API Integrations
Systems that agree with each other: integrations engineered for consistency, failure, and change.
Integration work looks trivial until data disagrees at month-end. We design integrations around the hard parts, sync semantics, partial failure, schema drift, with reconciliation jobs that prove correctness continuously, because 'the webhook fired' and 'the systems agree' are different claims.
Who this is for
Teams whose CRM, billing, warehouse, and operational tools each hold a different version of the truth.
How the work is done
Design the data contract
Field-level mapping with explicit ownership: which system is source of truth for what, transformation rules, and how conflicts resolve. Ambiguity here is where month-end surprises are manufactured.
Choose the sync semantics
Webhooks versus polling versus batch decided by latency need and failure tolerance; idempotency keys and deduplication because exactly-once delivery is a myth to design around, not assume.
Engineer for failure
Retries with exponential backoff, dead-letter queues with alerting, rate-limit compliance, and circuit breakers, plus scheduled reconciliation jobs that compare systems and surface drift before finance does.
Operate with visibility
Sync dashboards (lag, error rates, volume), documented runbooks for common failures, and versioned mappings so schema changes upstream become planned work rather than incidents.
How the API Integrations engagement runs
We begin with the decision, use the evidence that can genuinely change it, and make the reasoning reviewable from first input to final handover.
What we need to begin
- The systems to connect, with API documentation, credentials, scopes, and rate limits.
- The field-level mapping between systems, including the fields nobody currently agrees on.
- The system of record for each field, decided before the build starts.
- Volume, frequency, and acceptable latency for each flow.
If an input is unavailable, we state the gap, its effect on confidence, and the agreed workaround. It is never quietly ignored.
Your four-phase engagement map
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Phase 1
Design the data contract
Field-level mapping with explicit ownership: which system is source of truth for what, transformation rules, and conflict resolution.
-
Phase 2
Choose the sync semantics
Webhooks versus polling versus batch by latency need and failure tolerance, with idempotency keys and deduplication.
-
Phase 3
Engineer for failure
Retries with exponential backoff, dead-letter queues with alerting, rate-limit compliance, circuit breakers, and scheduled reconciliation.
-
Phase 4
Operate with visibility
Sync dashboards for lag, error rates, and volume, documented runbooks, and versioned mappings so schema changes become planned work.
Methods and models we draw on
- Data-contract design
- Source-of-truth mapping
- Idempotency & deduplication
- Retry/backoff & circuit breakers
- Reconciliation-job design
- Observability for pipelines
Methods are chosen for the problem, not the brochure, expect a subset of these, applied properly, plus whatever the evidence demands.
The decision this enables
One version of the truth across systems, verified continuously, not asserted hopefully.