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Software Engineering

Backend Development

The server side is where correctness, data integrity and security actually live. We build it to hold up under real load — and we run it once it does.

Overview

The backend is the part of your system your users never see and cannot afford to have wrong. It is where the business logic runs, where the data is stored and kept consistent, where a request is checked against who is allowed to make it, and where the slow, unglamorous work — the payments, the emails, the exports, the third-party syncs — happens out of sight. When a frontend has a bug you see a broken button; when a backend has a bug you get double-charged, you leak another customer’s data, or a number that a business decision depends on is quietly wrong. That asymmetry is the whole reason this work rewards senior engineering: the cost of getting it wrong is measured in money, trust and legal exposure, not in a misaligned layout.

We build the server side across the languages that suit the problem rather than the one we happen to like — Node and TypeScript for I/O-heavy services and teams already living in JavaScript, Python where the data and machine-learning ecosystem earns its place, Go for high-throughput services that want low latency and a small memory footprint, Java for the JVM-heavy enterprise estate, PHP where a mature framework and hosting reality make it the pragmatic call. The language is a consequence of the workload, the team that will maintain it and the ecosystem the problem lives in. Anyone who leads with the language before understanding the problem is selling you their comfort zone.

And we are honest about the biggest, most expensive mistake in this field: reaching for microservices before you need them. A distributed system trades a class of problems you understand — a function call — for a class you mostly do not: network failures, partial outages, eventual consistency, distributed transactions and a deployment story that now needs a platform team. For the overwhelming majority of systems, a well-built modular monolith is the right first architecture, and it stays right for far longer than the conference circuit implies. We will build you the boring, correct thing that scales, and split it into services only when a real constraint — team size, a genuinely independent scaling profile, a hard isolation boundary — actually forces the decision.

Who it’s for — Teams whose product depends on data being correct, access being controlled and the system staying up under real traffic — where a server-side mistake costs money, trust or a breach notification rather than a cosmetic glitch.

What you get

  • A data model designed deliberately — schema, constraints, indexes and migrations — because the database is the part that outlives every rewrite and is the hardest thing to change once it holds real data
  • Business logic built where it belongs, tested against the rules that actually matter, not scattered across controllers and triggers where nobody can find the source of truth
  • Authentication and per-object authorisation enforced in the data layer, since a valid session is never the same thing as permission to touch a given row
  • Background jobs, queues and scheduled work for everything that must not block a request — payments, notifications, exports, retries — with idempotency and failure handling built in
  • Integrations with the third parties you depend on, engineered to survive their outages, rate limits and slow days rather than assuming a perfect network
  • Caching placed where it earns its keep, with a deliberate invalidation strategy, because a stale cache serving wrong data is worse than no cache at all
  • Observability shipped with the system — structured logs, metrics and traces — plus runbooks, so a production problem has an answer in minutes rather than a guess

What Backend Development does for you

  • Correctness that survives concurrency

    A backend that is right on a quiet afternoon is easy; one that stays right when two requests hit the same record at once, when a job retries after a crash, or when a payment webhook arrives twice is the hard part — and it is the part that protects your money and your data. We design transaction boundaries, unique constraints and idempotency so the invariants hold under the messy, concurrent reality of production rather than only in the demo.

  • Performance from the data up

    Most backend slowness is not the language; it is the database — a missing index, an N+1 query fetching a thousand rows one at a time, a report running on the same instance serving live traffic. We find the real bottleneck with measurement rather than guesswork, and fix it at the level it lives, so the system stays fast as the data grows instead of being rescued by throwing hardware at a query nobody profiled.

  • Security where breaches actually happen

    The headline-grabbing exploits are rare; the common breach is an authenticated user changing an ID and reading data that is not theirs. We enforce authorisation on every object in the layer that cannot be bypassed, validate every input at the boundary, and keep secrets out of code and logs — building security into how the backend works rather than bolting it on before an audit.

Why teams choose us for Backend Development

  • You are storing data or moving money where a mistake is expensive, and you want the correctness, the transactions and the access control handled by senior engineers who have operated systems where those decisions had consequences — not learned on your production database.
  • You want a straight answer on architecture, including being talked out of microservices when a well-built monolith is what you actually need, rather than sold a distributed system that adds a platform team’s worth of operational cost for problems you do not yet have.
  • You need the unglamorous production disciplines — indexing, queues, caching, idempotency, observability, on-call runbooks — treated as core engineering from the first slice, because those are exactly the parts a cheaper build quietly skips and you discover missing during the first outage.
  • You want the language and datastore chosen for your workload, your data shape and the team that will maintain it, with the trade-offs shown to you honestly, instead of whatever the last agency was comfortable in.

What Backend Development includes

The concrete pieces of work this covers — scoped to what your problem actually needs.

  • Data modelling, databases and migrations

    We design the schema deliberately — the tables or documents, the relationships, the constraints that make invalid states impossible to store, and the indexes the real queries need — and choose between relational and non-relational stores on the shape of your data and its access patterns rather than by default. Relational with proper constraints is the right answer far more often than fashion suggests. Migrations are versioned and reversible, because changing a schema that already holds production data is the most careful work a backend team does.

  • Business logic and domain design

    The rules of your business belong in one findable, tested place, not smeared across HTTP handlers, database triggers and a frontend that hopes the server agrees. We model the domain so the logic is expressed clearly and the invariants are enforced where they cannot be evaded, which is what lets the system grow without every new feature risking a rule nobody remembered was there.

  • Authentication and authorisation

    Authentication proves who is calling — sessions, tokens, OAuth2 and OpenID Connect where third parties are involved — with sensible lifetimes, rotation and secure storage. Authorisation, the part that is actually breached, is built as a deliberate layer that checks what this principal may do to this specific object, enforced in the data layer against the authenticated identity, never inferred from the fact that a request carried a valid credential.

  • Background jobs, queues and scheduling

    Anything slow, retryable or fire-and-forget belongs off the request path: sending email, generating exports, charging cards, syncing with third parties, running scheduled maintenance. We build these on a real queue with idempotent handlers, retry-with-backoff, dead-letter handling for the ones that keep failing, and visibility into what is queued, running and stuck — so a background failure is something you can see and reprocess, not a silent gap a customer discovers for you.

  • Third-party integrations

    Payment gateways, email and SMS providers, CRMs, logistics and identity services — the systems you do not control and cannot assume will behave. We integrate defensively: timeouts so their slow day is not your outage, retries and idempotency so a duplicate delivery does not double-charge, webhook handling that survives out-of-order and repeated events, and graceful degradation so one provider’s failure does not take the whole request down with it.

  • Caching, performance and observability

    We place caching where it measurably helps and design the invalidation up front, because the hard part of caching was always knowing when the cached answer became wrong. We profile before optimising, fix N+1s and missing indexes at the source, and instrument every service with structured logs, metrics and traces so that "it is slow" or "it is down" has a specific answer — which query, which service, which dependency — instead of a team staring at a dashboard guessing.

Where it fits

  • The backend behind a web or mobile product

    A single, coherent server side serving a web app, a mobile app, or both — authentication, the data model, the business logic, the jobs and the integrations that make the product actually work. Usually the right first architecture here is a well-structured modular monolith with clear internal boundaries: fast to build, cheap to operate, easy to reason about, and entirely capable of scaling further than most products ever need before any question of splitting it up arises.

  • A transactional system where correctness is non-negotiable

    Anything handling money, inventory, bookings or entitlements, where two requests racing for the same resource must not both win and a retried operation must not run twice. The engineering centres on transaction boundaries, the right isolation level, unique constraints and idempotency keys, so the invariants hold under concurrency — because a double-booking or a double-charge is not a bug you can patch after the fact, it is a refund, a complaint and a dent in trust.

  • Rescuing a prototype backend that has outgrown itself

    A backend that got a product to launch and is now the thing holding it back: logic tangled everywhere, no real authorisation model, queries that fall over as data grows, and no way to see what is happening in production. We stabilise it deliberately — an authorisation pass, the indexes and queries that are hurting, a proper job system for the work blocking requests, and the observability to see the rest — rather than a big-bang rewrite that stops everything while it is in flight.

  • Integration-heavy back office and automation

    Systems whose job is to move data reliably between other systems — a CRM, a billing platform, a warehouse, a payment provider — and run the scheduled and event-driven work that keeps them in sync. The hard parts are other people’s data models, flaky networks and failures you must survive rather than prevent, so we build for idempotency, clear retry semantics, and a mix of synchronous calls and queued events that keeps working the first time a dependency has a bad day.

How we approach Backend Development

We start from the data and the rules, not the framework. The first questions are what the system must never get wrong — which invariants have to hold no matter what, where money or personal data is involved, what happens when two requests race for the same record — because those answers fix the shape of the schema, the transaction boundaries and where authorisation has to live. We design the data model to enforce correctness with real constraints rather than hoping the application code always remembers to, since the database is the one component that survives every rewrite and is the most painful thing to change once it is full of production data.

From there we build in thin vertical slices that run in production — a real endpoint or job, authenticated, authorised, backed by real queries against a real schema, with its failure paths and idempotency in place — rather than accumulating a happy-path branch that has never met a concurrent request or a third-party timeout. Because the engineers who write the backend are the ones who operate it, the parts that only hurt later — indexes, retries, structured errors, monitoring — are present from the first slice, not deferred to a hardening phase that always gets cut when the deadline arrives.

How the engagement runs

We open on the data and the rules, because those are the decisions that are cheap to make now and ruinous to unwind later. Before any production code we agree the invariants the system must never violate, the shape of the schema, where transactions have to be atomic, and where authorisation lives — and we design the data model to enforce as much of that as possible with real constraints rather than trusting every future code path to remember. We pick the language and the datastore against your workload and the team that will maintain the result, and we say plainly when a monolith is the right call and microservices would be an expensive mistake this early.

From there the build proceeds in thin vertical slices that run in production, not on a branch. Each slice is a real endpoint or job — authenticated, authorised, backed by real queries against the real schema, with its error paths, retries and idempotency in place — so you are steering off working software rather than a plan. The disciplines that a cheaper build defers until the deadline eats them — indexes, structured errors, queues, monitoring — are present from the first slice, because the people writing the backend are the ones who will be on call for it, and that changes what gets built.

How we architect it

We default to a modular monolith with firm internal boundaries: the domain logic separated from the transport layer, the data access isolated behind those modules, and dependencies pointing inward so the core rules do not depend on the framework wrapping them. This is not the unfashionable choice by accident — it is fast to build, cheap to run, trivial to deploy, and it keeps a single database transaction available across the whole system, which is worth an enormous amount when correctness matters. Clear module boundaries also mean that if a genuine need to extract a service ever arrives, the seam is already there to cut along.

On data and scaling, we push consistency down into the schema — constraints, foreign keys, unique indexes, the right isolation level — so correctness does not depend on every caller behaving. We scale the parts that actually need it before splitting the system that does not: read replicas for read-heavy load, connection pooling, caching in front of expensive reads, and moving slow work onto queues so the request path stays quick. We reach for microservices only when a real constraint forces it — a team that has grown too large to share one deployment, a component whose scaling profile is genuinely independent, a hard isolation boundary — and we go in clear-eyed that the network failures, eventual consistency and operational overhead are the price, not a free upgrade.

Security

Most breaches are not clever exploits; they are authorisation failures. An authenticated user changes an ID in a request and reads or edits a record that was never theirs — broken object-level authorisation — and it is consistently among the most common and most damaging classes of vulnerability precisely because a valid session gets mistaken for permission to reach everything. We spend the most care here: access control is enforced on every object, against the authenticated principal, in the data layer where it cannot be bypassed, and function-level and field-level permissions get the same treatment. What a caller may do is checked, never assumed from the fact that they logged in.

Around that core we build the rest deliberately. Every input from outside the system is treated as hostile and validated at the boundary, with parameterised queries so untrusted data can never become executable — closing off the injection classes by construction rather than by vigilance. Secrets live in a managed secret store, never in the repository, the logs or a URL, and are rotated. Passwords are hashed with a modern algorithm, tokens are scoped and expiring, transport is encrypted, and error responses are careful not to hand an attacker a map of the internals. Security here is a property of how every endpoint and job is written, not a hardening pass someone runs in a panic the week before an audit.

Signs it’s time

  • The database has become the bottleneck — queries that were fine at a thousand rows crawl at a million, and nobody is sure which index would fix it or why
  • You are handling money, personal data or anything a mistake makes legally or financially expensive, and you need the correctness and access control done by people who have felt what getting it wrong costs
  • Work that should happen in the background — emails, exports, payments, syncs — is blocking requests or failing silently, because there is no proper queue or job system behind it
  • A prototype backend got you to launch and is now buckling: no real authorisation model, logic tangled across the codebase, no observability, and every change risks breaking something you cannot see

Our working method

The organising principle is that the backend is where correctness, data integrity and security live, so the expensive decisions — the data model, the transaction boundaries, the authorisation model — are the first ones we get right and the ones we design to be enforced by the system rather than by everyone remembering the rule. We measure before we optimise, because backend performance problems are almost always in the data access and almost never where a guess would put them, and we would rather show you a query plan than a hunch. We treat idempotency, retries and failure handling as part of writing a feature, not a follow-up, because in a system that talks to a network and runs concurrent requests, the failure path is not an edge case — it is the normal case that has not happened yet.

We build in thin vertical slices in production, and the engineers who design the backend are the ones who operate it — which is exactly why the unglamorous parts are present from the first slice. And because architecture carries real, lasting cost, part of the method is the honest call: a modular monolith over premature microservices, the boring datastore over the exciting one, the language that fits the problem and the team over the one that fits a CV. We would rather talk you out of complexity you do not need than bill you for operating it for years.

Technologies we build it with

Chosen per problem, not per fashion — this is the stack we most often reach for on this work.

How we deliver

  1. 01

    Discover

    We map the system, the constraints and the business it serves — including the parts nobody documented.

    Architecture brief

  2. 02

    Architect

    Decisions get made, written down and defended before a line of production code exists.

    Decision records

  3. 03

    Build

    Short cycles against working software. You see progress in the product, not in a status deck.

    Shipping increments

  4. 04

    Operate

    Monitoring, incident response and iteration. The system is alive, so the engagement is too.

    Runbooks & SLOs

What changes

  • Data you can trust

    A schema that enforces its own invariants with real constraints and transactions, so correctness survives concurrency, retries and the next developer who forgets the rule.

  • Breaches designed out

    Authorisation checked on every object in the data layer, because most backend breaches are access-control failures, not exotic exploits.

  • Holds up in production

    A system with the indexes, queues, caching and observability that keep it correct and responsive under real load, run by the people who built it.

Industries we serve

Domain knowledge changes what gets built. A few of the sectors we know before the first meeting.

How pricing works

  • Fixed-scope build for a well-defined backend — a product’s server side, a transactional system, or an integration platform with a known surface — quoted once the data model, the rules and the integrations are understood, so you are not paying for scope nobody has pinned down.
  • Monthly senior engagement for evolving backend work, where the system grows with the product and you want continuity, a stable data model and someone genuinely on call for it rather than a one-off deliverable handed over and forgotten.
  • Focused audit or rescue for an existing backend — an authorisation and security review, a database and query performance investigation, a rescue of logic and jobs that have outgrown a prototype, or an integration that keeps failing — priced by the assessment.

Typical timeline

  1. 01

    Data model and rules

    One to two weeks agreeing the invariants, the schema, the transaction boundaries and the authorisation model, choosing the language and datastore for the workload, and settling the architecture — captured as a design your team can review before code exists.

  2. 02

    First vertical slice in production

    Two to three weeks delivering real endpoints and jobs end to end — authenticated, authorised, backed by real queries, with idempotency, structured errors and monitoring in place — proving the model against real data and real failure cases.

  3. 03

    Iterative build-out

    Feature-by-feature delivery of the rest of the system in short cycles, each slice shippable, with indexes, queues, caching and integrations built as the surface grows rather than retrofitted at the end.

  4. 04

    Hardening and handover

    A security review focused on object-level authorisation, load and query-performance testing, observability and runbooks in place, and documentation so your team can operate, be on call for, and extend the backend without us.

Why teams choose us for Backend Development

  • Senior engineers only

    The backend is easy to stand up and easy to get quietly, expensively wrong — the race condition that double-charges, the missing index that surfaces at scale, the authorisation gap that leaks data. The people building yours have operated systems where those mistakes had consequences, and they design to avoid them. No juniors learning transactions and access control on your production data.

  • We operate what we build

    We run the backends we ship, so the parts that only matter under load and failure — idempotency, retries, indexing, caching, monitoring, runbooks — go in from the start rather than after the first outage. We optimise for the years a system has to stay correct and available, not for a demo that looks clean against an empty database.

  • Honest about architecture

    We will talk you out of microservices when a well-built monolith is what you actually need, recommend the boring datastore over the exciting one when it fits better, and choose the language for your workload and team rather than for fashion. The complexity we do not add is the operational cost you do not carry for years.

  • Correctness and security as the point

    Because the backend is where money, data and access control live, we treat data integrity and authorisation as core engineering rather than features to add later. That discipline is the difference between a system a business can depend on and one that works until the day, under real concurrency, it very publicly does not.

How to engage us

Three ways to work with us on this — chosen to fit the problem, not our margin.

Related services

Part of Custom Software Development. Other work we do alongside this.

Common questions

How is this different from your API Development and Frontend services?

Frontend is what the user sees and touches in the browser. API Development is specifically about designing and operating the contract that other systems and teams call — the surface, its versioning, its documentation. Backend development is the whole server side behind that surface: the business logic, the database and data model, authentication and authorisation, background jobs, integrations and caching. An API is one way of exposing a backend; the backend is the thing doing the actual work, holding the data and enforcing the rules. Many engagements involve all three, but this service is the correctness, integrity and reliability that live on the server.

Which language should we build the backend in?

It depends on the workload, the ecosystem the problem lives in, and the team that will maintain it — not on fashion. Node and TypeScript suit I/O-heavy services and teams already in JavaScript. Python earns its place where the data and machine-learning ecosystem matters. Go is excellent for high-throughput, low-latency services with a small footprint. Java fits an existing JVM enterprise estate. PHP is a pragmatic, well-supported choice with mature frameworks and hosting. We will make the call on your actual constraints and show you the trade-offs, and we are wary of anyone who names the language before they understand the problem.

Should we use microservices?

Almost certainly not yet. Microservices trade a problem you understand — a function call — for a set you mostly do not: network failures, partial outages, eventual consistency, distributed transactions and a deployment story that needs a platform team. For the large majority of systems a well-built modular monolith is the right first architecture, and it stays right far longer than the industry noise suggests. We build it with clear internal boundaries so a genuine need to extract a service later has a clean seam to cut along, and we split only when a real constraint — team size, a truly independent scaling profile, a hard isolation boundary — forces it. Premature microservices are one of the most expensive mistakes we are regularly called in to unwind.

Our backend is slow — can you fix the performance without a rewrite?

Usually, yes, and a rewrite is rarely the right first move. Backend slowness is almost always in the data access, not the language, so we start by measuring — query plans, the N+1 patterns fetching rows one at a time, the missing indexes, the reports running on the same instance as live traffic — rather than guessing. From there we fix the real bottleneck at the level it lives: the index, the query, a read replica for read-heavy load, caching in front of the expensive reads, or moving slow work onto a queue so it stops blocking requests. Throwing bigger hardware at a query nobody profiled is the expensive way to postpone the problem, and we would rather find it than feed it.

How do you keep our data correct when lots of things happen at once?

By pushing correctness down into the database and designing for concurrency rather than hoping to avoid it. Invariants are enforced with real constraints — unique indexes, foreign keys, check constraints — so an invalid state cannot be stored even if a code path forgets the rule. Operations that must be all-or-nothing run inside a transaction at the right isolation level, so two requests racing for the same record cannot both win. Anything that might be delivered or retried more than once — a payment webhook, a queued job — is made idempotent with keys, so running it twice has the same effect as running it once. That combination is what keeps a system correct under the concurrent, retrying, partially-failing reality of production rather than only in a single-user demo.

Let’s talk about Backend Development.

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