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MIANXIANGAI® · ENTERPRISE AI FOUNDATION · MXOS

MianxiangAI® | Enterprise AI Foundation

We plan, build, deploy, deliver and continuously upgrade AI-native application systems for enterprises.

It is an AI application foundation that industries and enterprises can reshape. Six things every AI project otherwise rebuilds — AI orchestration, data modelling, workflow, permissions and visibility, licensing and metering, multi-channel access — are built once into the foundation; what is specific to an industry or a company is added on top through configuration, composition and module extension.

Book an assessment Working with us ↓
Private cloud or SaaS, one codebaseSwitch models without touching business codeWhitelisted workflows — no arbitrary codePermissions and visibility built into the coreEvery AI call metered
MXOS · Six layers
  1. AccessAdmin console, WeChat mini program, website, open API, Feishu / WeCom / DingTalk, MCP server
  2. Industry instancesResource center, work orders, field visits, AI Office, education, projects / CRM / contracts / finance and more
  3. CompositionMxJSON engine: workflows, online forms, datasets, triggers, AI page generation, open platform
  4. AI hubCapability-code dispatch, multi-vendor routing and fallback, agent proxy, RAG knowledge base, usage metering, health probes
  5. Platform coreIdentity, permissions and data visibility, portals, licensing, tenancy, seats and quotas, queues, scheduled jobs, audit log
  6. RuntimePHP 8 / MySQL 8 / Redis (optional) / Vue 3 admin

Business logic lives in the service layer; every channel is just a different way in.

Why a foundation

AI rarely fails in the enterprise because the model is not good enough. It stalls at three gaps between the model and the business.

01

AI welded into business code

Calling a model straight from business code means every vendor switch, every fallback and every cost question becomes a code change or a log hunt.

Our answer: business code calls capability codes; models, prompts and fallbacks are configured in the console.

02

"Every customer is different" vs "one codebase to maintain"

Roughly 80% of an industry system is common (forms, approvals, ledgers, reports, reminders, exports) and 20% is specific. Patch the core for the 20% and it rots; build separately and you maintain one system per customer.

Our answer: express the 20% as metadata composition, not core code changes.

03

From demo to delivery

A demo is quick. Delivery asks: who sees whose data, what happens when the licence expires, who pays for tokens, does it run on-premises, how are third parties authenticated and rate-limited.

Our answer: permissions, licensing, metering, multi-channel access and open APIs are core features every instance gets.

In one sentenceTurn "building an AI system for an industry" from a from-scratch project into an assembly on a foundation.

Four pillars

AI capability hub

From "call a model" to "call a capability".

  • Switch models without changing business code — bindings live in the console
  • Built-in fallback chains when the primary model times out or errors
  • Every call metered — tokens, cost, latency, fallback — by feature, user and tenant
  • Mainstream Chinese and international models, or your own inference service

MxJSON composition engine

Personalisation without code changes.

  • 20+ workflow node types, all whitelisted — the engine never runs arbitrary code
  • Human-review nodes that pause and resume; checkpoint restart and step-level retries
  • Form → AI processing → notification + ledger: the minimal loop for many industry scenarios
  • Open platform with signed requests, quotas and async callbacks for integrating existing systems

Portals and visibility

Many businesses on one platform, each seeing its own data.

  • One platform split into business portals; users see only the portals granted to them
  • Portal admins manage members and content, not the portal configuration itself
  • Anything you can edit you can see; nothing hidden from a list can be read by id — both enforced by tests
  • Role, menu and button-level permissions on top

Licensing and metering core

What makes a system deliverable and operable.

  • Signed licences covering modules, seats, quotas, expiry and grace period, verifiable offline
  • Trial / Standard / Professional / Enterprise editions, gated by module
  • Token wallet plus monthly quota, safe against over-deduction and races
  • If metering fails it lets the call through and logs it — a billing bug never blocks a successful AI response

Four ways to personalise

From lowest to highest effort — from same-day changes to deep customisation.

01 · No code · immediateConfigure

Portals, menus and permissions, model bindings, prompt templates, quotas and licences. For differences between companies in the same industry.

02 · No back-end code · where industry fit happensCompose

Datasets, forms, workflows, triggers, AI pages. An industry's specific process can usually be fully expressed here.

03 · Days, not weeksExtend modules

New business modules built to foundation conventions inherit CRUD, sensitive-field encryption, visibility scoping and capability codes.

04 · Clear extension contractsExtend the core

New model vendors, workflow node types and channels plug in without touching the engine core.

Working with us

Choose either, or combine them.

COMPLIANCE

Algorithm-filing authorization

Offering generative AI inside your own product means meeting China's rules for algorithms and generative AI services. We have completed algorithm filing (the official CAC listing prevails; the filing number is on our Brand Facts page). Under an authorization, your application can deliver AI features through our filed algorithms and services.

  1. Assess the scenario firstInternal use or a public-facing service; calling our service directly, or needing your own filing or registration — settled per scenario before we sign.
  2. Responsibilities in writingScope, permitted use, content-safety and AI-content labelling duties are written into the contract.
  3. Supporting controls includedSensitive-content rules and filtering, scenario grading, call audit trails and exportable compliance documentation.
  4. What we do not promiseNo "filing-free" promises, and no regulatory approval on anyone's behalf.
Ask about authorization →
CUSTOM BUILD

Custom development on the foundation

We plan, build, deploy, deliver and continuously upgrade AI-native application systems for enterprises.

We have the technical groundwork for this, but we complete a requirements and delivery-risk assessment before giving you a firm proposal.

  1. PlanMap the business, data and permission boundaries; choose the first scenario.
  2. BuildConfigure → compose → extend modules → extend the core, lightest first; no code where configuration will do.
  3. Deploy and deliverPrivate cloud or SaaS, licensed by edition and module; delivered and accepted in stages.
  4. Keep upgradingModel upgrades without business-code changes; mature industry instances become reusable templates.
Book an assessment →

The official CAC listing prevails for our algorithm filing; the filing number is on our Brand Facts page. Whether you need your own filing or registration is assessed per scenario and agreed in writing.

By type of organisation

One foundation; three different starting points.

SMEs · AI applications

What AI do SMEs actually need

SMEs rarely lack a model. What is missing is the step that puts AI into daily work: who answers…

Learn more →
Industrial parks · AI and partnership

What AI do industrial parks need

A park has two layers of AI need: the operator's own management and services (tenant services, …

Learn more →
Government & state-owned · AI and partnership

What AI do government and state-owned organisations need

For government and state-owned organisations the first questions are where the data lives, who …

Learn more →

A typical rollout

~1–2 weeksDeploy and get to know the business

Deploy, license, connect a model; set up organisation, roles and portals; get the most painful table running so people start entering real data.

~3–6 weeksCompose the industry process

Build the specific process — forms, AI steps, approvals, notifications, ledgers, dashboards — as a chain the business can accept directly.

~6–12 weeksDeep modules and integration

Extend modules where composition is not enough; integrate ERP / OA / finance; build the industry knowledge base; tune model bindings and fallbacks.

OngoingOperate and upgrade

Usage and cost visibility, model iteration, and mature instances turned into reusable templates.

Typical durations; the assessed proposal is what counts.

Boundaries: what we do not do

Saying what we do not do matters more than listing more features.

See the full platform →
  • Not a model-training platform: no training or fine-tuning; we are the orchestration and application layer, and private models plug in as self-hosted inference.
  • Not a full replacement for low-code: composition covers forms, workflows, dashboards and integration; rich real-time UIs and heavy graphic editing still need front-end work.
  • Uneven module maturity: resource center, work orders, AI hub, composition engine and licensing are the most polished; other modules' industry depth depends on the project.
  • Private deployments need on-site fitting: servers, PHP extensions and database versions vary, so we check the environment before delivery.
  • AI output needs human confirmation: approvals and external commitments are never delegated to AI.

Start with a requirements and delivery-risk assessment

Tell us about your business and data boundaries; we assess feasibility, risk and a staged plan before quoting.

Book an assessment → Book a demo