What happens when systems don't talk
The most visible symptom is double data entry: someone copies data from one system to another because there's no connection between them. But the real cost lies deeper. Every manual hand-off introduces a chance for error, and every data error spreads quietly until someone catches it weeks later.
The less visible — and more expensive — symptom is being unable to answer simple questions. How much was processed yesterday by channel, which transactions are still pending, where a request got stuck. If the answer means querying three systems and reconciling by hand, the organization is flying blind between one report and the next.
Our approach
We start with an inventory: which systems exist, what each one exposes, which integrations are already in place and which were built without documentation. In organizations with years of operation, there are always connections nobody remembers making.
From there we define the integration architecture: APIs where the system exposes them, middleware where orchestration is needed, and alternative mechanisms for legacy systems that were never designed to connect. Each option carries different risk and maintenance consequences, and we lay them out before a decision is made.
What we require of an integration
It must be observable. An integration that fails silently produces inconsistent data for days before anyone notices. That's why we build in monitoring and alerts from the design stage, not as an afterthought.
It must handle failure. Transactions that don't go through need automatic retries, a pending queue and notification to someone responsible. Without that, failed records are simply lost.
It must be traceable end to end. For any transaction, the organization must be able to reconstruct where it went, when and with what result. In supervised entities, this stops being a best practice and becomes a requirement.
Controlled migration
Connecting new platforms to systems that support day-to-day operations requires a coexistence plan. The new and old systems run in parallel for a defined period, results are compared using real data, and only then is the old system switched off.
It's slower than a direct cutover. It's also the difference between finding a problem in a controlled environment and finding it with customers waiting in line.