An internal knowledge assistant can be useful without sending every document to a public service. Whether on-premises AI is the right approach depends on the use case, data constraints, quality requirements and the ability to operate it.
Start with one business task and a defined set of users. Document what a useful answer looks like, what errors would matter and which information the system may access.
What private deployment can control
Running models on your infrastructure gives you choices about hosting and connectivity. It does not automatically prove that data never leaves the network.
Map the complete flow: model downloads, document ingestion, embeddings, external tools, telemetry, updates, backups and support access. If isolation is required, configure and test it, including outbound connections. Review access to prompts, outputs and logs as carefully as access to the source documents.
The PDPL Implementing Regulations address personal-data safeguards. Deployment location is one input to the assessment, not a substitute for lawful processing, permissions and oversight.
Select a model for the task
Evaluate models against representative questions and documents. Check language coverage, factual accuracy, refusal behavior, response time and operating cost. Review the chosen model's license and support conditions before production use.
Retrieval-augmented generation can supply relevant documents to the model, but it does not guarantee correct answers. The retrieval layer must enforce user permissions so that a user cannot obtain information through the assistant that they could not access directly.
Include tests for outdated documents, contradictory sources, unsupported questions and attempts to extract restricted information. Decide where a human review is required.
Choose the platform and hardware together
Red Hat's AI platform documentation describes RHEL AI and OpenShift AI deployment options. Select against the workload, existing infrastructure and operating model.
Size with measured assumptions: model, precision, context length, concurrent requests, target response time and growth. An “8B model” or a user count alone is not enough to specify a GPU server. Benchmark a representative configuration before committing to hardware.
Plan storage for documents, indexes, models and backups. Include monitoring, maintenance and a recovery procedure rather than treating the model server as an appliance that never changes.
Run a bounded pilot
- Choose one use case and an accountable business owner.
- Identify approved data and access groups.
- Define quality and response-time acceptance criteria.
- Measure results with real tasks in a controlled environment.
- Review security, operating cost and support needs before expansion.
Keep a record of model and document versions used in evaluation. A later model or retrieval change should be tested against the same acceptance set.
Before procuring GPU hardware, use the AI infrastructure readiness guide to review workload measurements, power, cooling, networking and storage.
BustanTech provides AI infrastructure assessment and server infrastructure services. Contact us to discuss a pilot grounded in your data and performance requirements.