For IT and digital teams: one agent platform, data foundation, model gateway, training platform and private compute. Domestic databases, ARM servers and offline installers are already proven.
Featured solution
Agent orchestration platform
One platform for every department's agents — domestic stack, offline, white-label
How it works
1
One foundation
Platform, AI and front-end layers
2
Domestic stack
Domestic databases, ARM
3
Offline delivery
Install, upgrade, rollback
4
Agent orchestration
Knowledge, tools, workflows
5
White-label
Under your own brand
What you get
No duplicated effort across departments
Meets domestic-stack and offline requirements
Runs under your own brand, internally and externally
Visual orchestration of LLM agents, with white-label delivery and offline deployment on the domestic stack.
The problem
Each department buys its own AI tools; domestic-stack and offline environments are hard to deploy into.
Who it's for
State-owned enterprises, government, groups needing one AI platform
Delivery
Private deployment, delivered in WeCom, Feishu, DingTalk or on the web
What it does
Platform, AI and front-end layers decoupled
Runs on domestic databases and ARM servers
Offline installers with rehearsable install, upgrade and rollback
White-label ready: name, logo and banned-term checks
Enterprise data foundation
Deliverable
Connectors, versioned datasets with lineage, transformation pipelines, a business ontology and agents — with every action approved by a person before it runs.
The problem
Data is spread across systems, AI doesn't understand business objects, and agents editing data directly is risky.
Who it's for
Companies with solid data that want cross-system AI applications
Delivery
Private deployment, delivered in WeCom, Feishu, DingTalk or on the web
What it does
Multi-source connectors and versioned datasets
SQL and Python pipelines with data lineage
Business ontology: objects, properties, relations, actions
Agents only propose actions; people approve before execution
AI data hub and model gateway
Deliverable
One exit for every LLM call in the company: routing by use case, usage metering, budget circuit-breakers and audit.
The problem
Model calls are scattered, costs are unclear, and problems can't be traced.
Who it's for
Groups and institutions where many departments use LLMs
Delivery
Private deployment, delivered in WeCom, Feishu, DingTalk or on the web
What it does
Unified model gateway compatible with mainstream APIs
Usage and cost metered by department and use case
Budget circuit-breakers and call-loop detection
Full audit; connects to knowledge bases and the data platform
Model training and tuning platform
Deliverable
Data, labelling, training, evaluation and release in one line, tuning models continuously with business feedback.
The problem
General models don't know industry terms; user feedback is collected but never used.
Who it's for
Companies with proprietary data that need an industry model
Delivery
Private deployment, delivered in WeCom, Feishu, DingTalk or on the web
What it does
Data management and labelling
Fine-tuning and reinforcement-learning tuning
Promoted only after evaluation passes, with one-click rollback
GPU scheduling and allocation
Apps from one sentence
Deliverable
Describe an idea in one sentence and a team of agents builds a web or mobile app you can run, iterate on and publish.
The problem
Small internal tools never make the IT roadmap, and outsourcing is slow and expensive.
Who it's for
Business units, innovation teams, IT
Delivery
Private deployment, delivered in WeCom, Feishu, DingTalk or on the web
What it does
Agents split planning, building, review and verification
Code generated live and reviewed automatically
Live preview, iteration and one-click publishing
Good for internal tools, campaign pages and light business systems
Private compute and model deployment
Deliverable
GPU server sizing plus private deployment and operation of open-weight Chinese LLMs — built once, shared by many use cases.
The problem
You want LLMs but can't send data to the internet, and don't know how much compute to buy.
Who it's for
Clients in any sector with strict data-security needs
Delivery
On-premises, dedicated cloud or hybrid
What it does
Compute sized per use case, starting from consumer-grade GPUs
Open-weight Chinese LLMs deployed privately; external APIs can be disabled in one step
Load-balanced local models with a domestic cloud model as fallback
Monitoring alerts to WeCom or Feishu
Start with a half-day workshop
Your business owner and IT lead join us to pin down 1–3 candidate use cases and their success metrics.
