Hoarding data ≠ Digital transformation
Data sitting in DCS or SCADA isn’t the same as data being useful. Data without definition, structure, or a path to decisions is just digital clutter—and increasingly, technical debt.
Creart Lab is a Taiwan-based data and AI consultancy. We turn data into decisions, and AI ideas into production systems. From machine tools to process industries, from retail to energy—what we keep seeing on the ground is the same script: the gap isn’t technical. It’s the people who can translate between business language and AI language.
Your ERP, MES, Excel files, documents, and operational data are disconnected across teams.
Dashboards show what happened, but not what to do next.
Critical know-how lives in people’s heads, not in reusable systems.
AI demos work in isolation but fail to connect with real workflows and systems.
Your team wants to adopt AI but does not know which use case should come first.
Data governance, business intelligence, predictive modeling. We turn data scattered across systems into insights leadership actually uses. We don’t just build dashboards—we start with the right questions.
LLM applications, Agentic RAG, computer vision. From problem framing through tech selection, POC validation, and production deployment—we help you choose the right tool, avoid the traps, and embed AI into operations.
ERP, MES, IoT, APIs. When AI models can’t reach production, the cause is rarely the model itself—it’s the plumbing. We handle the integration work others would rather avoid.
Creart Lab is a fit when your organization needs more than an AI prototype. We help translate operational problems into validated use cases, working systems, and measurable business outcomes.
Not a big-bang rollout. The fastest, lowest-risk way to validate whether AI can actually work in your operations.
Enterprises with existing data but uncertain AI feasibility or ROI.
Validate technical feasibility, business value, user adoption, and the next-step rollout plan.
8–12 weeks
NT$ 350K–700K
Adjusted by scope.
Problem diagnosis, data assessment, solution design, MVP prototype, ROI validation report, rollout roadmap.
Seven common patterns:
From machine tools to process industries, from semiconductors to retail—across 50+ projects, the script is nearly the same.
Data sitting in DCS or SCADA isn’t the same as data being useful. Data without definition, structure, or a path to decisions is just digital clutter—and increasingly, technical debt.
The blocker isn’t ROI—it’s an earlier question: “Will the customer pay for this?” AI must solve a real problem, not just ship a feature.
Equipment prices are anchored by the market. Differentiation comes from service capability—improving yield, reducing energy use, eliminating downtime. In demanding industries, AI is taken seriously.
Start with the financials—find where you’re losing the most, fastest. Adoption should be modular, edge-deployed, and validated cheaply. Not a rip-and-replace.
A PM who speaks both shop floor and AI is the role that turns veteran intuition into learnable, testable, transferable assets. This is the linchpin of AI rollout success.
“AI doesn’t create competitive advantage. It only amplifies the strengths and problems your company already has.”
— CREART LAB · A core observation from 50+ projects
The cases below are anonymized for confidentiality. Specific clients and impact metrics can be shared under NDA.
Production data was scattered across ERP, MES, and spreadsheets, making capacity, delivery, and exception tracking difficult.
Built data pipelines, decision dashboards, and scheduling support logic across systems.
Management dashboards, exception tracking workflow, scheduling support module.
→ Reduced manual reporting effort and improved visibility across production and planning.
Root-cause analysis relied heavily on manual experience, making scrap and rework difficult to control in real time.
Integrated process parameters, inspection data, and historical abnormal records to build anomaly detection and analysis models.
Anomaly alert model, scrap analysis dashboard, prioritized improvement insights.
→ Faster response, helped management identify major sources of operational loss.
SOPs, specifications, customer requirements, and internal documents were scattered across systems and folders.
Built document parsing, vector search, permission-aware retrieval, and RAG-based Q&A workflows.
Enterprise knowledge base, AI assistant, answer traceability, query history.
→ Reduced manual search time and improved knowledge transfer across teams.
We don’t ask “which AI technique would you like.” We ask “where is the business leaking the fastest.” We triangulate from financials, process, and the field to surface the right priority.
You get two artifacts—a technical spec for engineers and an ROI projection for leadership. Before we write any code, conditions for success and failure are written down.
Every two weeks, real users come see the demo and recalibrate. We don’t disappear for six months and emerge with a surprise—that’s the fastest path to failure.
What you receive isn’t just a system—it’s the readable rationale behind every model decision. Your IT team can take over operations without being chained to us.
Four engagement models that flex with your project stage and maturity. Most enterprise clients start with a POC, then move into production rollout.
Prioritizing use cases and assessing data readiness.
Validating feasibility, ROI, and user adoption.
Integration, deployment, and scaling.
Ongoing AI roadmap, data governance, and productization support.
We’re neither pure consultants nor a dev shop. With a 1:1.4 consultant-to-engineer ratio, the same people who frame the business problem also ship the production system—closing the “great strategy, nobody can build it” gap that breaks most projects.
POCs are fixed-cost and bounded. If validation shows AI isn’t the right tool, we tell you—and propose alternatives. We don’t push unsuitable solutions just to extend the engagement.
All code, documentation, and models are fully delivered, mostly built on mainstream open-source. After we leave, your team can take over—not chained to any vendor, including us.
We discuss every project in your finance team’s language—downtime avoided, scrap reduced, headcount freed. Technology is the means; business impact is the end.
For the first conversation, you only need to share three things—we’ll figure out the rest together.
Downtime, scrap, reporting workload, knowledge retrieval, scheduling, forecasting accuracy. A single sentence is enough.
ERP, MES, Excel, PDFs, SOPs, sensors, databases, cloud systems. If you’re unsure, we’ll help inventory it during diagnosis.
An MVP, an ROI report, a dashboard, a validated workflow, a rollout roadmap. Even a vague answer works.
Tell us where your AI plan is stuck on translation. We’ll respond within 24 hours with directional advice and next-step recommendations.