Private & owned AI, explained
Plain-English writing for leaders deciding how to bring AI into a business the right way: owned, private, and built on your own data.
What 48 Fine-Tuning Experiments Showed
Six open-weight models, three task types, three random seeds each. Every one of the 48 runs improved. The full numbers, the two predictions we got wrong, and what the measurements do not cover.
Read the articleSix Results of Our Own That Were Wrong
An 88-point improvement that was entirely our own file format. A 45-point gain that reversed to a 9-point loss. Six measurements we made, believed, and found to be wrong, with the check that now catches each one.
Read the articleWhat 510 Real Contracts Taught Us About Measuring Extraction
Why we exclude the contract fields buyers most want reported, why exact match is right for a date and meaningless for a 317-character clause, and two pipeline defects that only appeared on the real corpus.
Read the articleYour Base Model Is Better Than You Think
We sell fine-tuning, and this is the case for measuring your untuned model first. It already scores 78 to 84% on eight-way classification where guessing scores 12.5%. Four situations where we would tell you not to fine-tune.
Read the articleChoosing a Base Model Matters Less Than You Think
After training six models on the same contracts, the gap between best and worst narrowed from 15.0 points to 4.5. What should decide your choice instead, and the counter-example that keeps it from being a law.
Read the articleHow We Prove Your Model Is Actually Better (Not Just Different)
Ask most AI vendors how they know their model is better and you get a demo. Here's the method that produces a number instead, with real results on 510 lawyer-annotated contracts (62% → 85%), how much data it actually took, and the two results we measured, caught, and threw away.
Read the articleOwn, Don't Rent: The Case for Private AI in Regulated Industries
The fastest way to get AI into your business is to rent it, and it may be the most expensive decision you make this decade. Why owning a private, fine-tuned model beats renting a cloud API on privacy, cost, control, and risk.
Read the articleWhat Air-Gapped AI Actually Means (and When You Need It)
"Air-gapped" gets used loosely, and the loose version is exactly what a security team can't accept. A precise read on what true isolation requires, who needs it, and why you can only get it from a model you own.
Read the articleWhere Your AI Comes From: Model Provenance & Country of Origin
For defense, government, and regulated buyers, who built the model, and where, is part of the risk calculus. Why provenance belongs on your procurement checklist, separate from license and from data safety.
Read the articleIs Your Data Ready for AI?
Most organisations have more usable training data than they think, in worse shape than they hope. What "AI-ready" actually means (volume, consistency, labelling) and how to check yours without sending it anywhere.
Wondering if your data is AI-ready?
Start with a free readiness scorecard you run yourself. We never see your data.
Get the free scorecard