Foundation first,
features second.
Most projects fail in the parts you can't see. We follow the same disciplined order on every engagement, so the foundation is right before a single feature is built on top of it.
Data model first
We start at the database. Single-tenant or multi-tenant — shared schema or an isolated database per tenant — is decided up front. Every entity and relationship is mapped, and the full data layer is finalised (migrations, models, factories, seeders) before anything is built on top of it.
Presentation architecture
We decide the surfaces — web, mobile, or both — and let that drive the architecture. Web and mobile means one Vue codebase with Capacitor handling iOS and Android in parallel. Routing, middleware and state management are designed as a complete front-end architecture, not bolted on later.
Laravel backend
Every backend we ship runs on Laravel — one stack, applied consistently, so systems stay maintainable long after launch.
Authentication, deliberately chosen
Auth is planned around the devices it serves. Cross-device usually means Laravel Sanctum — cookie-session auth for the first-party SPA, token auth for the Capacitor iOS/Android apps. One guard, the right mode per client.
Test-first, RBAC-first
We write the tests and watch them fail, then build the access layer — policies, permissions, resource classes, routing, controllers — until they pass. Only then does deep backend implementation begin.
What happens after step 5
The unglamorous engineering work that decides whether a system survives contact with real traffic. Every system we ship gets this pass — by default.
Designed for concurrency, from the schema up
Race conditions are solved where they actually happen — at the database level. The right composite and covering indexes for the access patterns the app actually has. Unique constraints and idempotency keys on natural-key columns, so a double-click, a retried webhook or two workers picking the same job can't create a duplicate charge, booking or row.
Write path ≠ read path
In high-concurrency systems we split the path that writes data (transactional, slow-OK) from the path that serves it (read-optimised, fast). Read replicas, Redis-backed caches and projection tables where they earn their keep — so the dashboard stays snappy when checkout is busy.
Queue work that doesn't leak
Slow, heavy or external work runs on queues, not the request cycle. Payments and webhooks get their own priority lane with supervised workers; bulky exports run on a separate lane so they can't starve the critical path. Every job is idempotent, retries don't double-write, and Horizon plus structured logs watch the lot — wired in before launch.
Shipped through a real pipeline
Every release rides a CI/CD pipeline — GitHub Actions, Jenkins or AWS CodePipeline, depending on your stack. Tests, lint, build, deploy and smoke-check on every push. No "works on my machine" deploys, no manual SCP into production.
AWS, sized to your budget
We pick the smallest combo that meets the SLA. EC2 behind Nginx or Caddy, optionally a dedicated batch node for heavy jobs. RDS for managed databases — or everything baked into the same instance for cost-sensitive launches. Files on S3 or a mounted volume. Queues on SQS or Redis-backed Horizon. No over-engineering on day one.
DigitalOcean, same discipline
Same shape, different cloud. Droplets running Nginx or Caddy, Managed Postgres/MySQL for separation — or a single droplet running everything for tight budgets. Files on Spaces, queues on Redis + Horizon. For teams who want predictable pricing without AWS's surface area.