We Started Building Neural Networks Before It Was Cool
Back in early 2018, most businesses were still trying to figure out what machine learning actually meant. We were already knee-deep in convolutional architectures and transformer models—not because we wanted to be trendy, but because we genuinely believed these systems could solve real problems.
Nixelon came from a simple observation: neural network architecture matters more than people think. You can throw computing power at a problem all day, but if your network design is inefficient, you're just burning resources. We wanted to build smarter architectures, not just bigger ones.
What started as a small research team in Yilan has grown into something we're proud of. We work with clients across Taiwan and beyond, helping them understand how different neural architectures fit different challenges. Sometimes that means a straightforward feedforward network. Other times it's recurrent systems or attention mechanisms. The point is matching the tool to the job.
How We Got Here
A few turning points shaped who we are and what we care about
The Beginning
Three researchers tired of inefficient network designs decided to try something different. Started working on custom architectures optimized for smaller datasets—something most companies ignored.
First Real Breakthrough
Developed a pruning technique that cut inference time by 40% without sacrificing accuracy. Published our findings and suddenly people were paying attention to what we were doing.
Expanding Reach
Worked with our first major manufacturing client in Taiwan. Helped them implement quality control systems using computer vision. Realized we could actually make this stuff practical.
Education Focus
Started teaching workshops because too many talented people were intimidated by neural networks. We wanted to demystify architecture choices and show why they matter in real applications.
What We Actually Do With Neural Architectures
Most of our work involves helping organizations understand which network structure actually fits their data and constraints. That sounds simple, but it's where most projects go wrong.
We've spent years experimenting with different approaches—residual connections, attention layers, hybrid architectures. Each has trade-offs. Our job is figuring out which trade-offs make sense for a specific situation.
Sometimes clients come to us wanting the latest trendy architecture. We might recommend something simpler instead. Better to have a lightweight network that actually works than an overcomplicated system that looks impressive on paper.
We care more about inference efficiency and real-world performance than benchmark scores. That's just how we approach problems.
How We Work With People
Every project starts with understanding constraints—computing budget, data availability, latency requirements. Then we figure out what's actually achievable.
We don't promise magic. Neural networks are powerful tools, but they're still just tools. Our role is helping you use them effectively without getting lost in hype or complexity.
"I've seen too many projects fail because someone picked an architecture that sounded cool instead of one that fit the problem. That's what we're trying to prevent."
Understanding Your Constraints
We start by mapping out what you're actually working with—data volume, computing resources, accuracy requirements, latency limits. These factors determine which architectures are even worth considering.
Prototyping Multiple Approaches
Usually we'll test three or four different network structures with your data. Sometimes the simplest one wins. Other times you need something more sophisticated. Testing reveals what actually works in your specific context.
Optimization and Deployment
Once we've settled on an architecture, we focus on making it efficient—pruning unnecessary connections, quantizing weights, optimizing for your target hardware. Then we help you integrate it into your actual workflow.
Ongoing Refinement
Neural networks aren't static. As your data distribution shifts or requirements change, the architecture might need adjustments. We stick around to help with that evolution rather than disappearing after initial deployment.