Cloud engineering
Secure, cost-efficient foundations across AWS, Azure and GCP.
Services
Engineering Excellence Services
Enterprise engineering practices that keep AI systems reliable, secure, and economical as they scale.
- 10×deployment frequency improvements
- 99.95%availability on managed platforms
- 40%typical cloud cost reduction
Capabilities
The engineering floor under your AI
Cloud, DevOps, Kubernetes, MLOps, platform engineering and security services.
DevOps and CI/CD
Fast, gated delivery pipelines for code, models and prompts.
MLOps
Model lifecycle discipline from training to serving to drift.
Security and compliance
Controls engineered in, evidence generated automatically.
How we deliver
Boring infrastructure, exciting outcomes
AI ambitions die on flaky pipelines and mystery bills. We make the foundational layer disciplined so the intelligent layer can move fast.
Delivery
Ship daily without drama
Pipelines with testing, security and AI-eval gates; progressive rollouts with instant rollback; DORA metrics measured honestly and improved deliberately.
- CI/CD with eval gates for AI artifacts
- Canary releases and one-command rollback
- Lead time and failure rate tracked and driven down
Invoice exception workflowRunning
✓Extract invoice fields1.1s
✓Match against PO0.6s
Resolve mismatch with agentrunning
·Post to ERPqueued
Platform
Cloud that earns its bill
Landing zones, GPU-efficient serving, autoscaling and FinOps discipline — reliability and cost engineered together, both measured continuously.
- Infrastructure as code, no console drift
- GPU utilization lifted from waste to efficiency
- Cost attribution per team and workload
Private VPC
GPU poolAutoscaleMulti-AZ
AWSAzureGCPOn-prem
Assurance
Security that accelerates delivery
Controls in the pipeline, secrets in vaults, least-privilege identities everywhere — compliance evidence that accumulates automatically instead of stalling releases.
- Policy-as-code enforced pre-merge
- Automated evidence for SOC 2 and audits
- AI-specific threat models and red-teaming
✓PII detected & redactedenforced
✓Answer grounded in sourcespassed
✓Role-based data accessenforced
✕Out-of-scope tool callblocked
SOC 2GDPRAudit logHITL
Use cases
Excellence engagements we run
Delivery acceleration
Monthly releases turned into daily non-events.
Platform builds
Kubernetes and serving infrastructure for AI at scale.
Cost programs
Cloud and GPU spend cut without reliability loss.
Reliability programs
SLOs, failover and incident discipline installed.
Security hardening
AI estates threat-modeled, tested and fixed.
MLOps modernization
Model lifecycles brought under release discipline.
Delivery
How we engage
Flexible models — from strategy sprints to dedicated pods — with clear phases, quality gates, and ownership through production.
- Clear delivery process
- Senior engineering talent
- Security built in
- Long-term capability transfer
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1
Align
Goals, constraints, success metrics and stakeholders.
-
2
Design
Architecture, delivery plan and risk controls.
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3
Deliver
Build, integrate, harden and launch.
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4
Operate
Monitor, optimize cost and continuously improve.
Deploys went from feared to forgettable — sixty a month, failure rate down 75%. That velocity is what actually unlocked our AI roadmap.
Works with your stack
The tools of the trade
Industry-standard tooling, expertly assembled in your environment.
- Kubernetes
- Terraform
- ArgoCD
- GitHub Actions
- Docker
- vLLM
- Prometheus
- Grafana
- Datadog
- Vault
- Snyk
- AWS
FAQ
Common questions
Can you improve delivery without a big-bang migration?
That's the only way we work — incremental hardening of what exists, with each sprint leaving the system better and shippable. Big-bang platform rewrites fail too often to recommend.
What's different about DevOps for AI systems?
The artifact set: prompts, models, indexes and eval sets deploy alongside code, and quality gates become eval-based. Same discipline, wider surface — most teams need additions, not replacement.
How fast do cost programs pay back?
Typically within a quarter — right-sizing, commitments and GPU utilization fixes surface 30–40% waste in most estates we audit.
Do you run what you build?
If you want: managed operations while your team skills up, with explicit handover criteria. Otherwise full transfer with runbooks and on-call shadowing.
Next step
Make the foundation an advantage
One assessment sprint across delivery, cloud and cost — then a prioritized plan with numbers attached.