Ruiguo Interactive provides AI planning, assembly, and on-site implementation for manufacturing companies. Founder Ye Chen brings 22 years of hands-on manufacturing IT and national-level Intelligent Manufacturing Pilot Demonstration project experience — all verifiable through official channels. Every project defines its acceptance metrics before implementation begins.
Diagnosis deliverables: your business problem list, prioritized recommendations, and items we advise against.
Anything unfit for your current stage is explicitly marked "not recommended yet."
Projects Ye Chen personally led at companies he served: industry and scale, role and team, and a one-line summary of what was delivered. Company names and operating figures are anonymized out of respect for former employers; verifiable detail available in person.
The four structural issues we encounter most often during diagnosis — unrelated to model selection, yet decisive for acceptance results.
Everyone says "AI empowerment," but nobody can answer: in this workflow, which exact step goes from 3 days down to 3 hours? Without a fixed metric, acceptance becomes one side's word against another's.
More than a decade of process data sits in the MES with inconsistent formats and definitions. Half of what the AI needs lives outside the systems; the other half lives in somebody's Excel file.
IT runs the project, the business watches from the sidelines, and IT takes the blame when things go wrong. Six months in, the business side loses interest and the project drifts into limbo.
Models, data, and workflows are all locked onto one vendor's platform. Three years later, switching costs more than the original purchase did.
We don't sell big-bang platform packages. Most engagements start with a modest diagnostic — spelling out both "where change is worthwhile" and "where you shouldn't touch anything yet" — before you decide whether to invest.
We walk the shop floor and the offices, look at the real processes, the systems, and how the reports are actually produced, and align regulatory and customer requirements along the way.
Install only the modules the current scenario requires. No ripping out existing systems — DataConnector links directly to your ERP / MES / LIMS / QMS.
Knowledge-base distillation, staff training, and results tracking. This phase only ends when the business side uses the system better than the IT side does.
Results are written into the contract. Yield, Cpk, audit-preparation hours, ESG rating gaps — the metrics go on the table, and fees are tied to outcomes.
Each domain is a complete business chain — assemble it on its own, or combine freely across domains
Full-process management from lead to payment
The full workflow from anomaly detection to audit trails
Courses, assessments, and certificates, automated end to end
Business data at a glance on one screen; reports no longer written by hand
An AI customer service agent that keeps learning, online 7×24
End-to-end ESG management for export-oriented companies
All business components share one runtime platform: model access, usage metering, licensing and billing, and data security are guaranteed uniformly at the platform level
In each industry the hard part isn't whether to adopt AI — it's the regulations, audit criteria, and customer requirements. Every solution can run on your existing computing infrastructure.
Dental clinic AI enablement has its own dedicated solution · View the dental clinic solution →
The answers state facts and boundaries — no unverifiable lines like "we're very professional."
Start with a business action that can be quantified and can show a measurable difference within a quarter — not with an "AI strategic plan."
Three criteria: How many person-days does this action take today? Who owns this action? Can its output be formally accepted? Only when all three have clear answers is it a fit for the first scenario.
It depends on scenario complexity. Document work, knowledge retrieval, and report drafting typically reach a first working version in 4 to 8 weeks; process-quality scenarios involving production data usually take 3 to 6 months, because they require data accumulation and full-cycle validation.
We write the acceptance criteria and timeline clearly into the contract. If the metrics haven't moved in three months, we will proactively recommend stopping — that's not failure; it means the scenario was never a fit.
DDW is designed to be vendor-agnostic: models connect freely from cloud or local deployments, with multi-level fallback; databases include PostgreSQL, MySQL, SQLite, Oracle, and other mainstream engines; on the IM side it connects uniformly with DingTalk, Feishu, and WeCom.
Enterprise data always stays in an environment you control, and the model layer is replaceable. That is a product-design decision, not a marketing line.
DataConnector links directly to your existing systems — no overhaul and no migration required. ERP, MES, LIMS, and QMS are all within the supported range.
One caveat: before connecting, we run a data availability assessment. If the critical data was never captured in the first place, that is the problem to solve first — no tool swap will bypass it.
Entry is tiered: L0 Free (full platform features available, try it first); L1 Base Platform (platform license + industry plugin packs); L2 Intelligent Services (AI operations, knowledge-base hosting, etc., billed by processing volume); L3 Results Delivery (pay by business results).
Specific quotes are assessed against scenario scope, deployment mode, and data conditions, and the formal proposal is authoritative. We don't publish fixed prices on a public page, because they carry no reference value for companies of different sizes.
Acceptance metrics are defined jointly before the contract is signed, and data-collection methods and statistical criteria are written into the contract — results are never defined by our side alone.
To be candid: enterprise IT and AI adoption is not a pure technology problem. Outcomes are the joint product of the company's internal reform commitment and execution, with responsibility boundaries on both sides. That is exactly why the diagnosis stage includes an organizational-readiness assessment: if your company is not yet in a position to invest, or internal consensus has not formed, we will say so clearly at the start — no wasting anyone's time.
Yes. Team members have complete application and implementation experience with national-level Intelligent Manufacturing Pilot Demonstration (plant-level) projects, integration-of-IT-and-industrialization pilot enterprises, 5G fully-connected factories, and provincial dual-benchmark intelligent manufacturing projects — all verifiable through the official channels of the competent authorities.
What we usually take on is the stretch that connects the application to the actual delivery — the application materials must satisfy the review experts, and once the designation is granted, the system still has to genuinely run. Doing only one of the two adds little value.
The titles are the exact questions clients ask. Every article may be freely republished or quoted with attribution.
An AI coding tool built for programmers, retrained into the company's operations team: instant document retrieval, cross-day memory handoffs, scheduled midnight publishing, and multi-agent collaboration. Includes three tests for enterprise adoption.
An AI value-quantification study jointly released by the China Academy of Industrial Internet and JD Industrial: evaluation shifts from "how much AI you use" to "how much money it earns back." The report's public methodology, plus a practical four-step estimation path.
Breaks "should we do it, what do we do first, how do we know it worked" into checkable items — with an alternative path for whenever an item can't be satisfied.
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Companies at different stages need completely different levels of investment. We lay out every entry point, so you can walk the path yourself before deciding how much to put in.
Try the full platform for free — run it yourself and see whether it solves your problem.
Platform license plus industry plugins: validate one clearly defined action with controlled investment. Start from as few as 3–5 components and scale as you see fit.
Covers one full business chain, including knowledge-base hosting, AI training, and other on-site guided adoption services.
Yield, process capability, audit-preparation hours, ESG rating gaps — the metrics go into the contract, and fees are tied to results; acceptance criteria and shortfall-handling clauses go into the contract too.
For typical investment ranges and timelines, see Services & Pricing · first-order fees are fully deductible within 90 days
If it's not a fit, I'll say so outright. Leave your details and a human will reply within 24 hours on weekdays.
