1.1
Semantic connector
Reads the ERP / MES / spreadsheets you already use — read-only — and produces a semantic profile, data-consistency report and ROI estimate.
PLATFORM
Platform capabilities
Four capability groups share one data model, one fact base and one permission system. Nothing is coded that can be configured; nothing is built that can be composed — which is how delivery time and total cost stay predictable.
4capability groups sharing one data model and fact base
12modules, delivered configure → compose → extend
2deployment modes, one codebase: private cloud / SaaS
CAPABILITIES
One foundation, four capability groups
Delivered in order: configure → metadata → compose → extend → new module. Nothing is coded that can be configured; nothing is built that can be composed.
01Intelligence foundationMake existing systems understandable and business needs describable
1.2
MxJSON protocol engine
Turns a one-sentence requirement into an executable MxJSON protocol: forms, flows, pages and reports are all protocol-driven.
1.3
Models & compute
Unified access to multiple model vendors, routed per scenario; quotas, metering, logs and failover in one place.
02SEO + GEO business engineFindable and citable by search engines and AI — and fully accounted for
2.1
Server-rendered website & store
Products, brands, topics and knowledge base are server-rendered and static-cached, bilingual, with JSON-LD, llms.txt and machine-readable fact sets; inquiry, order, payment and booking in one chain.
2.2
GEO workbench
Produces distribution kits by target mix (Doubao / Qwen / DeepSeek / Baidu AI), monitors whether each AI mentions you or a competitor, and audits robots and crawler access.
2.3
AI exposure → deal attribution
Source attribution (landing params / referrer / self-report / manual confirmation) lands on every event and lead: which AI brought the inquiry, booking or order — computed back to the piece of content.
03AI OfficeHand off the repetitive work
3.1
AI Office
Documents, spreadsheets, slides, PDF, OCR, images, audio and video — drafted from one sentence, downloadable as finished files.
3.2
Enterprise knowledge base
Turns company documents into a brain that answers questions — every answer carries its source.
3.3
Workflows
Cross-department processes run as drawn; AI capabilities attach directly to nodes.
04Business applicationsOperating systems that work out of the box
4.1
CRM & lead attribution
One inbox for leads, channel and AI-source attribution, dispatch and SLA, quotes and contracts, collections.
4.2
Delivery & service factory
Requirement → quote → project blueprint → milestones → acceptance → case authorization; AI engineering tasks run on feature branches.
4.3
Industry systems
Education & examinations, fire-safety training, B2B commerce, international sites and more — composed and deployed per customer blueprint.
DEPLOYMENT
Two deployment modes, one codebase
One core; choose the deployment that fits your data boundary and growth stage. Moving from SaaS to private cloud later does not mean starting over.
Book a demoPrivate cloud / dedicated instance
Runs in your own cloud or data centre; data never leaves your domain. Models via your own API keys (BYOK) or on-premise models.
SaaS multi-tenant
Ready on sign-up, with per-tenant data and quota isolation — ideal for starting with one team or one scenario and expanding.
Zero-config multi-site
Website, store, branch sites and international sites share one core; domain and language switch per site, content and facts stay single-sourced.
DELIVERY
See how the AI growth service plugs into this chain →
How we deliver
01
Understand the current state
Read-only connection to existing systems; we deliver a semantic profile, data-consistency findings and an ROI estimate before anything is built.
02
Blueprint and scope
Ordered configure → compose → extend → new module into a project blueprint and scope baseline; three quote tiers map to three scopes.
03
Milestone delivery
Every milestone ships a demonstrable result with acceptance criteria; AI engineering runs on feature branches, humans merge.
04
Acceptance and the flywheel
After acceptance and with customer authorization, results become case assets and fact cards — evidence for the next round of AI visibility.
PRINCIPLESOur delivery principles,
Our delivery principles,
built into the system, not the contract
Read-only assertions, SQL allow-lists, sample masking, full audit trails, no unsupported numbers, human-only approvals — not verbal promises, but gates in the code.
Read-only is asserted, not promised
After connecting we attempt a write on purpose — it must fail before we proceed.
Model-written SQL never runs directly
Allow-list validation: queries only, no multi-statements, no files or system tables; local sources get an extra sandbox.
Samples are masked before they leave
Phone numbers, ID numbers, emails and card numbers are masked before reaching any model; credentials are stored encrypted.
Every read and write is logged
Which table, how many rows, who triggered it and when — all in an exportable, auditable ledger.
No number without evidence
Unverified facts show as "unconfirmed"; comparison curves with too few samples are simply not drawn.
Approvals are never delegated to AI
Official facts, public content, quotes, contracts, publishing and customer cases are confirmed by a person in the system.
See what AI says about you today
60–100 buying questions across 4+ AI platforms, API and in-app results separated; baseline report and 90-day roadmap within two weeks.