About
Why this exists
Running AI in production has become an infrastructure problem, and most of the guidance available on it is written by someone with something to sell.
Getting a model to work is no longer the hard part. Getting it to run dependably, at a cost that holds as usage grows, is where most organisations now spend their time — and that is an infrastructure question, not a modelling one.
The decisions involved are unusually easy to get wrong. Serving configuration determines what an inference workload costs, often by a multiple. Where data sits relative to compute determines whether expensive hardware is working or waiting. Capacity commitments are signed under real uncertainty and are hard to unwind. Each of these choices is made once, early, and then lived with for a long time.
Almost every source of guidance on those decisions is attached to something being sold — a vendor's own comparison page, a reseller's shortlist, an analyst report funded by the companies appearing in it. That is not dishonest, but it is not independent either, and the difference matters when the decision is expensive and difficult to reverse.
NuGen IT is an independent infrastructure advisory for teams running AI workloads. You describe the problem. We deliver a written assessment: a target architecture, a cost model for each option, a vendor shortlist with the trade-offs stated, and a migration sequence — including the case for changing nothing, where that is the right answer.
What the practice is today
Small, focused, and new. We would rather say that plainly than imply a scale or a track record we have not built. What we do have is a defined method, a fixed set of evaluation criteria applied to every product the same way, and a commercial model with no incentive to point you towards any particular vendor.
Every product note on this site carries a badge showing exactly how far our knowledge of it goes — Desk Research, Vendor Verified or Hands-On Tested — and a badge only moves when the underlying work has genuinely been done.
Where our depth is, and where it is not
Our evaluation depth is in the software and compute layers: how inference is served and what that costs per request, how work is scheduled across shared capacity, where accelerators are sourced and on what commercial terms, and how data moves between storage and compute. At the network layer we specify requirements and review designs. At the facility layer we map the constraints that power and cooling impose on everything above them, and we bring in specialists rather than claiming to be facility engineers.
The simplest way to judge us
Ask us something specific and see whether the answer is useful. It costs nothing, it takes one message, and it is a far better signal than anything we could write on this page.
Who we work with
Described by situation rather than by size — the shape of the problem matters far more than the shape of the company.
Teams running AI in production
The system is serving real traffic. Provider invoices, accelerator utilisation, or reliability are not where they need to be, and the cost curve is steeper than the usage curve.
Teams facing a commitment they cannot easily reverse
A multi-year reservation, a hardware purchase, or a platform decision is in front of you, and you want a neutral read on the total cost before the signature rather than after it.
Teams standing up AI infrastructure for the first time
The prototype worked. Now there are questions about serving, scheduling, data access and on-call that nobody has had to answer before, and often no dedicated platform function to answer them.
Teams delivering AI systems for others
You build and ship the application. The infrastructure, networking and cost model underneath it needs depth you would rather bring in than build permanently.
Teams operating their own facilities
You are adapting existing space for accelerator density — power envelope, thermal headroom, DCIM, and the Kubernetes and GPU operator layer the workloads above will expect.
Tell us what you are trying to solve.
Describe your current setup and the problem in your own words. We reply within 2 working days with an honest assessment of whether we can help.