A dedicated AI server, in your city.
The applications we build run against an AI backend co-located at a professional datacenter in your city — not in a US cloud region, not in an office server cupboard. Frontier-grade hardware, enterprise-grade facility, remotely managed by us.
Better than your office. Better than US cloud. Both.
Why colocation beats an office server
- Redundant power, UPS backup, and diesel generators — far more reliable than office infrastructure
- Biometric access control, CCTV, security guards — a stronger physical security posture than any office server room
- Enterprise-grade cooling purpose-built for GPU hardware
- High-bandwidth, low-latency internet with multiple redundant connections
- Remote management access — issues resolved without an on-site visit
- Typical cost: $150–$300/month for a rack slot with power
Why colocation beats US cloud
- Data stays in your city and country — full jurisdictional control
- No third-party access to data at rest or in transit
- Predictable fixed cost — no per-token or per-API-call billing
- Hardware is owned outright — no dependency on cloud provider pricing or availability
- The sovereignty argument is fully intact: data is in a professionally secured facility in your jurisdiction
The recommended production configuration.
Our recommended configuration for a single-client or small shared deployment handles 3–5 concurrent users comfortably, running the current recommended model at full production quality.
| Component | Specification |
|---|---|
| GPUs | 2× NVIDIA L40S (48GB VRAM each, 96GB total) |
| CPU | AMD EPYC or dual Xeon |
| RAM | 512GB DDR5 |
| Storage | 2–4TB NVMe |
| Network | 10GbE |
| GPU licence | Datacenter-licensed — compliant for commercial colocation |
| Approximate hardware cost | $18,000–$22,000 |
The L40S is datacenter-licensed and purpose-built for AI inference workloads in colocation environments. It runs DeepSeek V4 Flash at production quality and handles the concurrent load of a professional services team comfortably.
Capacity planning
One server in this configuration comfortably supports:
- 3–5 concurrent active users
- 5–10 total staff for a professional services firm
- Multiple applications running simultaneously
- Document indexing running in the background
For larger teams or higher throughput, we scale horizontally — an additional server adds capacity in a predictable, manageable increment.
Server economics
One server at $20,000, co-located at $300/month, can support 5–8 clients on a shared sovereign tier.
- Monthly revenue from 5 clients: $15,000
- Monthly costs (colo, power, misc): ~$500
- Hardware amortised over 3 years: ~$550/month
You use the applications. We handle everything else.
We source and configure the hardware.
We procure the server, configure the operating system and inference stack, arrange colocation at a facility in your city, and bring the system online. You don't need to make hardware decisions.
Security patches, model updates, monitoring.
We maintain the system — operating system security patches, inference server updates, model upgrades when meaningfully better options become available, and proactive monitoring. Issues get resolved before you notice them.
Email inside 4 hours. Critical incidents faster.
Email support with a 4-hour response SLA during business hours. Critical system issues reach an on-call engineer in under 30 minutes. Monthly reporting on system health and usage.
The hardware keeps earning through every model generation.
When a better open-source model ships — which happens every few months — we update the deployment and clients wake up with a more capable system. The hardware investment is not tied to any specific model or vendor.
This mirrors the historical pattern of data centre infrastructure: companies that owned hardware in 2005 ran email servers, web applications, databases, and virtualisation workloads across every software generation. The hardware kept earning through every shift in the software layer.
The owner of sovereign AI infrastructure in 2025 is positioned at the infrastructure layer of the most important compute workload of the next decade, with the software capability to build applications on top of it.