Owned, on-premises AI isn't a pitch — it's already running. Here's what happened when we sat inside our first client's business and rebuilt their slowest task.
Report turnaround, down from 2–3 hours — a 90%+ cut.
Now NeoNir's AI implementation partner across multiple projects.
Monthly cloud or API bills — it runs on their own machines.
Every compliance report at NeoNir was assembled by hand. A single one ate 2–3 hours of an engineer's day — pulling raw plant readings out of scattered files and formatting them into one compliant document, every single time. Their best people were spending their hours on repetitive formatting instead of actual engineering, and the backlog only grew with the order book.
We mapped the reporting workflow first — exactly how a good report gets built — before any model was trained. Then we trained a private model on their own past reports and deployed it on infrastructure they own. It reads their existing plant data and produces the finished report; the engineer reviews and ships it, rather than assembling it from scratch.
No new software for the team to learn, no data leaving the premises, and no outside API being billed on every run. It works inside what they already use.
Turnaround dropped by more than 90%, the team got their afternoons back, and the AI keeps running with no cloud dependency and no recurring bills. We're now NeoNir's AI implementation partner across multiple projects — and that first deployment became part of our first wave: 3 clients closed in 15 days across D2C, electrical manufacturing and finance, with zero cold calls.
What took us an afternoon now takes minutes. Our engineers are back to engineering.