What we build
Private AI Systems
Every system Lucrion engineers runs on your hardware, in your facility, under your control, with no cloud APIs, no shared compute, and no data-residency compromise. The six capabilities below are the building blocks; most engagements combine several of them. If you are not sure where to begin, a Readiness Assessment is the fastest way to find out.
GPU Cluster Systems
High-density GPU compute for training, fine-tuning, and inference, sized, procured, racked, and commissioned for your facility. From a single-node server to a multi-rack cluster with a high-speed interconnect, the build is specified against your real workload, power envelope, and growth plan.
Stack
- NVIDIA GPUs (single-node 8× class, or multi-node)
- InfiniBand or RoCE interconnect for multi-node
- Linux base OS with the CUDA and driver stack
- DCGM, Prometheus, and Grafana for GPU telemetry
- Server vendors: Dell, HPE, Lenovo, or Supermicro
What you get
- Site and power assessment with a sizing recommendation
- Hardware specification, procurement, racking, and cabling
- OS, driver, and CUDA configuration, validated under load
- Monitoring and alerting wired in before handover
Good fit when
- You have GPU workloads that no longer fit a single workstation
- You need deterministic capacity rather than cloud spot availability
- Data residency or cost makes public-cloud GPU rental untenable
Private Inference Environments
Isolated inference stacks that run entirely within your network perimeter. No external API calls, no shared compute, no data-residency question marks. Open-weights models are served locally with deterministic, low-latency responses and a full request audit trail.
Stack
- vLLM or SGLang for high-throughput serving (Ollama for light workloads)
- Open-weights models (7B–70B class) on your hardware
- API gateway with authentication and rate limiting
- Encrypted model-weight storage at rest
- Request logging and an audit trail
What you get
- A private inference endpoint your applications can call internally
- Measured first-token and throughput figures on your hardware
- Access control and per-request audit logging
- Documentation for operating and updating the stack
Good fit when
- Prompts or documents are too sensitive to send to a public API
- You want predictable latency without shared-tenant throttling
- You need an auditable record of every inference request
Secure Architecture Design
Security designed in from the architecture phase: network segmentation, identity, encrypted storage, and audit logging applied at every layer, not bolted on after deployment. The design is documented so it can withstand external review, not just internal policy.
Stack
- Network segmentation and DMZ design
- Keycloak and FreeIPA for identity and access management
- Encrypted storage at rest and in transit
- Secret management (Vault or equivalent)
- Comprehensive audit logging and vulnerability hardening
What you get
- A documented reference architecture with security controls mapped
- Identity integration with your existing directory
- Audit and access-logging design ready for regulated review
- A hardening and vulnerability-scanning baseline
Good fit when
- You operate under GDPR, the EU AI Act, DORA, or NIS2
- You need to demonstrate technical and organisational measures
- Security review is part of your procurement gate
Model Deployment & Serving
End-to-end model deployment from procurement to production, hands-on and handed over. We handle hardware, OS and drivers, the inference server, model loading, endpoint authentication, and benchmarking, then train your team and leave complete documentation behind.
Stack
- vLLM or SGLang serving with API endpoints
- Model formats: Safetensors, GGUF, ONNX, custom fine-tuned weights
- Authentication and rate limiting on every endpoint
- Load testing and performance benchmarking
- MLflow for model and experiment tracking where relevant
What you get
- Models loaded and serving against agreed performance targets
- Benchmarked first-token latency and throughput
- Full technical documentation and runbooks
- Team handoff and training sessions
Good fit when
- You have chosen a model and need it served reliably on-premise
- You want benchmarked performance, not vendor-quoted figures
- Your team should own the system from day one
Enterprise Integration
Private AI infrastructure that slots into your existing environment, not the other way around. Identity, monitoring, data pipelines, and change-management processes are integrated so the system feels native to the teams that operate it.
Stack
- Identity providers (Active Directory, LDAP, SAML, OIDC)
- Existing monitoring stacks (Prometheus, Grafana, Datadog)
- Data pipelines, ETL workflows, and internal APIs
- On-prem, private cloud (VMware, Proxmox), or selective-egress hybrid
- Backup, disaster recovery, and ITSM/change processes
What you get
- Single sign-on against your existing directory
- Telemetry flowing into the dashboards you already watch
- Integration with your data and backup systems
- Documented runbooks aligned to your change process
Good fit when
- You have an established identity and monitoring estate
- AI infrastructure has to conform to existing IT policy
- You run hybrid or private-cloud environments
Ongoing Operations Support
Support that keeps your team autonomous as usage scales and requirements evolve: monitoring, model updates, capacity planning, security patching, and quarterly reviews. We provide support where it is needed, not a dependency that has to be renewed to keep the lights on.
Stack
- Infrastructure monitoring and alerting
- Model update and version management
- Capacity planning and hardware recommendations
- Security patching and hardening cadence
- Incident-response support
What you get
- A monitored, patched, and supported environment
- Capacity and upgrade recommendations ahead of need
- Quarterly infrastructure reviews
- Incident support without creating lock-in
Good fit when
- You want a safety net while your team builds operational muscle
- You prefer a retainer to a break-fix scramble
- You operate in the EU and value on-site visits where needed
Your data. Your infrastructure. Your control.
Start with a Readiness Assessment, a fixed-scope engagement to decide whether on-premise AI makes sense for your organisation, before any hardware is procured.