Architecture
Compute, storage, network, access and integration design aligned with existing infrastructure.
01 / ON-PREMISE AIPrivate AI inside company infrastructure
Private AI inside company infrastructure
under your control.
We design and deploy private LLMs and AI services where the data lives: on customer servers, in a private cloud or inside an isolated environment.
01 / WHEN IT FITSUse cases that need
Use cases that need
an engineering approach
- 01
Data cannot be sent to external AI APIs
- 02
The system must work with restricted internet access
- 03
Model versions and answer quality must remain controlled
- 04
Stable workloads require predictable economics
02 / DELIVERABLESWhat is included
What is included
in the outcome
Models
Evaluation of LLMs, VLMs and embeddings on customer data and scenarios.
Infrastructure
GPU server, storage and software specifications without single-vendor lock-in.
Operations
Monitoring, audit logs, backups, model updates and acceptance criteria.
03 / PROCESSFrom a hypothesis
From a hypothesis
to a verifiable system
- 01
Workload and constraints
Define use cases, request volume, latency targets and the system perimeter.
- 02
Model evaluation
Compare suitable models on representative customer examples.
- 03
Infrastructure sizing
Estimate hardware, capacity headroom and total cost of ownership.
- 04
Deployment
Configure model services, access, observability and integration interfaces.
- 05
Acceptance
Validate quality, performance, resilience and team readiness.
04 / QUESTIONS
The essentials
Do we always need new hardware?
No. We first assess available resources and run a representative test. Hardware is specified after measurements.
Can the system work fully offline?
Yes, when models, dependencies, updates and integrations are prepared for an isolated environment.
How is a private LLM selected?
By quality on real tasks, memory, speed, licence, language support and maintainability — not model size alone.
05 / START A PROJECTStart with the task,
Start with the task,
data and outcome
In the first meeting we will review the use case, constraints and a realistic path to a measurable prototype.
Discuss a project ↗