Product & Software Engineering
Web, mobile and platform products, built end to end.
Great software is the difference between a business that scales and one that hits a ceiling. We design, build and scale products end to end — from the first wireframe to a production system handling real users — so you never have to stitch together different agencies for design, frontend, backend and DevOps.
We work across the full stack: consumer-facing web and mobile apps, SaaS platforms with multi-tenancy and billing, internal tools that retire the spreadsheet your team has been fighting for years, and the APIs and integrations that hold it all together. The work is senior by default — no rotating juniors, no black-box handoffs.
Every engagement ends with clean, documented code on your own repositories and hosting accounts. There is no lock-in and no ongoing licence to us — you own what we build, and it is scoped to run and grow without us in the loop.
- A credible product that earns user trust from the first interaction
- Manual work replaced by software that scales without extra headcount
- A codebase your own engineers can maintain and extend without help
Capabilities
Web applications & SaaS platforms
Custom web apps with authentication, roles, billing and real data — built on modern frameworks and designed to grow from your first hundred users to your hundred-thousandth.
Mobile apps (iOS, Android, cross-platform)
Native-quality iOS and Android apps using React Native or Flutter, alongside pure native builds when performance demands it — shipped to the App Store and Google Play.
Marketing sites & landing pages
Fast, accessible, SEO-ready sites that load in under a second on mobile and are easy for your team to update without touching code.
E-commerce & storefronts
Conversion-focused storefronts with payments, inventory, checkout flows and the integrations (shipping, ERP, CRM) that make them run without manual intervention.
APIs, backends & microservices
RESTful and GraphQL APIs, event-driven backends, and service architectures that are well-documented, versioned and designed to be extended by other engineers.
Internal tools & workflow software
Replace fragile spreadsheets and brittle manual steps with purpose-built tools that fit exactly how your team works — built in days, not months.
System integrations & migrations
Connect your stack — Stripe, Salesforce, HubSpot, WhatsApp, legacy databases, third-party APIs — and migrate off outdated systems without losing data or downtime.
Deliverables
- A live, production-grade application or platform on infrastructure you own
- Clean source code in your repository, documented and ready for your team
- CI/CD pipeline, analytics, SEO and accessibility basics in place
- A handover session so your team can operate and extend what we built
- Optional ongoing maintenance, feature work and support retainer
Best suited for
- Founders who need an MVP designed, built and shipped — not just prototyped
- Businesses replacing legacy software or retiring a brittle spreadsheet-based process
- Startups adding a mobile app to an existing web product
- Teams that need senior engineering capacity without a full-time hire
What we build with
Product & software engineering
Full-lifecycle product engineering means owning a product idea from the first discovery conversation to the last production incident — not just writing code to a spec handed over the wall. It spans user research, technical discovery, architecture decisions, iterative delivery, and the ongoing operational work that keeps software trustworthy in production.
Discovery uncovers the real problem before a line of code is committed. Delivery turns validated ideas into shippable increments through short, feedback-rich build cycles. Iteration uses production data — usage patterns, error rates, user feedback — to decide what to build next, kill, or reshape. Each phase informs the others; the loop is continuous, not a one-time waterfall.
Our involvement ends only when your team can operate and extend the system confidently. That means documented architecture decisions, a healthy test suite, observable infrastructure, and code you own outright — no proprietary frameworks that create lock-in.
How we engineer a product
Each stage has clear entry criteria, defined outputs, and a handoff checkpoint. We compress or expand stages based on what is already known — a greenfield product needs deep discovery; a rescue engagement may jump straight to architecture review.
- Stakeholder interviews and assumption mapping
- User research: jobs-to-be-done, workflow walkthroughs, pain-point ranking
- Competitive and market landscape review
- Technical feasibility spikes for high-uncertainty areas
- Definition of measurable success criteria and non-goals
- Context diagram and bounded-context identification (DDD)
- Technology selection with documented rationale (ADRs)
- API contract design and schema-first stub generation
- Security threat-modelling and data-classification review
- Estimation, risk log, and prioritised backlog seeding
- Sprint planning against the prioritised backlog
- Feature development with unit, integration, and contract tests from commit one
- Continuous integration: every push triggers lint, type-check, test, and security scan
- Demo and retrospective at the end of each iteration
- Lightweight risk and dependency review before each iteration starts
- Load and soak testing against production-like data volumes
- Observability setup: structured logging, distributed tracing, and alerting thresholds
- Security pen-test or automated DAST scan
- Runbook authoring and on-call handoff
- Staged rollout with feature flags and automatic rollback criteria
- Production monitoring review and anomaly triage
- Qualitative feedback synthesis (support tickets, user interviews)
- Backlog grooming against updated success metrics
- Periodic architecture health review and tech-debt burn-down
Discovery & scoping
Understand the problem space, validate assumptions, and produce a shared definition of success before any architecture or code is committed. The output is a Product Brief that all stakeholders sign off on.
Architecture & planning
Translate validated requirements into a system design that is simple enough to build quickly, safe enough to operate, and structured so that the team can iterate independently. Every significant decision is captured in an Architecture Decision Record (ADR).
Build in iterations
Deliver working, tested software in short cycles — never longer than two weeks — so that real feedback can redirect effort before assumptions compound into expensive rework. Vertical slices ship whole user-facing capabilities, not isolated technical layers.
Hardening & launch
Raise the operational bar before traffic hits production. This stage is not a gate at the end — items like observability and load testing are started during Build and completed here.
Iterate & support
Post-launch is where most of the value is created or destroyed. We stay engaged through a structured cadence of measurement, triage, and prioritisation so that each subsequent release improves the metrics that matter.
Engineering principles we hold firm on
These are non-negotiable defaults. We will explain the reasoning and discuss trade-offs, but we will not quietly abandon them under schedule pressure.
Boring technology over clever
We reach for well-understood, widely-supported tools first. A PostgreSQL table with a well-designed index solves most data problems without the operational overhead of a specialised store. Clever solutions impose a learning tax on every engineer who touches the codebase after us — including your future hires.
Decisions recorded as ADRs
Every significant architectural decision — why we chose this database, why we rejected that framework, why we drew the service boundary here — is written down in a short Architecture Decision Record before work begins. ADRs give future engineers the context to evaluate whether a decision still holds, rather than cargo-culting it or unknowingly reversing it.
API-first, schema-first
We design and agree on the API contract — OpenAPI, GraphQL schema, or Protobuf — before writing business logic. This lets frontend and backend teams work in parallel against a stub, and makes contract testing straightforward. It also forces clarity about what data the consumer actually needs rather than what the server finds convenient to expose.
Tests and CI from commit one
A test suite written after the fact is a test suite that covers the happy path and nothing else. We wire up a CI pipeline before the first feature branch is created and require that every change passes lint, type-check, and the test suite before it merges. The cost of setting this up early is trivial; the cost of retrofitting it to a legacy codebase is enormous.
Ship vertical slices, not horizontal layers
A vertical slice delivers an end-to-end user-facing capability: database schema, business logic, API endpoint, and UI component. Horizontal layers — 'we built all the data models this sprint' — produce nothing a user can touch, make integration surprises invisible until late, and generate a false sense of progress. We plan, estimate, and demonstrate by slice.
You own the code — no lock-in
Every repository, pipeline, and infrastructure configuration is in your version-control account from day one. We use open-source frameworks and managed cloud services with clear migration paths, never proprietary abstractions that make us impossible to replace. Our goal is to make ourselves unnecessary, not indispensable.
Technology we reach for and why
Tool choices are always context-dependent. What follows is our default selection for product work, with the reasoning that drives each choice. We will deviate with justification.
Frontend
- TypeScriptStatic types catch an entire class of runtime bugs at the editor level, make large-scale refactors safe, and serve as living documentation of data shapes. We treat any JavaScript-only codebase as a migration candidate.
- ReactThe largest ecosystem of accessible component primitives, testing utilities, and hiring pool in frontend development. Its unidirectional data flow makes state bugs easier to reproduce and fix than two-way binding alternatives.
- Next.jsHandles the rendering strategy decision (SSR, SSG, ISR, or client-only) at the route level rather than the application level, which is the right granularity for most products. File-system routing, image optimisation, and the App Router reduce configuration overhead significantly.
- React Native / FlutterReact Native is the default for mobile work where a shared web codebase is already in React, because logic and some component patterns transfer. Flutter is preferred when pixel-perfect fidelity, high-frame-rate animation, or tight native API access dominates requirements and the team has no existing React investment.
Backend
- Node.js (TypeScript)Shares type definitions with the frontend, reducing the surface area where shape mismatches cause bugs. Fast for I/O-bound workloads — API gateways, BFF layers, real-time features via WebSockets — and the npm ecosystem covers most integration needs without external runtimes.
- Java / Spring BootThe default for high-throughput, long-running workloads where GC predictability and the JVM's mature profiling toolchain matter. Spring Boot's dependency-injection model and mature testing support make large codebases maintainable across team changes. Preferred for financial or enterprise contexts where the hiring pool expects it.
- Python / FastAPIFastAPI generates an OpenAPI spec automatically from type annotations, which collapses the distance between code and contract. Python is the lingua franca for data science and ML workflows, making it the obvious choice when a service needs to call a model, run a pipeline, or hand off to a data team.
- .NET (C#)Strong choice when the client already runs a Microsoft stack, needs Azure-native integrations, or has an existing C# codebase. ASP.NET Core's performance benchmarks are competitive with Go, and the tooling around Entity Framework reduces boilerplate for CRUD-heavy services.
- GoA single statically-linked binary with near-zero startup time and deterministic GC makes Go the right pick for CLI tools, sidecar proxies, and services where container image size and cold-start latency are hard constraints. The language enforces simplicity in a way that keeps microservices from becoming micro-monoliths.
Data & storage
- PostgreSQLOur default relational store. ACID transactions, full-text search, JSONB columns for semi-structured data, and a row-level security model that enforces access control at the database layer. It handles the majority of product data requirements without requiring a second storage system.
- RedisPurpose-built for low-latency reads: session storage, rate-limiting counters, pub/sub fan-out, and caching layers that need sub-millisecond response times. We treat Redis as ephemeral by default — data that must survive a restart belongs in PostgreSQL.
- MongoDBAppropriate when the document model genuinely matches the problem — deeply nested, highly variable schemas that would require many nullable columns or EAV tables in a relational model. We avoid it as a default because schema flexibility tends to become schema inconsistency over time without strong application-layer discipline.
Platform & delivery
- DockerEliminates environment drift between development, CI, and production. A Dockerfile is also a form of documentation: it records every dependency the service needs to run, which makes onboarding and incident diagnosis faster.
- CI/CD (GitHub Actions / GitLab CI)Pipeline-as-code, versioned alongside the application, with no external server to maintain. GitHub Actions has the largest library of reusable actions; GitLab CI is preferred when the client is already on GitLab and needs its integrated security scanning. Both support matrix builds, environment promotion, and manual approval gates.
- Cloud (AWS / GCP / Azure)We are cloud-agnostic by principle and deploy to whichever provider the client already uses or has a negotiated commitment with. We favour managed services — RDS, Cloud SQL, S3, Cloud Storage — over self-managed infrastructure where the total cost of ownership is lower and the operational risk is carried by the provider.
How we'd choose
There's rarely one right answer — these are the trade-offs we weigh before recommending an approach.
REST vs GraphQL vs tRPC
API style is an architectural decision that compounds — changing it later is expensive. Choose based on who consumes the API, how stable the contract needs to be, and what your team can operate confidently.
| Criterion | REST | GraphQL | tRPC |
|---|---|---|---|
| Best fit | Public APIs, third-party integrations, mobile clients with predictable data needs | Products with many consumer types fetching different projections of the same data | Full-stack TypeScript monorepos where client and server are co-deployed |
| Type safety | Manual or generated from OpenAPI spec; contract drift is common without strict CI enforcement | Schema-first; client code-gen from the schema gives reasonable safety | End-to-end type safety out of the box — the router IS the contract |
| Caching | HTTP caching works natively at every layer (CDN, proxy, browser) | Requires persisted queries or a caching layer (Apollo, Relay) to cache at the HTTP layer | Application-level caching only; no HTTP cache benefits on mutations |
| Over/under-fetching | Fixed response shapes can over-fetch; versioning is required to change shapes | Clients request exactly the fields they need, eliminating over-fetching | Fixed procedure outputs; over-fetching can occur but is easily mitigated with separate procedures |
| Operational complexity | Low — any HTTP client, any language, minimal tooling required | Higher — schema registry, query complexity limits, N+1 protection (DataLoader) needed | Low for TypeScript teams; incompatible with non-TypeScript consumers |
| Team skill requirement | Universal — every backend engineer understands REST | Specialist knowledge needed; resolver design and N+1 avoidance require experience | Strong TypeScript discipline required; not suitable for polyglot teams |
PostgreSQL vs Redis vs DynamoDB
Storage choice has long-term consequences for correctness, cost, and operational burden. The right answer almost always starts with PostgreSQL — reach for a specialised store only when there is a clear, measurable reason.
| Criterion | PostgreSQL | Redis | DynamoDB |
|---|---|---|---|
| Data model | Relational with JSONB escape hatch for semi-structured data; enforces schema | Key-value, sorted sets, lists, streams — purpose-built for specific access patterns | Document / wide-column with composite primary keys; schema-free at the item level |
| Consistency guarantees | Full ACID transactions, serialisable isolation, foreign-key integrity | Single-key operations are atomic; multi-key operations require Lua scripts or transactions (limited) | Eventually consistent by default; strong consistency available per-request at higher cost |
| Latency profile | Low milliseconds for indexed queries; suitable for most transactional workloads | Sub-millisecond for in-memory reads; the right choice when latency SLAs are in the hundreds of microseconds | Single-digit milliseconds; consistent at any scale, which is the primary value proposition |
| Scale ceiling | Vertical scaling to large instance sizes plus read replicas; horizontal sharding is complex | Cluster mode supports horizontal sharding; data must be designed around key distribution | Effectively unlimited horizontal scale — the entire value proposition at massive volume |
| When to reach for it | Default choice for any relational or semi-structured data with integrity requirements | Session storage, rate-limiting, leaderboards, pub/sub, cache-aside pattern | Global-scale products with simple, well-defined access patterns and DynamoDB expertise on the team |
| Operational overhead | Moderate — requires index design, vacuuming awareness, and connection pooling (PgBouncer) | Low for caching use cases; Redis Cluster adds significant operational complexity | Very low — fully managed; capacity planning replaced by on-demand billing |
Anti-patterns we actively prevent
These failure modes appear in almost every project that has gone off the rails. We name them explicitly so that the team can recognise and call them out early.
Resume-driven / over-engineered architecture
Microservices, Kubernetes, event sourcing, CQRS, and a service mesh are introduced in the first sprint because they are interesting, not because the problem demands them. The result is an operational burden that consumes the entire engineering capacity, leaving no bandwidth to build features that users care about. A two-person startup does not need twelve services.
Start with a well-structured monolith. Identify the seams — bounded contexts, independent scaling requirements, team boundaries — and extract services only when those seams become load-bearing. Every architectural addition must justify its operational cost against a documented requirement.
No tests, or tests written as an afterthought
Skipping tests to move faster is a loan at a very high interest rate. Without automated tests, refactors become dangerous, bugs that were fixed come back, and every deployment is a manual regression exercise. Teams that skip tests spend more time debugging production than teams that invest in them from the start.
Wire CI before writing the first feature. Write the test alongside the code, not after. Use coverage as a signal, not a target — a 90% coverage number on trivial getters is less valuable than 60% coverage on critical business logic paths. Treat a failing test as a build failure, not a warning.
Big-bang launch
Months of development accumulate behind a single release date. Integration issues, performance problems, and user experience failures all surface at once, under maximum pressure, with no rollback strategy. The post-launch fire-fighting period costs more time than the phased rollout would have.
Ship to production early and often, behind feature flags if the product is not ready to announce publicly. Use staged rollouts — internal users, then a percentage of real traffic, then full rollout — with automated rollback triggers based on error rate or latency thresholds. Separate deployment from release.
Go deeper
Product & Software Engineeringcovers a lot of ground. Here are the specific things we're most often asked to build.
Common questions
Do you handle design as well as engineering?
Yes. We can take a project from wireframes and UX through to a shipped product, or slot in at any stage — design-only, build-only, or both. See the Design & Product Strategy pillar if discovery comes first.
We have an existing codebase — can you take it over?
We do this often. We audit what you have, stabilise the foundations, then build from there. We can rescue a half-finished project, migrate off a slow platform, or add features to a codebase we did not write.
Other services
Have something in mind?
Tell us what you're building or stuck on. The first consultation is free — no obligation, no hard sell.