Back-End Development
Built for Scaling

Our back-end architecture approach prioritises scalability, reliability, and maintainability from the start. We begin by understanding your constraints—growth projections, expected data volumes, latency requirements, and regulatory compliance needs. From there, we design systems that remain predictable under load rather than collapsing when usage unexpectedly spikes. This means making explicit trade-offs early. Do you prioritise consistency or availability? Should your data model normalise or denormalise? When should caching layer in? These decisions compound over time, so we document them carefully and revisit them as your product evolves. We avoid over-engineering for hypothetical scale, but we also avoid architectures that require complete rewrites when you grow past the initial design assumptions.

There's no universal answer. Relational databases like PostgreSQL excel at transactions and complex queries but require careful schema design. Document stores like MongoDB offer flexibility but move complexity into application code. Time-series databases like InfluxDB handle high-volume data streams efficiently but are poor choices for transactional consistency. We evaluate your specific access patterns, consistency requirements, and operational capabilities, then recommend technology that fits rather than technology that's trendy. For most product companies, PostgreSQL remains the pragmatic default. We'll recommend alternatives when they solve a specific problem better.

APIs need to evolve without breaking clients. We design versioning strategies that let servers add new fields and capabilities without requiring client updates. We maintain comprehensive API documentation that stays in sync with implementation. We use contract testing to validate that API changes don't break consumers before they reach production. We make breaking changes explicitly, through major version bumps, with deprecation periods that let clients migrate safely. Good API design is boring—it's consistent, predictable, and doesn't surprise anyone.

Performance is determined by schema design, not queries. If your schema forces expensive joins, no query optimisation will save you. We design schemas that align with your access patterns, plan indices strategically, and establish baselines so you know when performance degrades. We avoid over-indexing, which slows writes and wastes storage. We monitor query performance in production and surface slow queries and missing indices before they become user-facing problems. We also ensure your development environment lets you reproduce production performance problems locally rather than discovering them after deploy.

Cloud platforms offer immense flexibility and immense opportunity to waste money. We design infrastructure that scales automatically but doesn't scale unnecessarily. We containerise applications so they deploy consistently across environments. We use infrastructure-as-code so system configuration is version controlled and reviewable. We establish cost monitoring and budgets so your cloud bill remains predictable. We favour managed services where they reduce operational burden, but we avoid tight coupling to vendor-specific services that make migration difficult.

Scaling usually happens in phases. Initial growth is handled by vertical scaling—bigger machines, more memory, faster storage. Horizontal scaling—distributing load across multiple servers—comes next and requires stateless application design and load balancing. Beyond that, you need caching, asynchronous processing, and eventually database sharding. We design systems that can scale through these phases without requiring complete rearchitecture at each step. We also help you understand the operational cost of each scaling decision so you can make informed trade-offs between capability and complexity.

Security spans multiple layers. At the network layer, we ensure services only communicate where necessary. At the application layer, we validate all input, sanitise output, and avoid trusting client-provided data. We implement strong authentication and fine-grained authorisation so users can only access data they should. We encrypt sensitive data in transit and at rest. We maintain audit trails so security teams can prove who did what. We also establish incident response procedures and run security reviews to surface vulnerabilities before attackers find them.

Testing is an investment in confidence. Unit tests validate individual functions in isolation. Integration tests ensure services work together correctly. End-to-end tests simulate actual user workflows across the full stack. Load tests surface performance bottlenecks. Chaos tests deliberately break things to discover failure modes before production breaks them accidentally. You won't write tests for everything—that's diminishing returns. You'll write tests for the paths where failure is expensive: payments, data integrity, security boundaries. You'll establish test coverage targets that are meaningful, not arbitrary.

You can't operate systems you can't see. We establish structured logging so you can search logs and understand what happened. We capture metrics—request rates, error rates, latency—so you can track system health over time and alert when behaviour changes. We set up distributed tracing so you can follow a request through multiple services and understand where time is spent. We establish dashboards that show the metrics that matter to your business and engineering teams. Monitoring isn't optional or retrospective. It's designed in from the start, not bolted on afterwards.

We pragmatically select technology based on your constraints. For most product work, we've delivered excellent results with Node.js, Python, Go, Java, and Rust. Each has different strengths. Node.js excels at concurrent I/O with a large ecosystem. Python offers rapid development with strong data science libraries. Go is lightweight, fast, and excellent for distributed systems. Java has maturity, extensive libraries, and strong typing. Rust offers memory safety and performance without garbage collection. We choose based on your team's capabilities, the problem you're solving, and the operational environments you're targeting. There's no universal best choice, and we avoid technology decisions driven by personal preference rather than project fit.

Migrating from legacy systems is risky because your users can't tolerate downtime. We run old and new systems in parallel, gradually shifting traffic from old to new. We maintain data consistency between systems using event streams or periodic synchronisation. We establish rollback procedures so we can quickly revert if the new system discovers problems. We also maintain the option to run both systems indefinitely if migration becomes too risky. The migration itself is orchestrated carefully, often with feature flags that let you control which users hit the new code.

Documentation is not optional. API documentation needs to stay in sync with implementation. Architecture documentation needs to explain why decisions were made, not just what the decisions were. Runbooks need to guide operators through common failures and recovery procedures. We write documentation as we build, not retrospectively. We use tools that keep documentation close to code so it doesn't drift. We review documentation as rigorously as we review code because out-of-date documentation is worse than no documentation.

Cost isn't just cloud bills. It's engineering time, operational overhead, and technical debt. We design systems that are efficient to run, with reasonable resource usage that doesn't require constant optimisation. We monitor cloud spending so cost surprises don't happen mid-project. We help teams understand the cost of features—some are cheap to build but expensive to operate, others are expensive to build but cheap to operate. We make that trade-off explicit. We also establish cost budgets so teams know they're approaching limits before they overspend.

We've delivered back-end systems for trading platforms handling millions of securities, bookkeeping platforms processing thousands of transactions daily, regulatory monitoring systems analysing compliance data across jurisdictions, and fintech platforms managing customer portfolios. We've worked across relational databases, document stores, time-series systems, and purpose-built data warehouses. We've designed APIs that remain stable through years of evolution. We've migrated monoliths to microservices without downtime. We've operated systems with uptime targets exceeding 99.9% and response times below 50 milliseconds. Case studies are available on request.

Yes. Launching is only half the work. Systems degrade over time. Load patterns shift. New features create new bottlenecks. We offer retainer engagements for ongoing optimisation, feature development, and incident response. We establish monitoring and alerting so you know when things break. We maintain runbooks and documentation so your internal team can operate the system confidently. We also help you build internal capability for long-term independence, so the system doesn't depend on external support indefinitely.