What this is actually for
Every AI pilot dies on the invoice, not on the technology. The bill grows every single time the automation succeeds, which is a strange thing to punish.
What we built for ourselves
We bought the box. There's an NVIDIA DGX Spark on a desk in our shop running the models that read our receipts, score our photos and route our trucks — on our own power bill.
We split the work into three tiers: local for volume, cloud for judgment, frontier models for the rare expensive call.
We instrumented cost per workload, so tier decisions get made on measured spend instead of instinct.
What you get
- —A three-tier workload map: what runs local, what belongs in the cloud, what justifies a frontier model
- —Hardware sizing against your real throughput, not a spec sheet
- —Installation, wiring into your existing systems, and fallback behaviour for when the box is unreachable
- —Per-workload cost instrumentation so the tiering stays honest after we leave
Hear how it got built
We recorded the build of each of these, including the parts that didn't work.
- Episode 3 — SSH Into The Sparkrecording
- Episode 10 — Racing Fuel In A Lawn Mowerrecording
Episodes publish as they're cut. Nothing here is a teaser for something that doesn't exist — the systems are already running.
What this doesn't do
If your volume is low, buying hardware is the wrong move and we'll tell you so on the first call. There's a break-even point below which a cloud API is genuinely the right answer. We'd rather lose the sale than sell you a box that idles.
What it runs on
Built on NVIDIA hardware and software we own and operate, not a reseller relationship.
Where it's going next: Two external deployments, then a published cost-per-workload benchmark.
A member of the NVIDIA Inception program
Fifteen minutes, no deck.
Bring one specific problem in one sentence. We'll tell you whether it's worth building, roughly what it takes, and sometimes that the answer is don't build it.
Start hereTwo steps before we talk — it takes about five minutes.