The half of AI that won't run - we handle it ourselves
Compute assembly · GPU upgrades · device integration · ongoing tuning after installation
It is that it won't run - and any one of these five can leave a finished plan stuck at the demo stage
The machine can't keep up - the moment a local model runs, the computer freezes
The GPU isn't enough - VRAM falls just short and image generation or inference throws errors
Devices won't connect - cameras, industrial PCs, printers and barcode scanners refuse to talk
The intranet won't link up - domestic operating system environments, no external network, legacy systems side by side
Nobody looks after it afterwards - it runs fine on launch day, but three months later nobody can tune or fix it and it quietly falls into disuse
Software-only providers can only tune parameters remotely, and when hardware fails they leave you to find someone.
The people who write our code can also build the machine, tune the devices and come on site.
From speccing to connecting devices to adapting your intranet - end to end
We know which model needs which GPU.We configure to just enough for what you actually run , without overspending on a setup that cannot run, and without saving a few thousand yuan only to find it runs nothing.
If your current PC can't keep up, upgrade it directly . We look at GPU, memory, power and cooling together, and fix case airflow and stability problems along the way.So you don't have to study spec sheets.
Cameras, industrial PCs, printers, webcams, barcode scanners, access gates -these are the things that won't connect, and we connect them on site. So you don't have to dig through drivers and manuals yourself.
Domestic operating systems, no external network, coexisting legacy systems, multi-segment isolation - these most common blockers , and they are exactly what we deal with every day in operations.
The most common waste in this industry isn't "it won't install", it's "nobody looks after it once it's in"
Why do we talk about after-sales separately? Because the vast majority of AI projects don't fail on technology, they fail on nobody being able to pick it up after go-live . Machines age, systems update, models change generation, people leave - all of that needs someone to look after it.
You may find your answer here on compute configuration, device integration and after-sales tuning