On-premises
Within your infrastructure, with your own resources and operations.
AI that works with your context and respects your boundaries. Adapt models, connect knowledge and build assistants with evaluation and oversight.
Let’s discuss your project
Within your infrastructure, with your own resources and operations.
A dedicated environment with defined access, hosting and responsibilities.
Different workloads in different environments, with explicit information boundaries.
Model, memory and workload sizing. Evaluate on-premises, private or hybrid deployment based on privacy, latency and operating cost.
Search and answers over documentation using RAG, traceable sources and per-user permissions. Information retains its access boundaries.
Selection and adaptation of existing models. Tool-connected agents with bounded actions and human approval where appropriate.
Test sets, error analysis, cost controls and logging. Validate usefulness before expanding into operations.
Agree sources, privacy, users and how to evaluate a useful answer.
Let’s talkAn assistant evaluated against your cases, connected to authorised sources and explicitly bounded in its actions.
Document sources, information classification, target tasks and examples of expected answers.
Source catalogue, permissions and retrieval index.
Model selection and evaluation question set.
On-premises/private/hybrid architecture and tool permissions.
Not necessarily. We can design an on-premises architecture; hardware, maintenance and external-source requirements are reviewed before committing to scope.
Goals, current systems, users involved and time or budget constraints. If access to sensitive information is needed, we agree conditions and permissions first.
After defining scope, integrations, volume and acceptance criteria. The proposal separates implementation, licences or infrastructure and maintenance where applicable.
Tell us what needs to improve. We help you decide where to start and what is worth building.