// giant model · reading everything · slower, dearer
TO GET BIGGER.THE CONTEXT NEEDED
TO GET BETTER.
- The modelBigger, to brute-force itSmaller — it needn't be big
- The contextEverything, dumped inPrecisely what the task needs
- The answerGeneric, and easily wrongGrounded in your knowledge
// 01 — the problem
MOST
ENTERPRISE AI
IS RENTED.
You reach it through someone else's service. They own the model, set the price, decide where it runs — and whether you still have access tomorrow. You may own your data, but not the capability built around it.
// 02 — the risk
“Switched off” isn't hypothetical.
A government restricts it
Export controls or sanctions put a foreign-owned model out of reach — overnight, and for reasons that have nothing to do with your business.
The provider retires it
A model released last year gets deprecated. You migrate, re-test and re-certify on their timeline, not yours.
The terms change
Prices rise or limits tighten, making your usage either uneconomic or unviable.
// 03 — how we build AI you own
- 01Our engineers start with an open model, tuned to your work where it helps.
- 02We structure your organisational knowledge so the model can actually use it.
- 03We run it in an environment and geography that meet your requirements, under operating rules you set.
- 04We build the application around a real business problem — not a demo.
And it's more practical than it sounds.
No model to train
You start from a capable open model — the hardest, most expensive part is already done. Strong open models are now available from leading labs across Europe, the US and China.
No data centre to build
Specialist infrastructure providers can supply the computing capacity in the UK, EU or another chosen jurisdiction. You choose the environment, geography and operating controls without having to build the facilities yourself.
Fine-tuning is optional
Many open models are already capable enough. Often the bigger gain comes from structuring your organisational knowledge so the model can use it—not retraining the model itself.
// 04 — the manifesto
// 05 — the mechanism
One mechanism makes this possible: Alexandria.
Alexandria is our context graph. It hands a model exactly what a task needs — precise context substitutes for model size — so a smaller model, grounded in your knowledge, understands your business better than a giant one.
// SOVEREIGN
You own it
Precise context lets a smaller model do the job — so you run open weights inside your own infrastructure and jurisdiction. The graph is the durable asset; swap the engine, keep the knowledge.
// ACCURATE
Context beats size
The model gets precisely the context each task needs — not a pile of noisy search hits. Less to untangle means fewer mistakes, and every answer arrives with its sources attached.
// EFFICIENT
A fraction of the cost
Small open models can run ~10× cheaper and faster, on a fraction of the energy. The precision of the context is what makes the smaller model sufficient.
// SEE IT RUNNING
Ask a live agent about the AI industry. It runs on a small, open-weight model — French, American or Chinese, your pick — grounded in our context graph, and cites what it finds.
Served from Paris with zero data retention — nothing you type is stored or used for training. It's answering on our knowledge here; point the same mechanism at yours and it becomes an asset you own.
// 06 — the offer
We're used to hard problems.
Bring us your toughest.
US National Cancer Institute · BT · iQ Student Accommodation · AELTC (Wimbledon) · Genomics plc · Phoenix Group
A complimentary working session with our engineers. You leave with a prioritised starting use case, a clear view of what you should own — and what can safely stay external — and a practical next step.