Private AI. Practical Intelligence. Your Environment.

Yes, in suitable deployments. Once the selected model, its runtime and the knowledge index are installed on local hardware, an assistant can answer questions without an internet connection. Initial setup, updates and some optional features may still require connectivity. Whether a fully offline deployment is practical depends on the chosen model, the hardware available and the features you need.

Yes. Using retrieval-augmented generation (RAG), the assistant searches approved documents for relevant passages and uses them when answering. Answer quality depends on the documents' quality and structure, how they are prepared and indexed, and the model used, so we evaluate answers against real questions before rollout. Like any AI system, it can still make mistakes, and responses should be checked for important decisions.

Often, yes. Many open-weight models can run on an organisation's own workstation or server, subject to their licence terms and the hardware's capacity. We confirm model licensing, hardware suitability and your security requirements during the feasibility assessment.

It depends on the model size, the number of concurrent users and expected response speed. Smaller models can run on capable workstations; larger models or more users typically need servers with suitable GPUs and memory. We recommend hardware after understanding your use cases rather than prescribing a fixed specification.

Not necessarily. Many knowledge-assistant use cases are well served by a suitable existing model combined with retrieval from your documents and good configuration. Fine-tuning or other adaptation is considered when evaluation shows it adds value and suitable training data is available.

Where technically feasible, yes, for example through APIs, supported databases, document repositories or a web interface embedded in existing tools. Integration scope is defined during discovery, taking access controls and data boundaries into account.

We agree a set of representative questions and expected answers, test the assistant against them, and review accuracy, source references and failure cases. On the security side we review access controls, where data, indexes and logs are stored, any external connections, and administrative controls. Security depends on the whole deployment (infrastructure, configuration and operational practice), not on the AI model alone.