We started AdvancePulse in early 2022 because we kept seeing the same problem—companies investing in AI projects that never made it past proof-of-concept.
Three engineers with different backgrounds sat down in a Tainan coffee shop. One had spent years in computer vision, another in natural language processing, the third in production systems. We'd all watched promising AI initiatives crumble under the weight of messy data, unclear requirements, and the gap between research papers and actual deployment.
So we built something different. Not an agency that promises magic, but a development partner that focuses on practical implementation.
Our approach isn't complicated. We help teams integrate AI capabilities into existing systems without replacing everything they've built.
Last year, we worked with a logistics company in Kaohsiung that needed route optimization. They'd tried an off-the-shelf solution that kept suggesting impossible routes—turns that trucks couldn't make, roads with weight restrictions. We spent two weeks just understanding their constraints before writing a single line of code.
The final system wasn't revolutionary. It combined traditional algorithms with machine learning for traffic prediction. But it reduced fuel costs by 18% because it accounted for real-world limitations.
That's the work we care about. Not chasing benchmarks or academic metrics, but building tools that people can actually use on Monday morning.
Small team, diverse expertise. We keep it that way intentionally—easier to stay focused on quality when you're not managing layers of hierarchy.
Lead AI Engineer
Spent eight years building recommendation systems before realizing most companies needed something simpler. Now focuses on practical implementations that teams can maintain without PhD supervision. Believes the best AI solution is often the one that runs reliably at 3 AM.
Senior Developer
Background in computational linguistics and a talent for explaining technical concepts without jargon. Runs our client workshops where we figure out what problems actually need AI versus what just needs better data management. Has saved clients thousands by talking them out of unnecessary complexity.
Three principles guide everything we build. Simple rules that keep projects on track and prevent the scope creep that kills most AI initiatives.
Before discussing models or algorithms, we spend time understanding what data actually exists. Not what should exist, or what might exist someday—what's available right now. Half our discovery phase involves data audits that prevent expensive surprises later.
We deliver working features every two weeks. Not demos or prototypes, but production-ready components that teams can use immediately. This approach lets clients see value quickly and change direction if priorities shift without throwing away months of work.
Every decision, every trade-off, every architectural choice gets documented in plain language. When we finish a project, clients receive complete technical documentation and training materials. No vendor lock-in, no dependency on us to maintain basic functionality.
We're selective about projects. Right now, most of our work falls into specific areas where we've developed deep expertise through repeated implementation.
Quality control systems for manufacturing, primarily in electronics and textiles. We've built defect detection models that work with existing camera infrastructure—no need to replace equipment that's already paid for.
Recent project involved identifying fabric defects for a textile manufacturer. Their previous system flagged everything as suspicious. We trained a custom model on their specific materials that reduced false positives by 84%.
Document classification and information extraction from technical documents. Many Taiwan manufacturers deal with multilingual documentation—Chinese, English, Japanese specifications all mixed together.
We build systems that can parse these documents reliably, extract key specifications, and flag inconsistencies. Not glamorous work, but it saves procurement teams hours every day.
