Solutions
Your AI needs a home — not just an API key
Where is AI actually hosted in your company?
ChatGPT in a browser is not a strategy. When AI starts answering employees, searching documents or touching your systems, it needs infrastructure you can trust — like any critical application.
The problem
Most companies can run a pilot in a week. Almost none can run AI in production for a year — with predictable costs, clear ownership, and the confidence to scale beyond a single team.
What we address
- A cloud foundation designed for AI — not bolted onto legacy
- Managed models or your own — with a clear path between both
- Capacity that grows with demand — without surprise bills
- One front door for all models — reliability built in
- Visibility into what AI is doing and what it costs
- A roadmap from pilot to production your board can follow
Deliverables
- Target infrastructure architecture
- Runtime and inference design
- Observability and evaluation plan
- Cost and capacity model
- Implementation roadmap
For whom
- — Leaders who know AI matters but don't know where to start technically
- — Companies outgrowing their first AI experiment
- — CTOs who need one standard for AI across the organisation
We assess your current runtime, cloud footprint and operational readiness.
Solutions · Radiography
AI Infrastructure
Where does AI run?
Enterprise AI needs more than a model endpoint. It needs execution environments that scale, isolate workloads, route traffic, observe behaviour and control cost — across cloud, Kubernetes, managed services and private infrastructure.
The problem
Most organisations can prototype with an API key. Few can operate models, agents and workflows reliably — with SLOs, cost visibility, multi-environment isolation and a path from pilot to production.
What we address
- Cloud and multi-cloud landing zones for AI workloads
- Managed models (Bedrock, Azure AI) and self-hosted inference on GPU nodes
- Kubernetes (EKS/AKS), serverless and vLLM/TGI inference patterns
- AI gateways, model routing, fallback chains and circuit breakers
- OpenTelemetry, eval pipelines, LLMOps and drift monitoring
- Horizontal scaling, multi-tenant isolation and FinOps for token spend
Deliverables
- Target infrastructure architecture
- Runtime and inference design
- Observability and evaluation plan
- Cost and capacity model
- Implementation roadmap
For whom
- / Platform teams building internal AI capabilities
- / Enterprises moving from PoC to regulated production
- / CTOs standardising AI execution across business units
We assess your current runtime, cloud footprint and operational readiness.