An API integration service earns its price on the paths where things go wrong, and those are what the estimate is really about.
What happens when the other end is down for an hour, returns a field that is not in the documentation, succeeds but takes ninety seconds, or receives the same order twice because somebody hit refresh.
What an API Integration Service Has to Handle
Retries that do not compound the problem. Backoff with a ceiling, and a limit after which it stops and tells someone.
Idempotency. The same request arriving twice creates one record. That is a decision in the API contract, made at the start.
Dead letter handling. Messages that cannot be processed land somewhere a human can inspect them, with enough context to see why.
Alerting on the queue rather than the request. The useful signal is a growing backlog, visible long before any single call fails.
Where Integrations Actually Break
Rarely in the protocol. Almost always in the assumptions.
A field optional in the spec and mandatory in practice. A timestamp with no time zone, or an identifier unique per customer rather than globally.
Then a rate limit documented as one number and enforced as another, and a sandbox that differs from production in exactly the way that matters.
We budget for finding these, which is why the first phase is a small integration against the real system instead of a design document. It converts the estimate into a measurement.
What We Build
REST and JSON over HTTP, with OpenAPI specs generated from the code rather than maintained beside it and quietly diverging.
Webhooks with signature verification and replay protection. Scheduled sync where webhooks are not offered, with a cursor so a missed run catches up instead of starting over.
Where the integration is with a named platform, the work is usually less about their API than their data model, so we read that first.
Integrating a Named Platform
Payment providers, shipping carriers, CRMs and accounting packages each have a documented API and an undocumented set of habits.
We keep a record of those habits per platform: which fields arrive empty rather than absent, which endpoints rate limit under load, and which webhooks fire twice. That record makes the second integration with the same platform cheaper than the first.
Senior People Through Handover
The engineer who runs the discovery integration writes the production one, which is why the retry thresholds match the system’s real behavior.
You get the specification, the retry configuration and the runbook for the dead letter queue, so your own team can operate it. Our api development services carry the same terms.
An api integration service that reads both data models before quoting is the one whose number holds. Tell us which two systems have to agree and what happens today when they do not.
Related Services
Where the integration moves data for analysis rather than driving a workflow, that is data engineering.
The application the API serves is web development. Where the API is the product itself, that is SaaS development.