Overview
AI is built into Nobly Insight wherever it helps you find, understand, classify, and protect your documents. The way we run that AI is as deliberate as the features themselves: every model executes on Nobly-operated infrastructure, on open-weight models that we control end-to-end. Your documents and your search queries never leave that perimeter to be processed by a third-party AI provider. This section explains the principles behind that choice, where AI is used in the product, and what it means for your data, your compliance program, and your budget.Our principles
We do not train on your data
No customer document, search query, conversation, or interaction is ever used to train, fine-tune, or otherwise improve any model — ours or anyone else’s.
No third-party AI subprocessors
We do not send your data to OpenAI, Anthropic, Google, Azure OpenAI, Azure AI Foundry, or any other external AI provider. Every AI inference happens inside Nobly’s own infrastructure.
Open-weight models only
All models we run are open-weight — Qwen, Mistral, NVIDIA Nemotron, and similar families — so there is no closed-source dependency on a single AI vendor and no opaque update cycle out of our hands.
Operated by Nobly, in the EU
AI workloads run on Nobly-owned GPUs in EU co-location facilities, primarily in Denmark. The racks, servers, and software are operated end-to-end by Nobly; the facility provides only power, connectivity, and physical security. Your data stays under EU data-protection law.
Where AI is used in Nobly Insight
AI is applied at several points across the product. Every item in this list runs on the same Nobly-operated stack described above.Supporting infrastructure
Predictable cost model
The way we deliver AI also changes how it is priced. Most AI features in the wider market are billed per token, which makes the bill grow with usage in ways that are hard to predict and hard to budget for — exactly the opposite of what most procurement teams want. Because we run AI on infrastructure we own and operate, your cost is anchored to capacity allocated for your organization, not to the number of tokens your users happen to consume in a given month.
In practice, this means your AI budget is a line item you can lock in at procurement time, rather than a variable cost you have to monitor and explain quarter after quarter.
Where to read next
Data and privacy
How your data is handled by every AI feature: no training, no subprocessors, EU residency, per-tenant isolation, and what to put in your DPA.
Infrastructure and models
The hardware, the open-weight model families we run, how they map to features, and how we evaluate and update them over time.