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AI Platforms & Infrastructure
The machinery under AI: private, fast, observable and paid for on purpose.
01The capability
Private AI deployments, GPU capacity, model serving and the engineering platforms that build with AI. We design where models run, how they are monitored and what they cost, and we bring AI into software delivery itself: coding, testing, DevOps and operations.
02Services in this capability
Hover a service to see its form. Services with their own page open it; the rest are described here in full.
- 56Private Enterprise AI
AI models running in your own cloud or data centre, connected to your data. Nothing leaves your control; access follows your existing permissions.
Described herePrivate model hosting · Secure AI gateways · Internal AI assistants · Data residency - 57AI Cloud Services
Cloud architecture for AI workloads: training, serving and scaling. Managed AI services or self-hosted models, chosen per use case.
Described hereAWS Bedrock & SageMaker · Azure AI Foundry · Google Vertex AI · Serverless inference - 58GPU & AI Compute Services
GPU capacity planned, provisioned and scheduled for training and inference. Utilisation measured so you pay for work, not idle hardware.
Described hereGPU clusters · Inference optimisation · Job scheduling · Capacity planning - 59AI Infrastructure
The full stack under AI: data, compute, model serving and networking. Defined as code, reproducible and secure.
Described hereModel serving · Vector databases · Feature stores · Infrastructure as code - 60AI Infrastructure Management
Day-to-day operation of AI platforms: updates, scaling, incidents and capacity. Under clear SLAs, with on-call engineers who know the stack.
Described hereManaged AI platforms · Model updates · Scaling & capacity · Incident response - 61AI Observability & Monitoring
Quality, latency, cost and safety of every model call, visible in one place. Traces, evaluations and alerts when behaviour drifts.
Described hereLLM tracing · Quality evaluation · Drift detection · Alerting - 62AI Cost Optimization
AI spend broken down by feature and team, then reduced. Model routing, caching and right-sizing without losing quality.
Described hereCost attribution · Model routing · Caching & batching · FinOps for AI - 63AI Software Development
Products built with AI at the core, by engineers who use AI every day. Faster delivery with the same review, testing and ownership.
Described hereAI-first products · AI-assisted delivery · Model integration · Rapid prototyping - 64AI Coding Platforms
Internal platforms that give your developers safe, shared AI coding tools. Connected to your codebase, standards and security policies.
Described hereCoding assistants · Code review automation · Test generation · Codebase knowledge - 65AI DevOps
Pipelines that test, release and roll back with AI spotting problems early. Fewer failed releases, faster recovery.
Described hereIntelligent CI / CD · Failure prediction · Automated rollbacks · Root-cause analysis - 66AI Network Management
Networks monitored, tuned and repaired with AI that sees patterns people miss. Problems predicted and fixed before users notice.
Described hereTraffic analysis · Anomaly detection · Automated remediation · Capacity forecasting - 67AI Edge Computing
Models running on devices, gateways and local servers, close to where data is created. Fast, private and able to work offline.
Described hereOn-device models · Edge gateways · Model compression · Fleet updates - 68AI ERP/CRM Systems
ERP and CRM with AI built in: forecasts, suggestions and automatic updates. Records kept current without manual typing.
Described hereSales forecasting · Smart data entry · Demand planning · AI copilots in ERP & CRM
03Typical problems we solve
When this capability is the answer.
- P.01
Sensitive data cannot be sent to a public AI service.
- P.02
AI costs rise every month and nobody knows which feature spends what.
- P.03
Models run in production with no view of quality, latency or failures.
- P.04
Engineering teams want AI in their workflow but have no safe, shared platform.
04Example solution types
What it usually becomes.
- S.01
Private enterprise AI
Models in your cloud or data centre, under your control. - S.02
AI compute & cloud
GPU capacity, serving and scaling on demand. - S.03
AI observability & FinOps
Quality, latency and cost per feature. - S.04
AI-native engineering
Coding platforms, AI DevOps and intelligent operations.
05Approach
Four steps, one team throughout.
- 01
Assess
Workloads, data sensitivity, latency and budget.
- 02
Design
Where models run and how they are served.
- 03
Build the platform
Infrastructure as code, gateways and guardrails.
- 04
Operate
Monitoring, cost controls and capacity planning.
06Related technologies
Typical, not mandatory. Chosen per project, with your existing stack first.
BeginAI Platforms & Infrastructure
Bring us the problem.
We will bring the system.
A first conversation maps the problem to the right services, in the right order. No obligation, no slideware.