AI-ready landing zones
Secure multi-account foundations with networking, identity and guardrails designed for AI workloads.
Engineering Excellence
Cloud Engineering for AI
Secure, scalable cloud foundations for models, agents, data, and serving — with cost and reliability in mind.
- 99.95%availability across delivered platforms
- 40%typical cloud cost reduction
- 100%infrastructure as code, no console drift
Capabilities
Cloud foundations built for AI workloads
AWS, Azure and Google Cloud architectures purpose-built for production AI systems.
GPU and inference infrastructure
Right-sized accelerated compute — provisioned, scheduled and utilized properly.
Infrastructure as code
Everything versioned, reviewed and reproducible — environments spin up in hours.
Reliability and cost engineering
SLOs, failover and FinOps practices that keep systems up and bills down.
How it works
The cloud layer AI systems stand on
AI workloads stress clouds differently — GPU scarcity, bursty inference, heavy data movement, new security surfaces. We engineer for that profile.
Foundations
Landing zones with AI in mind
Account structure, private networking, identity and encryption designed for model endpoints, vector stores and data pipelines — so every AI project after lands on solid, compliant ground.
- Multi-account architecture with policy guardrails
- Private connectivity to model APIs and data stores
- Secrets, keys and identity engineered for AI services
Private VPC
GPU poolAutoscaleMulti-AZ
AWSAzureGCPOn-prem
AI compute
GPUs without the waste
Spot strategies, right-sized instances, efficient serving and utilization monitoring — accelerated compute that serves your latency targets without idling money away.
- GPU capacity strategy across regions and spot pools
- Inference serving tuned for utilization and latency
- Per-workload cost visibility and budgets
−62%p95 latency
Before
3.4s
After
1.3s
Cost / 1k tasks
$41
Optimized
$14
Operations
Reliability as a designed property
SLOs defined, failure modes rehearsed, failover tested, incident response instrumented — so AI features keep their promises even when a zone or a provider doesn't.
- SLO-driven design with error budgets
- Multi-AZ and multi-region failover where justified
- Observability wired through every layer
99.9%Uptime
1.2sp95 latency
$0.021Cost / task
QualityCostLatencyDrift
Use cases
Where cloud engineering delivers value
AI platform infrastructure
The cloud foundation under agents, RAG and model serving.
Cloud migration for AI
Workloads moved with AI-readiness built into the target design.
GPU cost optimization
Accelerated compute audited, rescheduled and right-sized.
Multi-cloud strategy
Deliberate workload placement across AWS, Azure and GCP.
Compliance environments
Regulated landing zones with encryption, residency and audit built in.
Reliability programs
SLOs, chaos testing and incident tooling for AI-era systems.
Delivery
How we build it
We start from the business outcome, then design agents, models, tools and guardrails that can survive production — not just a demo.
- Production-ready architecture
- Secure tool integrations
- Measurable business KPIs
- Operate & improve playbooks
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1
Discover
Map workflows, data, constraints and ROI.
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2
Architect
Define models, tools, memory and trust boundaries.
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3
Build
Ship a production-ready system with evals and observability.
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4
Scale
Optimize cost, quality and adoption across teams.
Our AI spend was a mystery bill and our GPU utilization was under 20%. Ninety days later: full cost attribution, utilization above 70%, and environments that deploy from code in an afternoon.
Works with your stack
Built on the tools you already run
We integrate with your models, clouds, data platforms and enterprise systems — no rip-and-replace.
- AWS
- Azure
- Google Cloud
- Terraform
- Pulumi
- Kubernetes
- ArgoCD
- Datadog
- Grafana
- Vault
- Cloudflare
- GitHub Actions
FAQ
Common questions
Which cloud is best for AI workloads?
The one that fits your constraints — existing commitments, model availability, GPU access, data residency. We're certified across all three majors and give placement advice based on your workload profile, not partnership incentives.
Can you fix our cloud costs without a migration?
Usually, yes. Most savings come from right-sizing, commitment strategy, storage hygiene and GPU utilization — changes made in place. We typically find 30–40% within the first quarter.
How do you handle GPU scarcity?
Diversified capacity: multiple instance families, regions and spot pools, plus serving optimizations that cut GPU demand in the first place. Scarcity is a design constraint, not a surprise.
Is everything really delivered as code?
Yes — infrastructure, policies and pipelines in versioned repositories your team owns. If it was clicked together in a console, it doesn't count as delivered.
Next step
Put your AI on solid ground
Get a cloud readiness and cost assessment focused on what AI workloads actually need.