AI-channel Growth Center: get your business into Doubao, Qwen and DeepSeek answersLearn more ›
Buyers ask an AI before they decide who to call. We get your business named correctly in that answer — rendering your site and knowledge base into content AI assistants can read and cite, and accounting for the chain from "who asked an AI about you" all the way to the deal.
One foundation answers "how does AI get into the enterprise"; one engine answers "how does the enterprise get into AI". Both live in one system, with a single source of data and facts.
The semantic connector reads your ERP / MES / spreadsheets read-only, the MxJSON protocol turns requirements into executable flows, and models and compute are unified and routed per scenario.
Website and store are server-rendered, static-cached and bilingual, with JSON-LD, llms.txt and machine-readable fact sets. Brand facts go public only after human verification.
Track mentions, recommendations and citations per AI platform; attribute every lead to its source; run quoting, contracts, delivery, acceptance and case evidence as one chain.
AI Office drafts from one sentence, every knowledge-base answer carries its source, and AI attaches directly to workflow nodes. CRM, quoting, delivery and industry systems compose per customer blueprint — private cloud or SaaS.
Delivered in order: configure → metadata → compose → extend → new module. Nothing is coded that can be configured; nothing is built that can be composed.
Reads the ERP / MES / spreadsheets you already use — read-only — and produces a semantic profile, data-consistency report and ROI estimate.
Turns a one-sentence requirement into an executable MxJSON protocol: forms, flows, pages and reports are all protocol-driven.
Unified access to multiple model vendors, routed per scenario; quotas, metering, logs and failover in one place.
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.
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.
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.
Documents, spreadsheets, slides, PDF, OCR, images, audio and video — drafted from one sentence, downloadable as finished files.
Turns company documents into a brain that answers questions — every answer carries its source.
Cross-department processes run as drawn; AI capabilities attach directly to nodes.
One inbox for leads, channel and AI-source attribution, dispatch and SLA, quotes and contracts, collections.
Requirement → quote → project blueprint → milestones → acceptance → case authorization; AI engineering tasks run on feature branches.
Education & examinations, fire-safety training, B2B commerce, international sites and more — composed and deployed per customer blueprint.
One foundation; industries differ only in module mix and blueprint. Typical scenarios that existing modules compose into today.
Read-only ERP / MES and product documents, private knowledge base and AI service assistant, every answer cited.
Book a demo →Courses, item banks, exams and certificates from the industry module; AI-generated items and grading take effect after human approval.
Book a demo →Training, assessment, audit trail and expiry reminders in one chain; evidence exportable for audits.
Book a demo →Products, brands, topics and knowledge base server-rendered and bilingual; inquiry → order → payment → booking, with llms.txt and fact sets.
Book a demo →Attribution (including AI sources) → lead pool → three-tier quotes → contract → collections; every AI-sourced inquiry accounted for.
Book a demo →Website, branch and product sites share one core and fact base; sites are managed centrally and published to llms.txt and JSON-LD.
Book a demo →We build a brand-fact system that AI assistants can recognise, trust, recommend and verify — and turn the decision-stage demand AI generates into traceable leads, opportunities, projects and long-term customer value.
Grade the buying questions your customers ask AI into S/A/B/C tiers and bind each to target facts and a landing page.
Sample across multiple AIs with API and in-app results kept separate: who gets recommended, who gets cited, what is wrong.
Produce content only for gaps with commercial value; every number is bound to a verified fact and its evidence.
Attribute leads to the specific AI and piece of content; quotes, contracts, delivery and acceptance become new evidence.
Real customer questions → Question & intent library → AI visibility sampling → Fact & source gap analysis → Content production & approval → Website & authorised channels → Re-testing & experiments → Landing page / form / phone → CRM diagnosis & 3-tier quotes → Contract scope baseline → AI engineering & delivery → Acceptance, authorisation, evidence
Your buyers now ask Doubao, Qwen and DeepSeek who to buy from. This system does two things: measure what those AIs say about you today and whose sources they cite; then turn your missing facts into corpus the models can actually ingest, distribute it where they source from, and track whether it gets cited. Subscribe and start — no in-house GEO team required.
See plans →Build a graded S/A/B/C library of the buying questions your customers really ask, then sample them repeatedly across multiple AIs under fixed conditions. API sampling and in-app tests are always reported in separate columns — merging them into one number is self-deception.
Most tools stop at a gap report. The corpus center continues: gap → master corpus generated only from verified fact cards → platform versions ordered by the corpus map of your target AIs → a person publishes and registers the URL → indexing push → automatic re-test → citation reflow. Every piece can answer whether an AI actually cited it.
Authorised resellers buy at 50% of list price — half the margin stays with you; referral partners who only pass leads get 20%. Single tier, no joining fee, settled on real collections.
Every tier states its deliverables and acceptance criteria. We do not sell "guaranteed rankings" — only verifiable fact-building, monitoring, content, conversion and the business loop.
Baseline of 60–100 buying questions, sampling across 4+ AI platforms (API and in-app separated), competitor / source / fact-error inventory, 90-day roadmap.
Book an auditBrand-fact building, content production and approval, publishing on your website and authorised channels, scheduled re-testing, landing pages with lead attribution, monthly report.
See the programPrivate cloud / dedicated instance: knowledge base, AI service and sales assistants, CRM, quoting and contracts, project delivery, case-evidence flywheel.
Book a demoMonthly monitoring, content refresh, optimisation experiments, customer reports, lead reviews, service allowance.
Talk to usQuestion coverage, sampling frequency and method, content volume and evidence quality, lead handling and attribution rules, report completeness — every item is acceptable and auditable.
"Guaranteed Doubao ranking / guaranteed recommendation / guaranteed #1". AI answers vary by platform, time and randomness; anyone promising otherwise is betting you will never re-test.
Automating consumer AI accounts, bypassing CAPTCHAs, scraping consumer pages at scale, fake reviews, fabricated media, hiding AI-generated labels.
Numbers, qualifications and customers come from human-verified fact cards; expired or withdrawn ones drop off automatically. This is also the authoritative source when AI assistants cite us.
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.
After connecting we attempt a write on purpose — it must fail before we proceed.
Allow-list validation: queries only, no multi-statements, no files or system tables; local sources get an extra sandbox.
Phone numbers, ID numbers, emails and card numbers are masked before reaching any model; credentials are stored encrypted.
Which table, how many rows, who triggered it and when — all in an exportable, auditable ledger.
Unverified facts show as "unconfirmed"; comparison curves with too few samples are simply not drawn.
Official facts, public content, quotes, contracts, publishing and customer cases are confirmed by a person in the system.
MianxiangAI is operated by Shanghai Paixu Network Technology Co., Ltd., a company registered in Shanghai in September 2011, with unified social credit code 9131011658212749XB. It holds two registered software copyrights covering the MxJSON business description language and its protocol execution engine. Company facts are published at /en/facts with their sources. Read more →
The trial is free for 14 days, with no payment method and no automatic charge. It covers 20 buying questions and API sampling on 2 AI platforms, one round a week. On day 7 you get a visibility baseline and gap list: how often you are named, how you are described, what is stated incorrectly, and where the gaps are. Fact cards and the corpus center are read-only during the trial. Read more →
Every change is measured by a matched re-test: same questions, same platforms, same sampling windows, one variable at a time. Citations count only on an exact normalised-URL match; domain-level resemblance does not count. Rounds that moved nothing are recorded as such. Read more →
That is the case we are set up for. Chinese assistants draw on Chinese-language sources that most foreign sites never produce, so the work is establishing a verifiable Chinese-language fact layer about your company and publishing it where those crawlers actually read. Filing, hosting and platform-account requirements differ by channel and we scope those before quoting. Read more →
Usually one of three things: there is nothing machine-readable about you online; there is content but it sits where crawlers cannot reach it; or there is content that contradicts itself, so the model cannot settle on a claim. The three need different fixes, so we run a baseline sample before changing anything. Read more →
Turn the questions your buyers actually ask into a question set, then ask them repeatedly across assistants at different times of day and record each answer: whether you were named, how you were described, and which pages were cited. A single result proves nothing; repeated sampling is the data. Read more →
近日，上海派许网络科技有限公司正式推出 派da · 企业AI工作搭档 V1.0。 派da基于：面向AI · MianxiangAI®｜企业级AI新智基座 打造，面向企业员工日常办公与业务协作场景，提供智能化工作支持能力，帮助企业进一步提升信息处理效率、知识应用效率与业务协同效率。
GEO 效果怎么衡量：提及率、推荐率、引用率与归因的口径GEO 的效果可以衡量，前提是口径先定好。这篇说明常用的几个指标怎么算、采样怎么做才可信、"被引用"和"已收录"如何认定，以及怎样判断一次变化是不是你的改动带来的。
AI 里搜不到你的公司？一份企业可以自己做的诊断清单在 AI 助手里搜不到自己的公司，或者被说错了，原因通常可以归到几类。这份清单按五个步骤带你自查，每一步都说明怎么查、查到问题先做什么。
60–100 buying questions across 4+ AI platforms, API and in-app results separated; baseline report and 90-day roadmap within two weeks.