AI-Native Laravel Engineering

Because time to production is a business risk, and Laravel is consistently faster to it. The framework ships with the parts enterprise systems need: queues, caching, authorisation, task scheduling, event broadcasting and database migrations. You maintain less bespoke infrastructure, and a new engineer joining in two years learns a documented framework rather than your custom one. Where Java or .NET genuinely wins is heavy CPU-bound computation or an existing organisational mandate, and we will say so when that is the case.

That reputation is about a decade out of date. Modern PHP with JIT compilation performs comparably to other interpreted runtimes for normal web workloads, and Laravel Octane keeps the application in memory to remove bootstrap overhead. In practice, almost every slow application we are asked to fix is slow because of database queries, missing indexes and N+1 problems, not because of the language.

Yes. We have built and operated a HIPAA-compliant multi-tenant platform on Laravel carrying real patient data, and satisfied all twelve technical safeguards in the Security Rule with evidence rather than assertions: 60 audited actions in an append-only log, six-year immutable retention under object lock, 15-minute idle sessions, per-record access resolution and two documented restore drills. Laravel handles CSRF protection, SQL injection prevention, password hashing and encryption as defaults rather than as developer discipline. We are comfortable working through your security questionnaire and penetration testing requirements, and we will tell you early which parts of compliance a development team cannot close on its own.

Horizontally, like any well-built stateless application. Web servers scale behind a load balancer, queue workers scale separately from request traffic, and the database scales through read replicas, partitioning and caching. The decisions that actually determine whether a system scales are made early, around tenancy, data modelling and job design, which is what the discovery phase is for.

Often. Most of our work starts with an existing codebase rather than a blank one. We begin with an architecture review covering dependency health, upgrade path, test coverage, performance and security, then give you a prioritised plan. Whether that leads to incremental improvement or a staged rebuild is your call, made with real information rather than a guess.

Usually less than teams expect. If your data already lives in your Laravel application, the retrieval layer is mostly built. The work is to find where AI creates measurable value, build the pipeline as queued jobs, use structured output so results are typed and testable, add a human review step in the interface, and roll it out behind a feature flag while you watch quality and cost. A first production feature is often live in a few weeks.

We build on Prism, which puts a consistent interface in front of the provider. You can run OpenAI, Anthropic, Groq or a self-hosted open-weight model, and move between them on cost, latency or data residency grounds without rewriting application code. For sensitive workloads we can run models inside your own infrastructure.

You own the code, the infrastructure and the documentation from the first day. It lives in your repositories and your cloud accounts throughout. We write architecture decision records and runbooks as we go, and handover includes walkthroughs with your team. Plenty of clients keep us on retainer afterwards, but that is a choice rather than something we build a dependency around.