How we build software — our disciplines, process, engineering standards, and the real-world systems we've shipped. No fluff, just craft.
The core engineering practices we specialize in
High-performance APIs, microservices, and distributed systems built for reliability at scale. We default to Go for new services and bring experience in Python, PHP, and Java.
Component-driven UIs with React and Vue.js. We build fast, accessible, maintainable interfaces — from internal dashboards to high-traffic consumer apps.
Applications designed for cloud from day one — containerized, observable, and deployable via automated pipelines across AWS, GCP, and Azure.
Robust data infrastructure that moves, transforms, and loads data reliably. We've built pipelines handling hundreds of millions of events per day.
Connecting systems through well-designed APIs and integration layers. REST, GraphQL, gRPC, and Thrift — we choose the right protocol for the job.
Cross-platform mobile apps with Flutter that deliver native-quality experiences on iOS and Android from a single codebase.
A proven process from discovery to production
We spend time understanding your domain, constraints, and goals before writing a single line of code. Clarity here saves weeks later.
System design happens on paper first. We document architectural decisions, define service boundaries, and choose the right data stores before building.
Two-week sprints with working software at the end of each. You see progress constantly, not just at the finish line.
Every PR is reviewed. Unit, integration, and end-to-end tests run automatically. We don't ship code we haven't tested.
Automated pipelines deploy to staging first, then production. Blue-green or canary strategies ensure zero downtime for every release.
Every system we ship runs with dashboards, alerting, and structured logging from day one. We monitor, triage, and fix — post-launch is not an afterthought.
The non-negotiables in every project we ship
| Standard | Area | How We Apply It |
|---|---|---|
| 12-Factor Apps | Architecture | All services follow 12-factor principles for portability and resilience |
| API Versioning | API Design | Explicit versioning in all public APIs to prevent breaking changes |
| Test Coverage | Quality | Minimum 80% unit coverage; integration tests for all critical paths |
| Code Review | Quality | Every PR reviewed by at least one senior engineer before merge |
| Zero-Downtime Deploys | Operations | Blue-green or canary deployments enforced on all production services |
| Structured Logging | Observability | JSON logs with correlation IDs across all services from day one |
| Secrets Management | Security | No secrets in code or CI env vars — all managed via Vault or cloud KMS |
| Documentation as Code | Docs | Architecture docs, runbooks, and ADRs live in the repo alongside the code |
| Dependency Pinning | Security | All dependencies pinned; automated scanning for known vulnerabilities |
| On-Call Runbooks | Operations | Runbooks written before a service goes live — no exceptions |
Real systems, real results
A microservice that pipelines 40 million analytics operations every 6 hours across high-volume harvest data. Replaced a fragile legacy system with a maintainable Go service on GKE.
A PHP/React rendering platform capable of handling 110,000 hits per second, powering over 200 broadcast sites. Built with Docker Swarm and Kubernetes, integrated GraphQL for flexible content delivery.
A Symfony-based API that automatically updated response objects based on the current state of underlying data — eliminating manual cache invalidation and reducing backend complexity significantly.
A git-like utility for versioning machine learning training data inside SQL-based datastores. Built with GoLang APIs and a HACK backend, enabling reproducible ML experiments at scale.
An Apache NiFi-style ETL microservice allowing non-technical users to configure data integrations without engineering involvement. Built with GoLang, deployed via Docker and Terraform.
The "Improbability Engine" — a self-service utility to generate random data with defined or semi-defined object schemas for feature testing. Eliminated reliance on prod data in testing environments.
Tell us what you're working on. We'll figure out the right approach together and get to work.
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