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Who we are
A full-stack enterprise AI implementation partner focused on access, deployment, applications, governance, and operations.
Enterprise AI Implementation
Enterprise AI implementationmake AI accessible
OurYun.AI connects compute, models, data, applications, and security governance so enterprises can move from AI pilots to managed, deployable, and continuously operated business capability.
Positioning
Based in the Greater Bay Area, built for enterprise AI rollout
Before talking solutions, we answer four questions: who we are, who we serve, what we solve, and how AI becomes an operational capability.
0202
Who we serve
Enterprises that need AI applications, data security, system integration, and ongoing AI operations.
0303
What we solve
We solve model selection, access, deployment, data, permission, security, cost, and value-retention problems.
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How we work
We package resources, model capability, data knowledge, business applications, security governance, and enablement into one rollout path.
Implementation challenges
Enterprise AI gets stuck at systematic rollout
Between tool trials and enterprise rollout sit accounts, models, APIs, data, security, workflows, and operating models. Clarify the scenario and rollout boundary before choosing the right access, governance, and deployment path.
01
Fragmented accounts
Tools, models, accounts, and permissions are separate, making usage boundaries hard to unify.
02
Hard model selection
Cloud, local, and proprietary models coexist without a comparable and governable selection mechanism.
03
Complex API access
Interfaces, routing, auth, cost, and call patterns are fragmented, increasing integration cost.
04
Data remains siloed
Docs, knowledge, tickets, and business data are scattered, so AI cannot reliably use trusted context.
05
Security is hard to control
Permissions, masking, audit, risk, and compliance boundaries lack unified governance.
06
Value is not retained
Pilot results fail to become reusable knowledge, workflows, metrics, and operations.
AI rollout is not tool procurement. It makes AI usable by employees, maintainable by technology teams, and measurable by management.
Approach
AI rollout is not tool procurement. It makes AI usable by employees, maintainable by technology teams, and measurable by management.
Enterprises do not need isolated tools. They need an AI capability system that can be connected, governed, deployed, and operated.
SEC01
Security governance
Data security, model security, permission control, and compliance guardrails across the full AI lifecycle.
AD02
Auditability
Record calls, cost, output, risk, and key business events for traceability and review.
CO03
Cost control
Break down cost by scenario, role, model, and provider with quotas and optimization.
TR04
Enablement operations
Enable employees to use AI, teams to manage it, and management to measure outcomes.
Full-stack capability system
From infrastructure to business scenarios, build an enterprise AI capability network
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Scenario solution layer
Package scenario solutions for service and sales, knowledge management, back office, R&D ops, manufacturing inspection, and industry AI.
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Application capability layer
Build reusable capabilities such as AI assistants, agent workflows, document generation, service QA, and workflow automation.
03
Data and knowledge layer
Turn enterprise documents, policies, projects, product material, and business data into reusable AI knowledge assets.
02
Model platform layer
Unify multi-model access, model gateway, routing, evaluation, monitoring, and audit.
01
Infrastructure layer
Plan compute, servers, cloud resources, storage, networking, and deployment environments so AI runs reliably first.
Business workflow
Put AI into real business actions
OurYun.AI does not only deploy models. It connects employee entry points, AI assistants, agent workflows, business systems, and governance operations.
Typical scenarios
Choose AI rollout paths by business entry point
Build deployable solutions around knowledge management, service and sales, back-office work, R&D ops, workflow automation, and industry AI.
智能制造
Manufacturing AI quality inspection
An AOI + AI inspection appliance for PCB production that performs AI-based second-pass review on AOI NG images and reduces manual review load.
国产化算力
Domestic AI inference resource pool
A unified domestic AI inference resource pool for a large manufacturing group, supporting multiple large models and group-level AI platform operations.
AI 算力
DeepSeek private compute foundation
A 6-node, 48-GPU private compute foundation for DeepSeek-R1 671B, including API publishing, AI security governance, and model security assessment.
本地化部署
AI server and DeepSeek R1 70B private deployment
A private AI server delivery with DeepSeek R1 70B deployed locally for document parsing, image recognition, speech-to-text, knowledge Q&A, and business reasoning.
费控商旅
Expense & travel assistant: travel cost -23%
AI handles trip request, booking, expense and reconciliation — month-end close from 15d to 2d.
Agent 工作流
Agent workflow: 15 min credit review to 90s
Agents automate business lookup → credit check → limit → report, fully auditable at every step.
AI 知识库
AI knowledge base: 12K docs, 40s to retrieve
Process, quality and compliance docs behind RAG — engineers get cited answers on the floor.
智能客服
AI customer service: 4 minutes to 12 seconds
120K SKUs and 20K member rules on a RAG knowledge base — 20x faster for stores and members.
Delivery method
AI rollout needs continuous delivery and operations
From diagnosis, solution design, and PoC to deployment, enablement, and continuous operations, we build an enterprise AI loop that can land, repeat, and evolve.
01
Scenario diagnosis
Identify business pain points, users, data conditions, system boundaries, and success metrics to decide where AI should start.
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Solution design
Design compute path, model access, data knowledge, security governance, business workflow, and delivery milestones.
03
PoC validation
Validate effect, cost, security boundaries, and rollout feasibility with a focused real-data pilot.
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Deployment integration
Deploy the environment, integrate systems, configure permissions, enable monitoring and audit, and release to production.
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Enablement rollout
Train employees, administrators, and operations teams by role to drive real usage and organizational adoption.
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Continuous operations
Continuously optimize models, knowledge, workflows, cost, and outcome metrics so AI productivity keeps evolving.
Implementation results
From capability building to business outcomes, every step can be delivered and reviewed
0Capability layers
0Delivery steps
0Scenario families
0+Governance dimensions
Work
Selected projects
We work with enterprise teams to bring AI into service, knowledge, approval, expense, and travel workflows.
智能制造
2026某制造业企业
Manufacturing AI quality inspection
An AOI + AI inspection appliance for PCB production that performs AI-based second-pass review on AOI NG images and reduces manual review load.
国产化算力
2026某大型制造业集团
Domestic AI inference resource pool
A unified domestic AI inference resource pool for a large manufacturing group, supporting multiple large models and group-level AI platform operations.
AI 算力
2026某科技企业
DeepSeek private compute foundation
A 6-node, 48-GPU private compute foundation for DeepSeek-R1 671B, including API publishing, AI security governance, and model security assessment.
本地化部署
2026某企业客户
AI server and DeepSeek R1 70B private deployment
A private AI server delivery with DeepSeek R1 70B deployed locally for document parsing, image recognition, speech-to-text, knowledge Q&A, and business reasoning.
费控商旅
2024蓝湖网络
Expense & travel assistant: travel cost -23%
AI handles trip request, booking, expense and reconciliation — month-end close from 15d to 2d.
Agent 工作流
2024长江金科
Agent workflow: 15 min credit review to 90s
Agents automate business lookup → credit check → limit → report, fully auditable at every step.
AI 知识库
2025鼎力智造
AI knowledge base: 12K docs, 40s to retrieve
Process, quality and compliance docs behind RAG — engineers get cited answers on the floor.
智能客服
2025联华零售
AI customer service: 4 minutes to 12 seconds
120K SKUs and 20K member rules on a RAG knowledge base — 20x faster for stores and members.
Why OurYun
Why customers can trust OurYun.AI
Not isolated tool delivery, but long-term partnership across resources, platform, scenarios, and operations.
RS
Resource integration
Integrate cloud, compute, servers, networking, storage, and model ecosystems to reduce selection and deployment cost.
PL
Platform engineering
Build model access, knowledge bases, agent workflows, and system integrations.
SEC
Security governance
Bring permission, audit, masking, risk control, and compliance into the AI lifecycle.
SC
Real scenario experience
Experience across service, knowledge bases, compute deployment, manufacturing inspection, finance travel, and R&D assistants.
PT
Ecosystem partnership
Connect cloud, model, security, and industry software partners to compose the right path for each scenario.
Insights
What we're thinking
Observations on engineering, design, AI and business.
RAG20 min read
5 common pitfalls in RAG engineering
From demo to production: chunking, retrieval eval, prompt injection, cost, and observability.
Read →
私有化15 min read
Self-hosted LLMs: architecture, inference and cost
Why finance, healthcare and government must self-host — and a production path to ship a 70B model in 6 weeks.
Read →
AI 治理12 min read
AI usage governance: turning scattered model calls into auditable capabilities
When 12 teams call multiple mainstream models independently, how do you satisfy audit, compliance and cost at the same time?
Read →
AI 接入8 min read
4 design principles for an enterprise AI gateway
Consolidate model calls scattered across 7 business systems into one governable capability platform.
Read →
Contact
Book an enterprise AI rollout review
Tell us about your scenario, data conditions, and current AI usage. We will help you identify the right rollout path.