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Most integration failures aren’t technical.
They are failures of ownership, contracts, and data clarity. Our approach focuses on clarity first, then delivery in controlled increments.
Define what “good” looks like in business terms:
latency, accuracy, data ownership, and support responsibilities.
Define explicit data contracts: canonical objects, identifiers, consent and preference models, event schemas, and error semantics. Assumptions are replaced by contracts.
Select integration patterns based on reality, not theory: real-time vs batch, pub-sub vs sync, API-led vs direct calls. Patterns serve operations, not architecture slides.
Build with discipline: automated tests, sandbox strategy, performance validation, controlled releases. Quality is designed in, not inspected later.
Run integrations as living systems: monitoring, alerting, reconciliation, documentation, and continuous iteration.
Integration is operated like a product, not closed as a project.
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