We build the server-side foundations that power reliable, high-performance digital products. From API design and database architecture to microservices and cloud-native infrastructure, our engineers deliver back-end systems that scale predictably under load.
Through rigorous architecture decisions and hands-on implementation, we build back ends that give your front end everything it needs to perform—and your organisation the technical foundation to grow without accumulating debt.
Most back-end failures aren't architectural brilliance that went wrong. They're the result of decisions made early in the product lifecycle that made sense at the time—then became constraints when scale increased or requirements shifted. We build back ends designed to accommodate change without constant rewriting.
Our approach starts with understanding your business constraints: how users will grow, what data volumes you'll handle, what latency your product requires. We design APIs that your front end can use elegantly, build databases that query efficiently at scale, and implement monitoring that catches performance problems before users do. We also document the trade-offs we made—so when requirements change, your team understands why decisions were made and what it would cost to change them.
The back-end work establishes well-designed APIs that your front end and third-party partners can use with confidence. APIs follow consistent patterns, document their contracts clearly, and evolve without breaking existing clients.
The back-end design establishes relational and non-relational database architecture aligned with your access patterns. It includes indexing strategies, query optimisation, and data migration approaches that keep databases performing as data volumes grow.
The back-end implementation breaks monolithic systems into focused services that can scale, deploy, and evolve independently. Services communicate through well-defined interfaces and maintain data consistency through carefully planned synchronisation approaches.
The back-end architecture implements secure user authentication, permission management, and access control that grows with your user base. It includes strategies for handling sessions, tokens, and multi-tenant security.
The back-end system enables live updates across connected clients through websockets, server-sent events, or message queues. Implementation focuses on reliability and scalability for high-volume concurrent connections.
Asset Management & Investment Funds
Personal Finances
Private Equity & Venture Capital
Banking & Financial Services
Audit & Assurance Services
Governance, Risk, and Compliance
Law firms
Insurance & Reinsurance
Real Estate & Brokerage Firms
Internal Workflows
We architected and built a trading platform back end that handles high-frequency market data ingestion and analysis across millions of securities. The system included real-time data synchronisation, complex portfolio calculations, and integration with multiple data providers.
We designed and implemented a distributed document processing back end for their audit platform, handling concurrent document analysis across thousands of concurrent users. The system included sophisticated caching, queue management, and distributed task processing.
We built a payments transaction processing back end handling millions of daily transactions across multiple currencies and jurisdictions. The system prioritised reliability, audit trails, and integration with legacy settlement systems.
We architected a real-time settlement system for a global payments provider, handling concurrent transaction processing, reconciliation, and reporting across 24-hour operations. The system maintained consistency across distributed database replicas.
Every product's back-end requirements are unique. Your growth trajectory, data volumes, integration constraints, and business rules don't match anyone else's. Building a back end that actually serves your business requires understanding what you're trying to achieve—not applying generic architecture patterns or assuming that scalability always requires microservices.
What we bring is experience building back-end systems for products across different scales and domains, discipline around architectural decision-making, and the honesty to recommend simpler approaches when complexity won't deliver real value.
We begin by mapping your technical and business constraints before touching architecture. This means understanding your current and projected user volumes, data access patterns, latency requirements, regulatory obligations, and integration dependencies. These constraints are the foundation every subsequent decision rests on.
Requirements analysis at this stage prevents the most expensive back-end mistakes: over-engineered systems that cost more to build and maintain than the business can justify, or under-engineered systems that require expensive rearchitecture the moment scale increases. We document constraints explicitly and revisit them if business conditions change during development.
Outcome: Technical constraint documentation, data volume projections, latency requirements, integration dependency map
We design your API contracts before implementation begins—defining endpoints, request and response structures, error handling patterns, authentication flows, and versioning strategy. API design is reviewed with your front-end team and any external partners who will consume the API, so the contract is agreed before any code is written.
Good API design is undervalued. A well-designed API makes front-end development faster, reduces integration errors, and allows the back end to evolve without breaking clients. A poorly designed API becomes a constraint that costs more to change than to live with. We invest in getting API design right before implementation, not as a documentation exercise after the fact.
Outcome: API specification (OpenAPI/GraphQL schema), authentication design, versioning strategy, reviewed contract
We design the data model to reflect how your application actually accesses data—not just how the data is logically structured. This involves choosing between relational and non-relational databases based on your access patterns, designing schemas that support efficient queries at your data volumes, and planning indexing strategies that scale without becoming maintenance burdens.
Database decisions made early become very expensive to change later. A schema designed for early-stage data volumes may require significant migration work as you scale. We design with growth in mind and document the assumptions underlying our decisions—so your team understands what would need to change if volumes or access patterns shift significantly.
Outcome: Data model documentation, schema design, indexing strategy, migration plan, query performance benchmarks
We design the infrastructure that your back end will run on—choosing between cloud providers, containerisation strategies, managed services, and deployment approaches based on your team's capabilities, compliance requirements, and operational budget. Infrastructure decisions affect reliability, cost, and your team's ability to scale and maintain the system over time.
We recommend the simplest infrastructure that reliably meets your requirements, not the most sophisticated available. A well-configured managed database service is often more reliable and less expensive to operate than a self-managed cluster, even at significant scale. We document infrastructure decisions and their rationale so your team can make informed choices as requirements evolve.
Outcome: Infrastructure architecture, cloud provider recommendation, cost estimate, deployment pipeline design, disaster recovery plan
We build the back end iteratively, with comprehensive testing integrated throughout rather than batched at the end. This includes unit tests for business logic, integration tests for API endpoints and database interactions, and load tests for critical paths where performance is a requirement. Test coverage is a first-class deliverable, not an afterthought.
Back-end bugs discovered in production are more expensive to diagnose and fix than those caught during testing, and they affect users rather than just development timelines. We maintain test environments that mirror production conditions closely enough to catch real issues, and we automate testing as part of the deployment pipeline so regressions are caught before they reach users.
Outcome: Tested back-end implementation, test coverage reports, API documentation, load test results
We implement monitoring as part of the build—not as a post-launch addition. This covers application performance metrics, error rates, database query performance, and infrastructure health. Alerts are configured to notify your team of issues before they escalate into user-facing failures.
Monitoring without action is just data collection. We configure alerting thresholds based on realistic baselines, establish escalation procedures, and document runbooks for common failure scenarios. After launch, we conduct performance profiling to identify and address bottlenecks, and we track key metrics over time to identify degradation before it becomes a crisis.
Outcome: Monitoring setup, alerting configuration, performance baseline, runbooks, post-launch optimisation report
We offer flexible engagement options to match your development needs, timeline, and team structure. Choose the model that fits—or combine them as your back end evolves.
The primary engagement model for ongoing back-end development and scaling. Provides dedicated engineering capacity, predictable budgeting, and priority scheduling. Works best for continuous development, feature additions, and long-term partnerships where deep product knowledge drives efficiency.
Available for clearly defined back-end projects with specified deliverables and acceptance criteria. Provides cost certainty and a defined timeline. Works well for one-off architectural initiatives, system migrations, or isolated back-end builds.
Best suited for short-term development acceleration, specialist expertise, or variable scope projects. Billing is based on actual hours worked with complete visibility into team composition and time allocation. Maximum flexibility to scale capacity as project needs evolve.
A team of back-end engineers embeds within your organisation, working as an extension of your in-house capability. This model works well for large-scale product development, ongoing scaling initiatives, or when you need integrated technical leadership.
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