Technical capabilities for systems that run in production

We design, build and run systems on AWS, Azure, Google Cloud or on-premise.

Reference architecture

The layers of a typical system. DevOps, security and monitoring span all of them.

User channels

Applications and APIs

Data

Infrastructure

DevOpsCI/CD, infrastructure as code, release strategies

SecurityIdentity, encryption, scanning, audit logs

MonitoringMetrics, logs, tracing, alerting

Architecture chosen by scale and rate of change.

Application architecture
Patterns	Modular monolith Microservices Event-driven Kafka RabbitMQ SQS Monolith for MVPs, split when parts must scale
Web	React Next.js Vue TypeScript Design systems, accessible UI
Mobile	Swift Kotlin Flutter React Native Offline sync, push, device integrations
Backend	Node.js Python Java .NET Golang Rust C++ NestJS, FastAPI, Spring Boot
API	REST GraphQL gRPC OpenAPI Versioning, rate limits, API gateway
Integration	ERP CRM Accounting E-invoice Payments Zalo OA Webhooks, ETL, iPaaS

Cloud, on-premise or both, with a rollback plan for every move.

Deployment infrastructure
Platforms	AWS Azure Google Cloud VMware Proxmox Bare metal AWS Advanced Tier Services Partner
Models	Cloud-native Hybrid Multi-cloud
Containers	Docker Kubernetes k3s Rancher EKS, AKS, GKE
Infrastructure as code	Terraform AWS CDK Bicep Ansible
Migration	6R assessment Phased cutover Parallel run Rollback plan
Cost	Right-sizing Savings plans Autoscaling Cost tagging

Automated from commit to production, incidents handled by runbook.

DevOps and operations
CI/CD	GitHub Actions GitLab CI Azure DevOps Jenkins Build, test and security scan on every change
Releases	Blue-green Canary Feature flags One-step rollback
Environments	Dev Staging Production Anonymized data in staging
Observability	Prometheus Grafana OpenTelemetry Elasticsearch CloudWatch Central logs, tracing
Operations	Severity alerts Runbooks Post-incident reports
Backup	Agreed RPO and RTO Restore drills

Shared metric definitions and checked data quality.

Data
Databases	PostgreSQL MySQL SQL Server Oracle MongoDB Redis
Warehouses	Redshift BigQuery Synapse Snowflake ClickHouse
Pipelines	Airflow dbt AWS Glue Data Factory ETL, ELT, CDC
Reporting	Power BI Metabase Superset Looker Studio Or dashboards inside your product
Governance	Metric dictionary Quality checks Role-based access Access logs

Built into real workflows, evaluated, with a person approving changes.

AI
Models	Amazon Bedrock Azure OpenAI Vertex AI Self-hosted open models When data must stay in your environment
Knowledge search	RAG pgvector OpenSearch Qdrant Answers cite their sources
Document AI	Vietnamese OCR Field extraction Classification Users review the results
Agents	Permission-scoped tools Approval steps Action logs
Evaluation	Test question sets Accuracy Hallucination checks Cost per request
Controls	Sensitive data filtering Scoped data access Customer data never trains shared models

Designed in from the first sprint, checked on every build.

Security
Application	OWASP Top 10 SAST Dependency scans Penetration tests
Identity	SSO OIDC SAML MFA RBAC
Data	Encryption at rest Encryption in transit KMS Key Vault
Infrastructure	VPC Least privilege WAF Audit logs
Compliance	Decree 13/2023 Data stored in the region you choose

Applied by default; raised per project by risk.

Default engineering standards
Area	Default	Agreed per project
Code review	1 reviewer per pull request	2 reviewers for sensitive modules
Testing	Unit coverage 70%+, E2E on main flows	Higher bars in regulated domains
Release	CI/CD, 15-minute rollback	Windows set by your business hours
Backup	RPO 24h, RTO 8h	Critical systems: RPO 1h, RTO 2h
Monitoring	24x7 alerting	Response times per operations package
Security	Scans on every build, no critical issues at release	Pen tests, compliance audits
Documentation	C4 diagrams, OpenAPI, runbooks	Your templates and language

RPO: max data loss. RTO: max time to restore. Confirmed in the operations agreement.

Share your system and goals; we propose the architecture and plan.

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