choose cloud, private or hybrid delivery.
- 03
Prototype and evaluation
Test retrieval, answers, tool use and failure handling against repeatable examples.
- 04
Integration and iteration
Connect the capability to existing products, observe real use and improve prompts, knowledge and workflows.
AI that integrates with existing digital products
AI agents can connect to websites, mini programs, apps, enterprise messaging and administration through APIs and controlled tools.
- Model routing
- RAG knowledge retrieval
- Tools and workflows
- Identity and permissions
- Human review and fallback
- Evaluation and analytics
Security, accuracy and operability are part of the design
Data minimization
Provide only the data required for the task and separate public, internal and sensitive information.
Controlled permissions
Reuse business permissions and require confirmation for high-impact actions.
Traceable output
Provide sources for knowledge answers and preserve context, logs and review paths where needed.
Observable cost
Measure model usage, latency, failure and actual outcomes before expanding the system.
AI agent questions
What is an enterprise AI agent?
An enterprise AI agent can understand a task, use authorized knowledge or tools and assist with business work under rules and human confirmation.
Can AI be added to an existing website or system?
Yes. We first assess identity, APIs, data and permissions, then choose an embedded assistant, independent service or back-office workflow.
How does a knowledge base reduce hallucinations?
Restricting sources, retrieving relevant material, citing sources, refusing unsupported answers and adding human review can reduce risk, but cannot guarantee zero errors.
Can an AI agent fully replace employees?
The better first goal is assistance with retrieval, organization, drafts and standardized tasks while people retain exception handling and high-risk decisions.
How is an AI project priced?
Pricing depends on models, knowledge volume, integrations, permissions, concurrency, deployment and evaluation requirements. Start with discovery and a focused validation.
Start with one valuable, measurable AI use case
Bring the current workflow, representative material and the outcome you want to improve.