What we build with you
AI infrastructure is the set of components needed to develop, deploy and operate applications: model execution, data, integrations, automation and observability.
Nuxia designs and integrates these foundations. The use case and its requirements determine the platform; not every organisation needs the same environment or degree of isolation.
Three decisions before deployment
Data · What AI can use
Identify sources, owners and sensitivity. Define what information may be sent to an authorised provider, what requires a private environment and what access each use case needs.
Access · What it can do in your systems
Separate queries from actions. Connect systems with managed identity, permissions and credentials, and define which operations require human review.
Operations · What happens after the pilot
Prepare traces, metrics and usage and cost monitoring. Agree responsibility for maintenance, changes, incidents and evaluation of outcomes.
Nuxia AIbox
AIbox is Nuxia's private AI platform proposal, based on open-source components, for developing and deploying generative AI applications.
It supports local models or model access through APIs, low-code or programmable development, and observability through logs, metrics and traces. It includes RAG — querying retrieved knowledge — and model-tuning options where the use case requires them.
Deployment options include connected and isolated environments. Offline operation requires compatible components and dependencies; it cannot be combined with calls to external APIs.
Explore the current AIbox page
From components to the solution
Platform and execution
Size the environment according to workloads, models and constraints. Infrastructure may be on premises, in the cloud or hybrid; GPU requirements are assessed for each workload.
Data and integration
Prepare sources and connect corporate knowledge to applications. Information quality, permissions and updates affect the outcome.
Deployment and evolution
DevOps and MLOps practices to organise changes and deployments. Observability provides signals to review solution behaviour in operation.
Private AI and the corporate perimeter
Hosting a model within the company does not solve governance by itself. You must also decide who may use it, with which data, which systems it can reach and how it is supervised.
The AI Foundation & Perimeter approach in EstrategIA Nux integrates these decisions. It can combine private models and authorised external services, with policies adapted to each workflow.
Architecture does not eliminate every risk or ensure regulatory compliance on its own. Controls, contracts and procedures must be validated within each organisation's context.
How we start
We review a use case, the data involved and the available platform. We then specify connectivity, identity, capacity, integration and operational requirements.
This initial definition produces a technical scope to decide what to deploy and how to verify it. If the goal is to automate a process, we connect it with our offering for digital workers.
Frequently asked questions
Does everything need to run locally?
No. Architecture can be private, connected or hybrid according to requirements. Where the model resides is not enough: the entire data journey must be reviewed.
Do we need to buy servers or GPUs?
We do not assume so. Models, workloads, latency and available capacity are assessed first, alongside service alternatives.
Is it a closed product?
AIbox uses open-source components. Selection, integration, configuration and operation are agreed within the project, including third-party dependencies or services.