API Integration Services
Connecting the systems your business runs on, with idempotent writes, reconciliation and monitoring, so an integration fails loudly instead of silently.
Integrations fail quietly, which is what makes them dangerous
A broken web page produces a phone call within minutes. A broken integration produces nothing at all: orders stop syncing, inventory drifts, and everyone finds out three weeks later during a stock count. The failure mode is silence, and silence is the hardest thing to monitor.
So the engineering is mostly defensive. Every write carries an idempotency key so a retry cannot double-post. Every integration has a reconciliation job that compares both sides and reports divergence. Every failure lands in a queue a human can inspect rather than disappearing into a log.
None of this is exotic. It is the difference between an integration that works and one that appears to work, and it is what we build by default rather than after the first incident.
Teams that start here often pair it with REST to GraphQL migration, CRM development and custom software development.
A broken integration does not throw an error anyone sees. It just stops, and you find out at month end.
Why integrations break in ways nobody notices
Four failure modes we find in almost every audit.
A timeout triggers a retry and the order is created twice, because the receiving system had no way to recognise the duplicate.
Failures are logged and dropped, so a run that processed sixty of a hundred records looks identical to one that processed all of them.
Nothing compares the two systems, so drift accumulates for weeks and is discovered by an accountant rather than an alert.
A vendor adds a field and the parser throws, taking down an integration that had no business caring about that field.
What an integration engagement covers
Reliability engineering around the connections your business depends on.
Data flow direction, sources of truth, conflict rules and failure behaviour agreed in writing before code exists.
Every write carries a key derived from the business event, so retries are safe by construction rather than by luck.
Exponential backoff with jitter, a bounded retry scope and a dead-letter queue for anything that cannot be delivered.
Scheduled comparison of both systems with a divergence report, so drift becomes an alert rather than a discovery.
Parsers that ignore unknown fields and validate only what matters, so a vendor change does not become an outage.
Throughput, latency, error class and queue depth per integration, with alerts on volume dropping as well as on errors.
From integration map to production through controlled rollout
Failure paths designed before the happy path is built.
We document every system, direction, volume and source of truth, including the manual steps people currently do to patch gaps.
Payload schemas, idempotency strategy, retry policy and reconciliation rules agreed before implementation.
Integrations implemented with the failure paths built first, tested against sandbox and production-shaped data.
Comparison jobs and divergence reporting deployed alongside, so correctness is observable from day one.
Run in parallel with the existing process until reconciliation is clean, then retire the manual steps.
The stack behind our integrations
Queues, contracts and observability rather than cron jobs and hope.
Queues and webhooks with delivery guarantees, so a receiver being down is a delay rather than a loss.
Validated payloads with tolerant parsing, so upstream changes are absorbed rather than fatal.
The systems businesses actually run on, integrated through their real APIs rather than screen scraping.
Reconciliation and monitoring treated as part of the integration, not as optional extras.
What this looks like in production
Finding the orders that had been silently failing
A retailer synchronised orders between Shopify and NetSuite with a nightly script. It had run for two years and was considered reliable, because nobody had ever received an error from it.
The first reconciliation job we deployed found four hundred orders that had never reached the ERP, spread over eighteen months. The script had been swallowing failures for individual records and completing successfully overall.
We rebuilt it as a queue-driven integration with idempotency keys, a dead-letter queue and daily reconciliation. Nothing about the happy path was more sophisticated; the difference was that failures now had somewhere to go and someone to tell.
What API integration services should include as standard
The engineering that matters in an integration is what happens when the other system is down, slow or wrong. We build that first.
Every integration ships with a job that compares both sides and reports divergence, because silent drift is the failure mode that effort real money.
A working demo and a written note every Friday covering what shipped, what slipped and what it means for the date. No status theatre.
Nothing goes live in one jump. We run in parallel or behind a flag until the numbers say it is safe to switch over.
