Run AI on machines you own, and know what they can handle before you buy anything.
Most of the firms we build for handle someone else's data: a US hospital's charts, a bank's loan files, a client's source code. Sending it to a public AI service isn't an option. So our tools run where the data is allowed to live, and we measure the hardware before anyone spends money on it.
Three ways to deploy
Machines you already own
After a readiness check, we deploy on your servers or workstations. Nothing leaves your network, and we connect only when you let us in for support.
A cloud account in your name
If you'd rather not run hardware, we deploy into a GPU cloud account you own, India-hosted if your data must stay in India (E2E Networks and Yotta are two such providers). You hold the account and the bill; we never hold your data.
A machine you buy through a partner
If your machines aren't enough, you buy a suitable one through a hardware partner. You own it, the warranty sits with the manufacturer, and we set it up. We don't resell hardware.
What the readiness check measures
We install a small program on your machines and run it against your own work. You get a report with a plain verdict for each workload and a recommendation, and nothing leaves your machines unless you send us the report.
About one week.
How long it takes for the first words of an answer to appear
How fast it writes once it starts
What happens when 4, 8 or 16 people use it at the same moment
How long a document it can read before it slows down
How accurate two or three candidate models are on 20 to 50 of your own examples
Whether the machine slows down as it heats up
What different machines can do
Published measurements give a sense of scale. On desktop machines built around NVIDIA's GB10 chip, a large "mixture-of-experts" model, gpt-oss-120b, writes roughly 59 tokens a second from a short prompt and about 43 from a long one. A dense 70-billion-parameter model on the same machine manages under three, which is too slow for chat. A token is roughly three-quarters of a word. The same machine can be excellent or useless depending on the model you choose, which is why we measure yours with your work.
What our software needs
Our tools themselves run on a server with four cores and 8 GB of memory. The AI model is the part that needs more, and how much more depends on the model and on how many people use it at once. That's what the readiness check measures, so you don't have to guess.
When we'll tell you not to run it yourself
You send a few hundred requests a month. A hosted service with the right contract is cheaper.
The task needs the strongest reasoning models available, and those aren't released to run on your own hardware.
You need speech recognition for hundreds of callers at once, with no GPU budget.
Nobody on site can restart a machine when it needs it.
We'll say so in the report, and recommend the hosted option with the contract terms your clients need.
Model licences
We check the licence of every model we deploy and list it in your handover. Some speech models for Indian languages are licensed for non-commercial use only; we don't ship those in client work.
Measure the hardware before you spend money on it
The readiness check runs on your own work and gives you a plain verdict for each workload. It takes about one week.
Book a readiness check