Managed AI Infrastructure for Your Agents — From Consulting to Operations

Inventory of infrastructure and workflows

We map what runs — servers, networks and access paths, data flows, existing automation — and work out the workflows where an agent could take something over.

• An infrastructure picture straight from daily operations — including what your environment already delivers for security and recovery
• For existing customers: much of the environment is already known from the ongoing administration — the consulting does not start at zero

Use-case analysis with an honest result

Where does an agent genuinely take over multi-step work — and where do scripts and classical automation remain the right choice? The analysis sets priorities with reasons, not a tool list.

• One proposal with priority and reasoning — and the answer “not (yet)” is put in writing just the same
• Entry points in daily operations: recurring workflows, ticket upkeep, handovers, log-reading tasks

Stack recommendation for self-hosted operation

The recommendation describes the build: open, self-hosted agent frameworks (such as OpenClaw or Hermes) for orchestration, models hosted locally where it pays off — plus the rights and access model and the wiring into your existing monitoring, backups and security level.

• Data sovereignty as the standard; cloud components only when a concrete use case demands them — then within a controlled frame
• A least-privilege rights model: agents get only the access they need and act with full logging

A roadmap you can decide on

At the end of the consulting you have a prioritised roadmap in writing: the sensible first steps, the security frame, what gets built first — and what deliberately waits.

• Prioritisation matched to operating reality, not to a tool catalogue
• The security frame is part of the plan from day one, not a later add-on
• Roadmap and analyses are yours — build and operation of the stack follow as separate project stages after your approval

GPU instances for AI workloads

We provision GPU-backed servers sized to your models and workloads, then run them like any production estate: patched, monitored, restartable.

• Provisioning of GPU-backed capacity for inference workloads
• Containerised model serving — isolated per workload, versioned, restartable (Docker, Podman, Kubernetes)
• Capacity planning reviewed with you before every build; changes only on your instruction

Local LLM hosting

Your models and data stay on infrastructure you designate: on-premise, colocated, or in an EU cloud region.

• You decide where models live — on-premise, colocation or EU cloud regions (AWS eu-central-1, Hetzner)
• Managed model updates and rollbacks, scheduled with your team
• Vector stores and retrieval components operated like any other service

Agent runtime environments

Agents get their own lane: sandboxes with least-privilege credentials, not your admin keys.

• Per-agent identities and scoped credentials — never shared admin access
• Secrets injected at runtime, never stored in agent context
• Full action logging per agent, feeding your audit log review

Network isolation

Agent workloads live in their own network segments, separate from your production traffic.

• Dedicated network segments and firewall rules, reviewed with you
• Administrative access via VPN only
• Agents reach your systems only through explicit allowlist rules

Hardening & audit trails

Runaway behaviour is contained; every action is traceable.

• CIS benchmark alignment, CVE patching and SSH hardening as standing practice
• Rate and resource limits so one misbehaving agent cannot take its neighbours down
• Audit log review after every intervention, incident report included

Monitoring that fits your stack

We wire GPU hosts, model serving and agent runtimes into monitoring — Grafana and Prometheus, your instance or ours.

• Alerting on capacity, errors and anomalies, routed to the channels you already use
• Proactive monitoring and alerting built into every engagement — not an add-on

Backups with tested recovery

Model artefacts, agent state and configurations get deliberate backup strategies — set up and reviewed by us.

• Backup strategies for models, agent state and configuration
• Recovery processes defined and tested — a documented restore, not a hope

Incident documentation

Every intervention lands in your ticket system or ours, with a full incident report.

• Clear handovers when your team returns
• Regular status updates — the communication rhythm you already know

Practice, not pilot: Agent frameworks are already in active operation for several of our customers.

Security is a working standard: hardening, controlled access and log review are our routine — including for agent environments.

Self-hosting is core business, not a buzzword: running services on machines you designate is our normal case — including where data-protection requirements (such as GDPR) argue for self-hosting.

Sober consulting: we recommend agents only where they carry their weight in operation; an honest “not (yet)” is part of the result.

Existing customers start faster: we already know your environment, history and security level — the analysis goes into detail, not into onboarding.