Chosen for production
We privilege boring reliability over tool novelty. The stack must survive real load, outages and team turnover.
Technology chosen for impact
A real stack for cloud, data, AI and delivery
We select technologies based on reliability, operability and total cost. This page shows the stack we most often use to ship systems to production and keep them healthy.
We choose technology for how it behaves in production, not by trend. We combine cloud, data platforms, applied AI, and custom software development to build solutions that scale, stay maintainable, and remain understandable. We define the stack around the technical and business context to avoid overengineering and preserve long-term control.
CloudDataAI / MLOpsSoftware deliveryObservability
How we make stack decisions
Vendor fit, not vendor lock-in
We use managed services when they reduce risk, but preserve exits, ownership and operational clarity.
Understandable by your team
A technically correct stack is still wrong if the team cannot operate it without external dependence.
Platforms we use to deliver
We do not force twenty tools into every project. We choose the smallest set that solves the problem well.
AWS
Landing zones, identity, networking, Kubernetes and cost control for production workloads.
Google Cloud
Data-heavy architectures, managed services and modern ML pipelines with operational discipline.
Azure
Enterprise integrations, IAM-heavy environments and hybrid platform rollouts.
Kubernetes
Container orchestration with sane observability, autoscaling, security and day-two operations.
Terraform
Reusable infrastructure modules, reviewable changes and environments that remain understandable.
Python
Automation, data engineering and applied AI tooling with maintainable code paths.
Capabilities by domain
Cloud and infrastructure
Cloud and infrastructure
We design stacks that can be operated, audited and evolved without heroics.
AWSGoogle CloudAzureKubernetesTerraformGitHub ActionsDockerNginx
Data platforms
Data platforms
Pipelines, storage and quality layers that serve analytics, operations and product teams.
Apache SparkKafkadbtBigQueryDatabricksSnowflakeSuperset
AI and MLOps
AI and MLOps
Applied AI stacks focused on evaluation, observability, inference cost and safe delivery.
OpenAIVertex AILangChainPyTorchHugging FacevLLMOllama
Software delivery
Software delivery
Frontend and backend choices biased toward clarity, performance and long-term maintainability.
ReactNext.jsTypeScriptNode.jsREST APIsWebhooksFigma
Observability and reliability
Observability and reliability
Metrics, logs and alerting designed to shorten incident response and keep systems explainable.
GrafanaPrometheusDatadogOpenTelemetrySLOsRunbooks
Need to review or simplify your current stack?
We can audit the architecture, cut unnecessary complexity and define a realistic technical roadmap without avoidable lock-in.