ACTIVEProject templateCustomisable

Enterprise LLM Application Development

Agents, RAG knowledge bases, mini-programs and on-premise deployment — bringing LLM capability into your business workflows

RAG knowledge baseCustom agentsAIoT analyticsContract analysisResume diagnosis

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Overview & features

Custom LLM applications for enterprises, built with the engineering methods refined in our two in-house AI SaaS products: intelligent analysis of documents / contracts / resumes, enterprise knowledge-base Q&A, business agents and AIoT device data analytics. Delivered as a web app, mini-program or on-premise deployment, with multi-provider model abstraction, an evaluation framework and compliance design (in-country data, informed consent, AIGC labelling).

Key features

From requirements to launch: prompt engineering, RAG, agent orchestration, evaluation and staged rollout
Switchable models: domestic first, selected on compliance and cost
AIoT integration: device data ingestion + LLM analysis and alerting
Compliance by design; on-premise deployment available

011 week
Requirements & data assessment
Define the scenario, data sources, compliance boundaries and acceptance criteria
021–2 weeks
Prototype & evaluation set
A runnable prototype plus test cases — see results before fixing scope
034–8 weeks
Build & integrate
RAG / agent / frontend / mini-program, integrated with enterprise systems
04Ongoing
Launch & operate
Staged rollout, monitoring, evaluation regression and model switching

Architecture & stack

LLM 多 provider（DeepSeek / Claude / 通义）RAGSSE 流式LLM-as-judge 评测Next.js / NestJS

Applications

Enterprise AI

FAQ

Does data from AI applications leave China?

By default we use models hosted in China and deploy in China, so data does not leave the country. If an overseas model is required, the customer makes that choice explicitly after a compliance assessment.