AI Lab
Most AI advisors are not hands-on. I have my own AI lab.
A secure, confidential research lab running current-generation GPUs, with rented GPU capacity for tests too large for its own machines. It is built for hands-on work with data and models: whether your data is ready for AI, and how a model behaves at the scale you need.
Why it exists
The gap between AI claims and AI reality is almost always technical.
Many AI problems turn out to be data problems. The model is rarely the weak point. The data it needs is incomplete, inconsistent, poorly labelled, or not what the pitch deck assumed. The lab exists to look at that directly, using your data if you choose to share it, rather than relying on a description of it. Where a question depends on scale, the lab rents GPU capacity to test models and inference at that scale. It still cannot answer every question about a vendor or a platform it has no access to.
What the lab is for
Hands-on work, starting with your data.
- 01
Assess data readiness
With data you choose to share, check coverage, quality, consistency, labelling, and whether it can support the use case you have in mind.
- 02
Try the idea on your data
Run experiments with open and commercial models on a sample of your data, or at full scale on rented GPUs, to see whether an approach is feasible before you commit budget.
- 03
Test claims, including at scale
Test specific claims where the configuration and data are shared: accuracy, throughput, latency, and cost per request under load, on rented GPU capacity when the lab’s own machines are not enough. Anything not tested is reported as not tested.
- 04
Stay current
Work hands-on with new model releases, so advice reflects how they behave in practice and not only how they are marketed.
The hardware
Secure, current-generation research compute.
The lab runs current-generation GPUs in a secure, confidential environment. That is enough to run models locally on a sample of your data. For tests that need more, the lab rents additional GPU capacity on terms agreed with you in advance, so client material does not go to a general-purpose AI service. It is a research lab, not a hosting provider.
How the lab is used, and how it is not
- The lab tests models and inference, including at scale on rented GPU capacity: throughput, latency, and cost under load. It cannot test an application, platform, or production system it has no access to, such as a vendor’s full stack, and findings say so when a claim is out of its reach.
- Most of its value comes from client data. It is used only on data and configurations you authorize in writing, and only if you are willing to share them.
- Client and target material stays on the lab’s own machines unless you authorize rented GPU capacity in writing. Rented capacity is used in a dedicated environment that is deleted after the test. Data-handling terms are set in the engagement letter.
- It supports advisory and diligence work. It is not sold as a hosting, build, or development service.
- Findings state what was tested, what was not, and how far the evidence goes.
What this means for you
A different kind of confidence in the room.
For deal teams
Where the data and a direct test can answer a question, findings grounded in direct evaluation and not only in documented representations.
For CEOs and boards
A view of whether your data can support an AI initiative, before you hire a team and spend heavily finding out.