Knowledge Silos
Policies, manuals, tickets and past cases sit across systems; they are hard to find, slow for new hires and fragile when experts leave.
Hard to ask
Shenzhen Yunqi Zhiyuan Technology Co., Ltd.
Put AI to Work Inside Your Business
From answering questions to executing workflows, we connect knowledge, data, systems and people so AI becomes an operational capability, not a demonstration.
About
A Team That Takes AI to Delivery
We serve enterprises on two tracks: an AI-native development and governance platform for the future and traditional software development for present-day delivery.
Who We Are
Founded in 2026 in Shenzhen, we build enterprise AI-native development and governance platforms and the business systems that run on them. Project delivery sustains current operations while platform work reserves both development capability and governance control for the future.
Products are delivered independently, configured on demand, with modules that can be split apart. Enterprises can add intelligence to existing systems or start with one verifiable scenario and scale later.
AI-Native Development & Governance Platform
Builds a long-term advantage with intelligence that is reusable and governable.
Traditional Software
Covers management systems, ERP, mini programs and mobile apps.
- Founded
- 2026 · ShenzhenRegistered in Pingshan District, Shenzhen
Challenges
Four Obstacles to Real AI Value
Enterprises are not short of models. They are short of a platform that connects knowledge, data, process and execution.
Data Islands
Systems do not talk to each other, so the same data is entered and moved by hand. Manual handling is both cost and error.
Cannot connectExecution Gap
AI only offers advice; the decision and system action remain disconnected, so the gain stops at the chat window.
Stops halfwayNo Capability Reuse
Each department pilots separately without unified management or continuous improvement, so spend never becomes capability.
Nothing accumulatesThe answer is not another chat window. It is an enterprise AI-native development and governance platform that can be governed, monitored and continuously improved.
Platform
AI-Native Development & Governance Platform
One foundation and six core capabilities. It supports efficient development of AI products while keeping permissions, data and results governed. The foundation is not sold as a bundle; it supplies shared modules to each product.
Multi-Agent Collaboration
Complex work is decomposed and handled by several agents working in sequence.
Document Q&A
Policies and manuals go in; staff ask directly and answers cite the source.
Data Relationship Mapping
Relationships between equipment, orders and suppliers become queryable.
Configuration over Coding
Forms and workflows are configured rather than coded; changes take effect the same day.
Legacy System Integration
Connects to existing systems via standard interfaces, without disruption.
Continuous Optimization
Corrections feed back automatically, improving results over time.
Configured to your need, not one fixed bundle. Customer service uses Document Q&A and human handoff; ERP uses workflow and system integration. Permissions, data scope and correction mechanisms are configured with each scenario, so you can add more later.
Products
Six Standalone Products, Configured as Needed
Each item is an independently delivered product: no bundling, nothing forced, with modules that can be split and customized.
Internal Management System
Approvals, assets, HR and projects managed in one place.
ERP
Procurement, inventory, orders and finance aligned, with no duplicate entry.
AI Customer Service
Answers from your own knowledge base; hands off to a human when unsure.
AI Lead Generation
Lead capture and follow-up reminders that reduce missed prospects.
WeChat / DingTalk / Douyin Mini Programs
Built once and reused across platforms.
Mobile App
On-site photo capture and reporting, with offline data collection.
How they relate to the platform: shared modules, independent delivery. Products draw on the same platform modules and can be built alone or in combination, with call scope and data boundaries governed centrally.
Execution
Knowledge You Can Query, Work That Gets Done
The platform does the thinking; bots do the doing. Suggestions, confirmation and system action form one closed loop.
Knowledge Retrieval Q&A
Policies, manuals, contracts and work orders go into one repository. Staff ask in plain language, and every answer cites the source file. When nothing is found, it says so rather than guessing.
What people askWho signs off on this expense? What parts were replaced at the last overhaul?
- Imports PDF, Word, spreadsheets and web pages
- Natural-language questions, no search syntax needed
- Structured tables and unstructured text queried together
Workflow Execution Automation
Repetitive work such as moving data, filling forms and reconciling records is performed by bots under defined rules, closing the loop from suggestion to confirmation to system action.
What it doesEnters paper work orders into the system; reconciles two systems daily and flags the differences.
- Describe what to do, not where to click
- Every step is viewable and editable; exceptions go to a human
- Workflow nodes and owners are adjustable, effective the same day
Delivery
Deployment and System Integration
Choose a suitable way to start, keep existing processes and data, and avoid a rip-and-replace project.
On-Premises Deployment
Data stays in-housePlatform and models installed on your own servers or private cloud; documents and operational data remain within your environment.
Typical for: energy, manufacturing, government and financeHosted Environment
Ready to useWe provide a managed environment suited to piloting first and deciding later whether to move in-house.
Typical for: pilot stage and lean teamsIntegration with Existing Systems
No rip-and-replaceConnects to ERP, OA, ticketing and CRM through standard interfaces, adding intelligence where it matters.
Typical for: companies with mature system landscapesIntegration points agreed before work starts. Access is granted by role and data scope; sensitive fields, write-back fields and rollback mechanisms are documented; acceptance criteria are defined before the pilot.
Roadmap
From One Scenario to Continuous Scale
Small before large, acceptance first, and handover on delivery.
01
Scenario Discovery
Select frequent and verifiable scenarios, then define goals, data and system boundaries.
1 to 2 weeks02
Pilot Build
Complete knowledge onboarding, workflow configuration and integration, then validate with real work.
3 to 6 weeks03
Delivery & Training
Confirm results against acceptance criteria and complete user training plus operations handover.
7 to 10 weeks04
Optimize & Extend
Monitor live performance, turn learnings into reusable assets and expand to adjacent steps.
OngoingSmall before largeValidate one or two scenarios and produce verifiable results before scaling.
Acceptance firstDefine metrics, data sources and counting rules before the pilot starts.
Handover on deliveryTransfer configuration notes, knowledge base inventory and operations manuals.
Value
Four Measurable Client Improvements
We make no claims we cannot measure. Every benefit gets an agreed definition before the pilot and is judged on data both sides accept.
Less Repetitive Work
Data movement, form filling and reconciliation shift to bots; people handle exceptions and judgement calls.
Measured by handling time and interventionsReusable Knowledge
Policies, manuals and past cases become searchable assets, shortening ramp-up for new staff.
Measured by hit rate and correctionsFrom Advice to Execution
Recommendation, confirmation and execution run in one flow, with task stage and owner visible.
Measured by automation share and cycle timeGoverned and Improving
Deployed assets are managed centrally; live performance is monitored and drives refinement.
Measured by assets and exception routing
Case
Delivery Case and Industries in Progress
Start with maintenance and integration delivery, then extend to adjacent steps.
Energy Sector Delivery Scenario
Phase 1 in deliveryScenario
Knowledge lookup, work order handling and cross-system data checks during maintenance and integration delivery.
Approach
Build an equipment and work order knowledge base; use collaborating agents to chain retrieval, form filling and verification, with exceptions routed to humans.
Acceptance
Observe handling time, number of human interventions and data-check error count, agreed in writing before the pilot.
Next
Extend to inspection, spare parts and supplier coordination.
Note: this project is in its first delivery phase. Results will be published after pilot acceptance on terms agreed by both sides; no unverified figures are presented here.
Security
Security and Compliance
Control is built into the design, with three gates on the AI.
Data Location Is Your Choice
Deployment optionalOn-premises deployment is supported; hosted storage scope and retention are agreed up front.
Role-Based Access Control
Actions traceableAccess is granted by role and department; key actions are logged and can be exported for review.
Connected Assets Registered
Capabilities controlledAgents and tools are registered centrally, with accessible data and permitted actions authorized item by item.
Ownership Clearly Defined
Ownership clearYour business data, knowledge base and workflow configuration remain yours and are handed over on delivery.
Data controlAccess scope, sensitive-field handling and retention are agreed before anything else.
Permission controlRead, write and trigger permissions are authorized one item at a time.
Output controlAnswers cite sources, exceptions go to humans, and every action is logged.
Contact
Start with one scenario, validate, then scale
If you are evaluating enterprise knowledge Q&A, workflow automation, management systems or AI integration, we are glad to discuss a verifiable pilot.
Phone18665887494