For specific business scenarios, we provide consulting, design and development services for AI agents. Combining large language models, enterprise knowledge bases and existing business systems, we build intelligent assistants capable of knowledge Q&A, information retrieval, process execution and analytical support.
An AI agent is typically built around a large language model, combined with knowledge retrieval, tool calling and process orchestration, so that AI does not stop at "answering questions". Within an agreed business scope it understands task intent, breaks the work into executable steps, calls system interfaces and returns results. Tasks such as checking order status, drafting tickets, consolidating report data or pre-screening forms against rules can all have part of their repetitive work handled by an agent.
From an engineering perspective, how well an agent performs depends closely on data quality, process clarity and how scenario boundaries are drawn — simply plugging in a model does not deliver results. At project kick-off we usually start with a scenario assessment: which steps suit an agent and which still require human judgement. On that basis we define scope and acceptance criteria, avoiding unnecessary complexity added purely for the sake of the concept.
Covering the common stages from building an agent to integrating it; the modules can be combined as a project requires
Based on response requirements, call costs and data compliance, we help choose a public-cloud LLM or a private deployment, and leave room to switch models later
Documents, FAQs and internal policies are cleaned and structured as the basis for answers, with citations provided so staff can verify them
Connect existing ERP, OA and CRM systems through interfaces, so the agent can query data, create documents or trigger approval flows
Supports continuous conversation with context, follow-up questions on vague input and branching intents, proactively confirming key details when information is insufficient
Repetitive tasks are broken into executable steps with conditional logic and human confirmation nodes, forming a semi-automated business workflow
Recognises and extracts text, images and tabular material — useful for document entry and quality-inspection records; accuracy depends on the clarity of the source material
Task stages are divided by role — retrieval, execution and review checking one another — which reduces, to a degree, the bias introduced by a single model’s output
Depending on the usage scenario, the agent can be embedded in a web widget, WeCom, a mini program or an app, or exposed as an API for other systems to call
Points that deserve attention when putting an agent into production; the order can be adjusted to suit the project
Review high-frequency business steps whose rules are relatively clear, define what the agent owns and when a human takes over, and settle on measurable acceptance criteria.
Organise usable material and define how it is updated and maintained; the completeness and currency of the knowledge content strongly affect how useful the answers are.
Write stable task instructions and tool descriptions, together with parameter validation, error handling and retry logic, to keep the calling process under control.
Build a test set of typical questions and keep tracking accuracy, stability and response speed, refining the agent step by step as real usage feedback comes in.
Reuse the accounts and permission system of the existing systems, require approval or a second confirmation for sensitive actions, and keep call logs for traceability.
Track usage data and user feedback, refresh knowledge content and adjust workflow nodes at regular intervals, so that the agent keeps pace with business change.
Some directions worth exploring; the applicable scope needs to be assessed against your actual business processes
For looking up policies, product material and operation manuals, answers come with source citations, cutting the time spent hunting for information.
For common enquiries, the agent suggests replies and points to relevant material; a support agent confirms the reply before it is sent. Suited to pre-sales answers and after-sales guidance.
Helps with meeting-minute write-ups, drafting notices and pre-filling form information, with the output reviewed by the relevant colleague before use.
Turns everyday data requests into queries and generates charts with a brief explanation, making it easier for business colleagues to review operational figures.
Organises customer follow-up records and drafts first versions of proposals and talking points, giving sales and marketing teams reference material to work from.
Visible data is controlled by role, critical actions keep a human confirmation step, and complete call logs are retained for later audit and traceability.
Combined flexibly according to project scale and the team’s current situation
Technology choices are usually settled after the requirements assessment, driven mainly by compatibility, maintainability and the team’s existing foundation. We do not stack components for their own sake.
Talk to us about your requirements and we will provide an initial assessment based on your existing systems and data
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