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. Whether the app is single-tenant or multi-tenant, and whether each tenant shares a schema or gets its own isolated database, 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 first (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 from the start.
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, and token auth for the Capacitor iOS and 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 real traffic. Every system we ship gets this pass as standard.
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 and read path, kept separate
In high-concurrency systems we split the path that writes data (transactional, where some latency is fine) from the path that serves it (read-optimised and fast). Read replicas, Redis-backed caches and projection tables where they make sense, so the dashboard stays fast even when checkout is busy.
Queue work that doesn't leak
Slow, heavy or external work runs on queues, not in the request cycle. Payments and webhooks get their own priority lane with supervised workers, and 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 all of it. All wired in before launch.
Shipped through a real pipeline
Every release ships through a CI/CD pipeline (GitHub Actions, Jenkins or AWS CodePipeline, depending on your stack). Tests, lint, build, deploy and a smoke check run on every push, so deployments are repeatable and nobody is copying files onto a server by hand.
AWS, sized to your budget
We pick the smallest setup that meets your requirements. EC2 behind Nginx or Caddy, with an optional dedicated node for heavy jobs. RDS for managed databases, or everything on one 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 approach, different cloud. Droplets running Nginx or Caddy, managed Postgres or MySQL for separation, or a single droplet running everything for tight budgets. Files on Spaces, queues on Redis and Horizon. A good fit for teams who want predictable pricing without the complexity of AWS.