(Back-end development)

Back-end development for systems that have to be right, not just fast

Back-end development is what makes your data trustworthy. We design the schema, the APIs and the event flows, then make them observable enough that a bad night is diagnosed in minutes rather than argued about.

Talk to an engineer What we deliver
Data mesh abstract
Back-end development

(Where the money is counted)

A front end can be redesigned in a quarter. A data model outlives three redesigns, two frameworks and the people who chose it. Get the entities, the boundaries and the invariants right and most later problems become small; get them wrong and every feature carries a workaround for the same original mistake.

Which is why the first week of a back-end engagement goes on nouns, rules and the things that must never be true at once — rather than on choosing a framework, which is the easy part.

(What we deliver)

Back-end and API development we take on

New services, or surgery on ones that already carry traffic. Every item is scoped and priced as its own phase.

  1. REST or GraphQL with an OpenAPI or schema contract written before the implementation, versioning agreed up front, consistent errors, pagination, idempotency keys on anything that takes money. Contracts generated into typed clients so the front end and the server cannot silently disagree.

  2. Entities, relationships, constraints and the invariants the database itself should enforce. Normalised where correctness matters, denormalised deliberately where reads dominate, with migrations written as reversible steps rather than a script someone runs by hand at midnight.

  3. Moving slow or unreliable operations off the request path: Kafka, RabbitMQ, SQS or Postgres-backed jobs, with retries, dead-letter queues and idempotent consumers. The outbox pattern where a database write and a published event must not disagree.

  4. Query plans read before any cache is added, indexes fixed first, then Redis or CDN caching with an invalidation rule you can explain. Read replicas, materialised views and connection pooling where the shape of the load justifies them.

  5. Structured logs with correlation IDs, OpenTelemetry traces across service boundaries, metrics that mean something to the business, and alerts tied to symptoms users notice. The aim is answering “what changed at 02:40” from a dashboard rather than from memory.

  6. Load testing at several times peak, rate limiting, timeouts and circuit breakers on every outbound call, graceful degradation when a dependency is down, and the capacity plan that says which resource runs out first.

(Languages and infrastructure)

(How we work)

How the work runs

Every engagement is scoped in phases, priced per phase, and reviewed with you at the end of each one.

  1. 01

    Discovery

    We map the problem, the systems around it and what a good outcome looks like, then scope the work in phases you can stop after.

  2. 02

    Design

    Flows, architecture and interfaces agreed before anyone writes production code.

  3. 03

    Build

    Two-week increments, a working environment you can open, and a demo at the end of each one.

  4. 04

    Testing

    Functional, performance, security and accessibility checks run through the build, not bolted on at the end.

  5. 05

    Launch

    Deployment, monitoring, documentation and the handover your team needs to run it.

  6. 06

    Support

    Fixes, updates and the next set of features, at an agreed monthly capacity.

(Why Team of Keys)

What makes a back end survive its third year

Most production incidents are not exotic. They are a missing index, a retry storm, an unbounded query and nobody able to see any of it happening.

  1. 01

    The database enforces the rules

    Foreign keys, unique constraints and check constraints stay switched on. Application-level validation alone means the one path that skips it quietly corrupts data nobody notices for months.

  2. 02

    Contracts before implementation

    The API shape is agreed and published early, so front-end and mobile work can start against a mock. It also forces the awkward questions — errors, permissions, pagination — while they are still cheap.

  3. 03

    Failure treated as normal

    Every outbound call has a timeout and a retry policy with backoff. Degrading a feature beats taking the whole service down when a third party has a bad afternoon.

  4. 04

    Boring by default

    One well-tuned Postgres instance handles far more than most teams assume. Microservices, sharding and event sourcing are introduced when a measurement demands them, not as an opening position.

  5. 05

    Migrations that can be reversed

    Schema changes ship in expand-and-contract steps behind flags, tested against a copy of production data. Deployments stop being events that need a maintenance window.

(Related)

More in development

All development services

(FAQ)

Questions, answered

Node with TypeScript when one language across the stack keeps a small team productive. .NET or Java where the enterprise estate, licensing and hiring pool already point that way. Python where the work is data or model heavy. Go when throughput per server genuinely matters. The strongest argument is usually who will maintain it in three years.

REST for most systems: simpler to cache, to rate limit, to debug in a log. GraphQL earns its keep when many different clients need different shapes of the same data and you would otherwise build a dozen bespoke endpoints. Mixing them is fine — a GraphQL layer over REST services is a common and reasonable arrangement.

Probably not yet. Services make sense when separate teams need to deploy independently, or when one part of the system has genuinely different scaling or compliance needs. Before that, a well-structured monolith with clear internal modules is faster to build, cheaper to run and far easier to debug. Modules first, split later where a seam proves itself.

Usually, yes. Tracing and query plans typically show a handful of endpoints doing most of the damage, and the causes repeat: N+1 queries, missing indexes, unbounded result sets, synchronous calls to a slow third party. Fixing those often buys a year. The rewrite conversation is worth having only after the measurement.

Parameterised queries throughout, validation at the boundary with a schema library, authentication and authorisation checked per resource rather than per route, secrets in a managed vault, and dependency scanning in the pipeline. Work follows OWASP ASVS as a practice, and sensitive flows get a threat model written down before they are built.

(Global presence)

Nine countries, one studio behind them.

Every project is designed, built and shipped from one studio.
Turn the globe, or pick a country to see what we deliver there.

NoidaDrag to turn

Studio · Noida, India · --:--

(Next step)

Send us the API, the schema or the incident report

An OpenAPI file, a slow endpoint or a description of what the system must guarantee is enough to start. You get a technical read and a phase plan, usually within two working days.

START

Or write to info@teamofkeys.com · Noida, India