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TypeScript APIs
HTTP and remote-procedure interfaces with validation at the boundary, typed errors, generated client types, and contracts shared with the front end rather than restated in it.
SERVICE · NODE.JS
Node.js Engineering
Node.js is at its best where a service spends most of its time waiting — on a database, a queue, another API, a model provider. We build those services in TypeScript with types shared across the boundary, back-pressure handled rather than hoped away, and enough instrumentation that an incident is a question with an answer rather than a search.
THE PROBLEM
The service is fine until it is not, and then nobody can tell what happened.
Asynchronous services fail in ways that do not show up in a stack trace. A queue consumer that processes the same message twice because the acknowledgement came after the side effect. A retry storm that turns a slow dependency into a dead one. An unhandled rejection that takes the process down at four in the morning with a message that names a file and nothing else. A memory profile that climbs for eleven days and then restarts itself. None of these are exotic — they are the normal failure modes of concurrent systems, and the difference between a team that handles them calmly and one that does not is almost entirely whether the system was instrumented before it broke.
Signals you might recognize
Incidents are diagnosed by reading source code rather than by reading telemetry.
The same message occasionally gets processed twice and somebody reconciles it by hand.
A slow downstream dependency takes the whole service down with it.
Memory climbs steadily between restarts and nobody has found where.
WHAT WE DELIVER
What this actually includes.
Concrete engineering capabilities deployed as part of this service practice.
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Event and queue architecture
Producers, consumers, and topic design with idempotent handlers, explicit ordering guarantees, dead-letter queues, and a documented answer for what a replay does.
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Real-time services
WebSocket and server-sent-event transports with reconnection, backfill after a gap, presence where it is needed, and horizontal scaling that does not assume one process.
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Integration layers
The service between your systems and somebody else's — rate limits respected, transient failures retried with backoff, permanent failures surfaced, and the third party's outage contained rather than propagated.
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Observability
Structured logs with correlation identifiers, metrics on the paths that matter, and distributed traces across service boundaries. Enough that an incident starts with evidence.
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Performance and memory work
Profiling under realistic load: event-loop lag, heap growth, connection pool behaviour, and the synchronous work quietly blocking everything else.
HOW WE WORK
How we approach Node.js Engineering.
Milestone-driven delivery, so integration problems surface in week two rather than in week ten.
01
Establish the contracts
Message shapes, API contracts, and delivery guarantees written down before implementation. Ambiguity about whether delivery is at-least-once or exactly-once is the root of a surprising share of production incidents.
Stage 01 activities
- Schema definition for every message and endpoint
- Explicit delivery and ordering guarantees per channel
- Versioning and compatibility rules agreed before the first consumer
- Failure semantics: what a consumer does when it cannot proceed
02
Build for repeated delivery
Handlers are written to be safe to run twice, because eventually they will be. Idempotency keys, conditional writes, and transactional outbox patterns where a side effect must match a database change.
Stage 02 activities
- Idempotency keys on every side-effecting handler
- Transactional outbox where a write and a publish must agree
- Dead-letter routing with enough context to reprocess
- Replay tested deliberately rather than discovered accidentally
03
Contain failure at the boundaries
Every external call gets a timeout, a retry policy with jitter, and a circuit breaker. A dependency that is slow should degrade one feature, not saturate the event loop and take the service with it.
Stage 03 activities
- Timeouts and bounded retries with backoff and jitter
- Circuit breakers and bulkheads around external dependencies
- Back-pressure so a queue backlog does not become memory growth
- Defined degraded behaviour per dependency
04
Instrument before launch
Correlation identifiers threaded through every hop, metrics on queue depth and processing latency, and traces that cross service boundaries — all in place before traffic, not added after the first incident.
Stage 04 activities
- Structured logging with a correlation identifier per request
- Metrics on queue depth, lag, and handler duration
- Distributed tracing across service and queue boundaries
- Alerts on the symptoms users notice, not on every anomaly
05
Load-test the real shape
Sustained and bursty load against realistic data volumes, watching event-loop lag and heap growth rather than only response times. A service that looks healthy at steady state can still fail at the first Monday-morning spike.
Stage 05 activities
- Sustained and burst load profiles based on real traffic
- Event-loop lag and heap profiling under load
- Connection pool and downstream limit verification
- A documented capacity ceiling and the first thing that breaks past it
TECHNOLOGY DEPTH
Technologies behind Node.js Engineering.
The libraries, runtimes, and services we standardise on for this work, and keep patched.
Frontend
- TypeScript
Backend
- Node.js
Data
- PostgreSQL
- Redis
- MySQL
Cloud
- Docker
- AWS
- Kubernetes
DELIVERABLES & OUTCOMES
What you receive, and what actually changes.
We measure success by durable working software in your possession, not presentation slides.
What you get
Typed API and message contracts with versioning rules
Idempotent handlers with dead-letter routing and a tested replay path
Timeout, retry, and circuit-breaker policy on every external dependency
Structured logging, metrics, and distributed tracing in production
Load test results with a documented capacity ceiling
Runbooks for the failure modes the system can actually produce
What changes
Incidents that begin with evidence rather than with a search through source code
A downstream outage that degrades one feature instead of the whole service
Duplicate processing handled by the system rather than reconciled by a person
A known capacity ceiling, and knowledge of what breaks first beyond it
QUESTIONS & ANSWERS
Questions about Node.js Engineering.
Direct answers to common technical and engagement questions.
Mostly because of where the time goes. For services dominated by waiting on other systems, Node.js handles high concurrency with modest resources, and sharing TypeScript types with the front end removes a whole class of contract drift. For processor-bound work, .NET is usually the better answer, and we will say so.
Whatever the team will maintain. Fastify or Express with a small set of well-understood libraries is the common choice; NestJS suits larger teams that want structure imposed. The deciding factor is who operates it afterwards, not which is currently fashionable.
By not promising it. Practically every message broker offers at-least-once delivery, so the reliable approach is idempotent handlers and deduplication keys, which produce exactly-once effects from at-least-once delivery. Systems designed around a stronger guarantee than the transport provides tend to fail quietly.
Yes, and the first step is usually instrumentation rather than changes. Adding correlated logging, queue metrics, and traces to a service nobody can diagnose often identifies the actual problem within days, and it makes every subsequent change measurable.
Those move off the request path, into worker processes or a language better suited to the work. Blocking the event loop with heavy computation is the most common way a Node.js service becomes unresponsive while every individual piece of code looks correct.
Have something ambitious to build?
Tell us what you're working on. We'll tell you honestly whether we're the right team for it.