DevOps engineering with AI in the delivery loop
We design, automate and operate production pipelines where AI does the heavy lifting and humans approve the destructive bits. Infrastructure-as-code copilots, autonomous Kubernetes remediation, AI-powered code review and intelligent FinOps — across every major cloud.
Talk to a SpecialistWhat we deliver
AI-powered code review
Pull requests reviewed by Claude / GPT before a human looks — security, style, regression risk, missing tests. Tuned to your codebase and conventions, not generic suggestions.
IaC copilots (Terraform / Pulumi)
Natural language → diff plan. AI proposes Terraform or Pulumi changes, you review the diff, plan stays auditable. Includes drift detection and remediation suggestions.
GitOps with AI triage
Argo CD pipelines paired with an AI agent that triages failed deploys, correlates with recent commits and proposes the likely fix in the rollback ticket itself.
Kubernetes agents
Conservative autonomous remediation — restart pods, scale, drain nodes, rotate credentials — bounded by trust ladder (read → suggest → auto) and human-in-the-loop guardrails on destructive actions.
AI-driven CI/CD
Pipelines that route by risk, batch related changes, auto-rollback on regression signals and surface flaky tests with LLM-summarized root cause.
ChatOps deploy bots
Telegram / Slack / Teams bots that trigger, approve and roll back deploys with full audit trail. Right-sized to your release cadence, not a generic chatbot.
FinOps with intelligent forecasting
AI predicts cloud cost spikes 7-30 days ahead, identifies waste (idle instances, oversized DBs, log retention) and proposes rightsizing — measured in dollars saved.
Where we operate
Cloud-agnostic by design — we pick what fits the workload, not what's on the brochure. Same engineering rigor across stacks.
Clouds
- AWS
- Google Cloud
- Azure
- DigitalOcean
- Hetzner
- Cloudflare
CI/CD
- GitHub Actions
- GitLab CI
- CircleCI
- Jenkins
- Argo CD
Infrastructure as Code
- Terraform / OpenTofu
- Pulumi
- Ansible
- AWS CDK
- Cloudflare Wrangler
Containers & Kubernetes
- Docker / Compose
- Kubernetes (vanilla, GKE, EKS, AKS)
- Helm + Kustomize
- k3s / k3d
- GHCR / ECR / GCR
AI Layer
- Claude (Opus, Sonnet, Haiku, Code)
- OpenAI GPT-4o / Codex
- Google Gemini 2.5
- MCP servers
- Function calling + RAG
Integration with Observability
- Grafana + Prometheus + Loki + Tempo
- OpenTelemetry
- Datadog / Grafana Cloud
- Alertmanager → Telegram / Slack
Cases
AI code review at scale — own infrastructure
Claude reviews every PR across multiple internal repos before a human touches it — security, style, regression risk. Cut review-to-merge time by ~60%.
IaC copilot — client engineering team
Terraform changes proposed in natural language, plan auto-generated, human approves the diff. Onboarded new engineers to IaC without weeks of ramp-up.
K8s autonomous remediation — enterprise
Bounded autonomous agents handle the routine (pod restarts, log rotation, credential rotation). Page humans only for the destructive bits with full context.
ChatOps deploy suite — production
Telegram-driven deploys with circuit breakers, dry-run validation and audit trail. Reference case under NDA.
Bring AI into your delivery loop
We assess your current pipeline, identify gaps and propose where AI can actually help — without breaking what works.
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