No "AI transformation" projects.
Fix your single most painful workflow — with a measurable pilot result in weeks.
I am Cai Yulin — mechanical & electrical engineering background, 20+ years across R&D, production, and sales in manufacturing since 2005. Today I help factories apply AI to one painful workflow at a time: on-site diagnosis, build, and acceptance — handled end-to-end by one accountable person.
Services on this site are provided by Guangdong Qingxian Technology Co., Ltd., delivered by engineer Cai Yulin. Case patterns below are drawn from published industry case collections (Datawhale "100 Real Enterprise AI Cases" and the fde-wiki manufacturing chapter). These are not my own deliveries — my service is reproducing these proven patterns on your floor, verified with your data.
Which workflow hurts the most?
The number-one reason AI projects fail in manufacturing is trying to digitize everything at once. We pick one workflow — the slowest, costliest, most error-prone — and fix that first.
Expertise walks out the door
Machine setup, material ratios, and fault diagnosis live in a few veteran minds. When they retire, new hires take months to become productive.
📐Slow quotations lose orders
High-mix, low-volume quoting means drawings, process planning, and cost estimates — days per order while competitors answer in three.
📦Materials that cannot be found
The same part has three names across warehouse, drawings, and ERP. Wrong purchases and duplicates quietly drain cash.
📊Reports everywhere, no visibility
The ERP has data but no business view. Problems surface when someone shouts — too late.
🔍Manual inspection, surprise downtime
Visual inspection misses defects; machines fail without warning. A stopped line is expensive by the hour.
🧮Is it worth it at all?
Vendors oversell. You need a number you can defend before committing budget.
Four manufacturing scenarios, already validated
Capturing veteran expertise & training new hires
Published case collection, No.1 — a German parts manufacturer (~70–80 staff)Their situation: critical know-how lives with a few masters. Training is word-of-mouth; when a master leaves, the line suffers.
- Interview and structure the veterans' tacit knowledge into a searchable knowledge base
- Build a two-tier Q&A: ask AI first, escalate to the master for hard cases
- Iterate with real questions from real trainees; fill the gaps
The published case reports document-parsing and question-recall accuracy meeting agreed acceptance criteria in a scoped pilot. My job is to reproduce this rigor on your floor, with your data.
Faster quotations for engineered-to-order parts
Published case collection, No.17 — a flexible manufacturing plant (a few hundred staff)Their situation: quoting high-mix orders takes days — drawings, process routes, cost estimates. Slow quotes lose orders; wrong quotes lose money.
- Parse drawings (PDF/CAD) and requirement documents to extract key parameters
- Decompose requirements, recommend process routes, estimate costs with human review at critical nodes
- Draft quotations — every single one reviewed by a human before it goes out (the case's own design: AI drafts, humans gate)
The published case reports the majority of related workload eliminated. Verified through a four-layer evaluation: response speed, quote usability, drawing-parsing accuracy, business value.
Purchasing: find the right material, stop duplicate buys
Published case collection, No.8 — an industrial company (15 teams on one legacy ERP)Their situation: messy data and weak search. The same item wears three names, so buyers order wrong or buy twice.
- Weeks of on-site interviews to find the root cause before proposing anything
- Clean and unify material naming; semantic search that understands plain language
- Embed precise lookup into the purchasing request flow
The published case measures success only by business outcomes — fewer wrong buys, fewer duplicates — never by model metrics. I adopt the same standard.
Operational transparency & proactive alerts
Published case collection, No.20 — an automotive supply-chain companyTheir situation: the ERP runs and reports exist, but the owner cannot see the business end-to-end. Delays and overruns surface through hallway talk.
- Start from the owner's questions, not from what the system can produce
- Connect the order lifecycle into one view, from PO to delivery
- Surface exceptions proactively — delays, overruns, quality drift
The published case ships the basic capability and honestly states that business-level impact needs longer observation. I publish the same honesty on my own projects.
Visual inspection & predictive maintenance (second-stage direction)
Methodology: fde-wiki manufacturing chapterCamera-based defect detection on plant-floor machines and failure prediction from equipment data are the two most requested directions. They require access to your MES/PLC systems and on-site commissioning — a scoping visit comes first.
Appendix: eight proven manufacturing AI scenarios
Source: China Manufacturing AI FDE Pathway Whitepaper (2026), ch. 4 — Scenescape × Daren Think TankCompiled by the whitepaper from public case disclosures. All figures are company-published reference points, not promises — for your plant, everything is re-baselined during diagnosis.
🧪 Fermentation control
Control precision 88%→97%, output +3–5% over control group (Chuanning × ManuDrive, annual-report disclosure)
🏭 Smart factory design
Design cycle 180d→7d, cost 3M→0.5M RMB (project retrospective)
🧵 Textile energy
Started from 60 data points + 300 lines of code; five-figure monthly savings (typical-plant estimate)
📅 Flexible scheduling
Equipment utilization +25%, on-time rate 70%→95%+ (public case)
🔍 Quality inspection
~5x throughput, miss rate below 0.3%, traceable conclusions (FDE delivery)
📦 Supply chain
Inventory turnover +30%, line stops −80% (project retrospective)
📞 Supplier chasing
4–5 h/day→30 min, ~17 h/week freed (equipment-maker trial)
📐 Drawing review
5 drawings 60 min→2–5 min; ~760K RMB/year loss avoided (6-month trial)
See your workflow on the map? Bring your scenario — I follow the whitepaper's SLP method (scene → link → pilot) to give you a grounded go/no-go read.
The pilot: a few weeks, one workflow
Diagnose first, build second, let the numbers talk. Pilot fees are fixed in writing before signing; whether to expand is decided only after acceptance — by you.
Week 1 · On-site diagnosis
Interview the owner, leads, and operators; quantify the baseline of the most painful workflow (hours per quote, error rates, ramp-up time).
Weeks 2–3 · Build
Build the AI-assisted flow for that one workflow only (knowledge base / parsing / search / reporting). Data stays on-premise where possible.
Week 4 · Acceptance
Compare against the Week-1 baseline in a written report: what moved, by how much, and what did not — labeled honestly.
After the pilot · Your call
If it works, a rollout roadmap for adjacent workflows. If it does not, an honest report on why and what preconditions are missing. No pushing.
Questions owners ask most
What is an FDE, and how is this different from a software vendor or a big consultancy?
FDE stands for Forward Deployed Engineer — an engineer who works inside your operations from diagnosis through build to acceptance, instead of handing over to a delivery team after the sale. Unlike a software vendor, I do not sell licenses or bill implementation days; I am accountable for the outcome of the one workflow we target. Unlike a consultancy, I build the thing myself — no slideware.
Who are you, and can one person really do this?
I am Cai Yulin (蔡宇林) — mechanical & electrical engineering background, 20+ years across R&D, production, and sales in manufacturing since 2005, now working as an independent AI implementation engineer. One person plus a well-built AI toolchain covers work that used to need a small team. My delivery records — including raw run data, issues found, and fixes — are kept end-to-end and available for review before we sign anything.
Will my process and business data stay confidential?
The default architecture keeps data inside your plant: knowledge bases, models, and retrieval run on your own infrastructure. If you do not have usable local servers or a private cloud yet, I assess first and we discuss options — I will not start by selling you hardware. I only access the data necessary for diagnosis and debugging, under NDA.
What if the pilot does not work?
Three possibilities, all stated up front: wrong target (mitigated by quantifying the baseline during diagnosis), missing preconditions (e.g., dirty data — reported honestly so we fix the foundation first), or poor fit (reported honestly — I will not push a phase two). Pilot fees are fixed in writing before signing. If the pilot fails, that fixed fee is the entire downside.
Preferred contact
Add me on WeChat
WeChat is the fastest way to talk. Scan the code or search my ID, describe your most time-consuming task, and I will take a look before we set up a call.
caishide When adding, mention: your industry, the slowest workflow, and roughly how much time it costs daily. Three sentences is enough — I will give you a first read.
Not sure which workflow to target first?
A free 30-minute conversation about your most painful process — you keep the read even if we never work together.