The real bottleneck
isn’t AI engineers.
It’s translators.

Three core services: Data Analytics (data governance, business intelligence, predictive modeling), AI Solutions (LLM applications, Agentic RAG, computer vision), and System Integration. We accompany clients from problem definition through POC validation to production deployment.

It varies widely by scope. A typical POC pilot runs NT$350K to NT$700K and validates ROI within three months. Full plant rollouts for mid-to-large enterprises typically range from NT$3M to NT$15M, phased over 6 to 18 months. We recommend starting small, validating, then scaling.

Yes, but in phases. Data governance can itself be the first phase of an AI initiative. We’ll inventory existing data, clean it, and define key metrics—typically a 4-to-8-week effort. Only after the data foundation is solid does AI modeling produce real value.

A typical SI takes requirements and builds. We work backward from business problems to design solutions. Our core value is being a translator—turning vague pain points into measurable technical questions, and turning model output into decision tools the field will actually use. We are a consultant + engineer bilingual team, not a development outsourcer.

POCs are scoped with fixed cost and bounded scope, so the maximum exposure is predictable. If validation shows AI isn’t the right tool for the problem, we tell you honestly and propose alternatives—process improvement, sensor deployment, or a different technical approach. We don’t push unsuitable solutions to extend contracts.

It depends on the problem. For extracting information from unstructured data (documents, conversations, reports) or generating content, LLMs fit better. For precise prediction from structured data (production data, transactions), traditional ML is usually more stable and lower cost. Many real-world cases are a hybrid—we’ll help you choose during diagnosis.

RAG (Retrieval-Augmented Generation) lets an LLM answer based on specified documents but only does single-shot retrieval. Agentic RAG adds agency—the AI plans multi-step queries, calls tools, validates answers, and iterates. It suits complex enterprise knowledge bases and decision-support scenarios.

Our most common arrangement is mixed staffing—Creart owns AI and data engineering core development, knowledge transfer, and architecture; your IT team handles day-to-day operations and ongoing extensions. At project end, all code, documentation, and models are fully delivered. We don’t lock you into our stack.

We follow a minimum-data principle—we only touch fields essential to the project, and sensitive data can be processed in-house without leaving your environment. NDAs are signed before every project; staff are trained in information security. For high-sensitivity clients, we offer on-premise deployment of open-source models so data never leaves the facility.

An AI translator (AI PM) speaks both the language of veteran operators (intuition, tacit knowledge) and engineers (algorithms, models). They turn vague field experience into testable hypotheses and turn model output into tools the field can actually act on. Regular PMs manage timeline and budget; an AI translator also owns technical feasibility, human-AI workflow design, and cross-functional translation—the linchpin of AI rollout success.

We’ve delivered projects across semiconductor packaging and testing, energy, electronics manufacturing, retail, logistics, machine tools, and more. Vocabulary differs by industry, but the translator’s methodology is shared.

Book a 30-minute free consultation; we respond within 24 hours. Describe your business challenge, current data state, and the problem you want to address—we’ll provide directional advice and propose a workable engagement model. Email [email protected] or use the contact form below.