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Illustrative solution pattern

System integration layer

Reliable data movement between line-of-business systems, with versioned contracts and reconciliation so failures surface.

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Illustrative solution pattern. This describes a solution we design and build, not a completed engagement. The expected outcomes below are design intent, not measured results, and we have not yet delivered this pattern for a client.
Cloud and integrationEnterpriseHealthcareEducation

The challenge

The problem this solves

Systems that should exchange data don't, so staff re-enter the same information in two places. Where integration exists it is often brittle, undocumented, and silent when it fails.

Existing process

The limitation being removed

Data is moved by manual export and import, or by scripts written years ago that nobody owns. Failures are discovered when someone notices a discrepancy, which may be weeks later.

The solution

What was built

An integration layer with explicit, versioned contracts between systems. Operations are idempotent so a retry cannot duplicate a record, transient failures retry with backoff, permanent failures land in a dead-letter queue with the reason attached, and a reconciliation report proves both sides agree.

Implementation approach

  1. Document the contract between each pair of systems: fields, semantics, ownership and change process
  2. Implement transfers as idempotent operations so a retry is always safe
  3. Add retry with backoff for transient failures and a dead-letter path for permanent ones
  4. Build reconciliation reporting that compares both sides and reports differences
  5. Instrument every hop so a specific record can be traced end to end
  6. Run alongside the manual process until reconciliation is clean, then retire the manual step

Technologies used

  • Versioned API contracts with schema validation at the boundary
  • Message queue with retry, backoff and dead-letter handling
  • Idempotency keys on every write operation
  • Reconciliation reporting between source and target
  • Structured logging and distributed tracing across hops

Services provided

Applicable sectors

  • Enterprise
  • Healthcare
  • Education
  • Government & public sector

Security and governance

Controls designed into the solution

Decided before implementation. Every one of these is an architectural choice, which is why they cannot be added afterwards without a rebuild.

  • Schema validation at the boundary; malformed messages are rejected, not partially applied
  • Idempotent writes, so retries and replays cannot duplicate records
  • Dead-letter queue with alerting, so nothing fails silently
  • Reconciliation reporting as continuing evidence that the systems agree
  • Credentials held in a managed secrets store with rotation
  • Data minimised in transit to the fields the target system requires

Outcome

Expected outcome (design intent, not a measured result)

These are design expectations for this pattern, not measurements from a delivered engagement. We would agree how to measure them with you before building.

We have not yet delivered this pattern for a client. If that matters for your procurement, we would rather you knew now.
  • Data is entered once and reaches the systems that need it
  • Integration failures raise an alert instead of being discovered as a discrepancy
  • The contract between systems is documented, versioned and changeable safely
  • A specific record can be traced end to end when something is queried

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Cloud & Infrastructure

Cloud architecture, migration and system integration — designed for the cost, resilience and compliance position you actually need.

Cybersecurity & Compliance

Security engineering, assurance and compliance support — including the controls specific to AI systems.

All solutions

Could this work for you?

Tell us how your situation differs from this example. Where a material uncertainty remains, a bounded proof of concept with a pass threshold agreed in advance is usually the cheapest way to find out.

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