DigiRAG · business workflows
Private AI deployment
Discuss a deployment designed around your infrastructure, data handling and operational requirements.
Illustrative scenario
A practical starting point
A team selects the datasets, infrastructure and compatible models for a knowledge-search workflow, then tests the configuration before rollout.
Hosting location alone does not determine model processing: examine every connection and provider involved.
What to agree in the implementation
Map ingestion, embeddings, retrieval, model calls, connected tools, logs and fallback routes. Local models need capacity sized for the workload; an entirely API-based setup does not need a local GPU. A DigiRAG local-only dataset requires compatible local models and is excluded from its MCP interface. Test the complete boundary and agree backups, monitoring and operational ownership.
What to prepare for a first test
Map where the data, application, models, logs and backups will run. Include identity management, infrastructure ownership, maintenance and each external dependency in the design.
Decide what happens next
Administrators define the permitted sources, tools and access. Depending on the process, a step can run directly, request a review or pass the case to a person.
Part of the wider platform
Combine this workflow with company knowledge search, other agents and a workspace for tasks and results. Start with one process and expand when it makes sense.
Need a different process? We can also build a tailored solution.
BUSINESSAI · LUXEMBOURG
Which piece of work would you change first?
Tell us about the process, its users and the result you need. We will help define a sensible next step.