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Overview

Every AI feature in Nobly Insight is served by infrastructure that Nobly designs, owns, and operates. This page describes that stack at a level appropriate for technical and compliance reviewers — the hardware, the open-weight model families, how they map to features, and how we evaluate and update them over time.

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Nobly-owned GPU infrastructure

  • Hardware: enterprise-class NVIDIA GPUs and supporting servers, owned by Nobly and sized for production document workloads.
  • Location: racks in EU co-location facilities, primarily in Denmark, with EU expansion as the default growth path for additional capacity.
  • Operating model: Nobly personnel operate the servers, network, operating systems, and AI software end-to-end. There is no co-located AI provider with privileged access to your data.
  • Capacity sizing: GPU and memory capacity allocated to your environment is dimensioned to your number of business users, which is what makes the predictable cost model possible.
  • Tenant isolation: workloads are partitioned so that one tenant’s queries cannot starve another tenant’s capacity, and so that documents and embeddings remain strictly tenant-scoped.

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Role of co-location providers

The buildings that house our racks are operated by commercial co-location providers in the EU. Their role is strictly limited to the physical facility — they are not AI subprocessors and they are not data subprocessors: Co-location staff have no logical access to the servers, no path to the data on them, and no role in AI processing. Your documents and queries are never visible to anyone outside Nobly.

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Open-weight model families

We run only open-weight models — models whose weights and architectures are published and which we host ourselves. This avoids closed-vendor lock-in and gives us full control over versioning, evaluation, and security review. The current generations of models we deploy come from these families: We deliberately describe these by family rather than exact version. Open-weight models evolve quickly, and we update the specific checkpoint we run as better ones become available — without changing the trust model around your data.

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How models map to features

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Model evaluation and updates

  • Updates are initiated by Nobly, not pushed by an upstream API provider. We choose when a new model version is qualified for production.
  • Pre-release evaluation runs on an internal benchmark of representative document and query samples before any model is rolled out.
  • No customer data is used in evaluation or training. Evaluation sets are constructed from non-customer material (synthetic, public, or Nobly-owned).
  • Rollouts are reversible. A model version that regresses on the benchmark, or that surfaces issues in production, can be rolled back without changing the surrounding pipeline.

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Operational characteristics

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Why this matters

Running AI on infrastructure we own, with open-weight models we control, is what makes the rest of the guarantees in this section possible:
  • It is what allows us to say no third-party AI subprocessors without exception.
  • It is what allows us to commit to EU residency for AI processing, not only for storage.
  • It is what allows us to offer a predictable cost model that does not move with token usage.
  • It is what allows us to respond on Nobly’s timeline when a model needs to be updated, paused, or replaced — including in response to a security finding.

AI Search introduction

How retrieval, ranking, and LLM rerank/rewrite work in AI Search.

Data and privacy

How AI features handle your data: no training, no subprocessors, EU residency, per-tenant isolation, and what to capture in your DPA.