Systems Integration: One Source of Truth Across Every System You Run On
Most mid-market and enterprise operations run on a patchwork: an ERP for inventory and accounting, a handful of marketplace channels (Amazon, Walmart, Shopify), a payment processor or two, a shipping carrier mix, and a growing list of point tools bolted on as the business grew. Each of these systems has its own model of "what is true" — its own view of an order, a SKU, a customer, an inventory count — and none of them agree with each other by default. The gap between those views is where inventory oversells happen, where orders get double-shipped or never shipped, where finance reconciles by hand at month-end, and where a support rep has to open four tabs to answer one customer question.
We build the integration layer that closes that gap: the pipes, transformers, and reconciliation logic that move data between systems reliably, in the right shape, in the right order, without silently dropping records when a webhook fires twice or a downstream API times out. This is not an off-the-shelf iPaaS connector wired up and left alone — it is engineered for your specific systems, your specific failure modes, and your specific definition of "correct," with the observability to prove it is working and the resilience to keep working when one link in the chain goes down.
Problems we solve
Every system has a different answer to the same question
Your ERP says 42 units are in stock. Amazon says 50. Walmart says 38, because its last sync failed silently three days ago. Nobody notices until a customer orders something you do not have, and now you are canceling an order and eating a metric ding on a marketplace that already throttles sellers with cancellation problems. Without a canonical source of truth and a reconciliation process that catches drift, every downstream system is a guess.
Point-to-point integrations turn into an unmaintainable mesh
The first integration between two systems is easy. The tenth — when Shopify, the ERP, the WMS, the accounting package, and three marketplaces all talk directly to each other in different formats with different auth schemes — is a tangle nobody fully understands, where changing one field breaks something unrelated three systems away. Adding an eleventh integration takes longer than the first ten combined.
Retries and duplicates quietly corrupt your data
A webhook fires, your server takes 30 seconds to respond because a downstream API is slow, the sender times out and retries — and now you have processed the same order twice, deducted inventory twice, or emailed the customer twice. Without idempotency keys, deduplication logic, and a clear-eyed design for at-least-once delivery, integrations that look fine in testing generate duplicate records under real production load.
How we approach it
A canonical data model with explicit ownership
We define which system owns each piece of data — inventory truth typically lives in the ERP or WMS, order truth in an order-management layer, customer identity behind SSO — and build every integration to read from and write to that canonical model rather than letting each system push its own version of reality. Conflicts get resolved by rule, not by whichever sync happened to run last.
Event-driven pipelines with queues, not fragile point-to-point calls
Rather than wiring each system directly to every other system, we route data through message queues and event streams: a change in the ERP publishes an event, and every interested system — marketplace listings, accounting, the storefront — consumes it independently, at its own pace, with automatic retry and dead-letter handling when a consumer is down. Adding a new system means adding a new consumer, not rewiring the mesh.
Idempotent processing with end-to-end observability
Every inbound webhook and outbound API call carries an idempotency key so retries and duplicate deliveries are collapsed safely instead of double-processed. We instrument the whole pipeline — queue depth, failed-job counts, per-integration error rates, reconciliation drift — so you find out about a stuck sync from a dashboard alert, not from a customer complaint or a finance team doing manual spreadsheet reconciliation weeks later.
What you get
- Integration architecture document mapping every system, data owner, and event flow before a line of code is written
- Event-driven integration layer (queues/webhooks) connecting ERP, marketplaces, payment processors, and carriers
- Data mapping and transformation layer reconciling field-level differences (SKUs, units, tax codes, statuses) between systems
- Idempotency, retry, and dead-letter handling so duplicate or failed deliveries never corrupt downstream data
- Monitoring and alerting dashboards for sync health, queue backlogs, and reconciliation drift
- Connected client portal giving your team full transparency into integration status, error logs, and data flow — no black box
Technologies & integrations
Our delivery process
- 01Discovery
We inventory every system in play — ERP, marketplaces, payment processors, carriers, accounting — and map current data flows, ownership gaps, and where drift already exists today.
- 02Architecture
We design the canonical data model, event/queue topology, and error-handling strategy, deciding system-by-system between an iPaaS connector and a custom integration layer based on volume, latency, and control needs.
- 03Build
We build the integration layer incrementally — connector by connector — with idempotency and retry logic in from day one, not bolted on after the first duplicate-order incident.
- 04QA & UAT
We test failure paths deliberately: dropped connections, delayed webhooks, out-of-order events, and duplicate deliveries, verifying the system reconciles correctly under real-world conditions, not just the happy path.
- 05Deploy & Support
We roll out with monitoring live from day one, then stay engaged for tuning, new-system onboarding, and incident response as your integration surface grows.
Apparel Globe — a multi-channel operations platform
Frequently asked questions
Should we use an iPaaS tool (Zapier, Workato) or build a custom integration layer?
It depends on volume, complexity, and how much control you need over failure handling. iPaaS tools are fast to stand up for low-volume, simple field mappings between well-supported connectors. Once you need custom retry logic, high-throughput event processing, non-standard data reconciliation, or integrations with systems an iPaaS does not support well, a custom layer built on queues and your own transformation logic gives you control an off-the-shelf tool cannot. Many engagements end up hybrid: iPaaS for simple syncs, custom code for anything touching inventory, orders, or money.
How do you prevent duplicate orders or double-processed webhooks?
Every inbound event carries or is assigned an idempotency key, and processing logic checks that key before acting — so a retried webhook or a duplicate delivery is recognized and safely ignored rather than reprocessed. Combined with queue-based delivery and dead-letter handling for events that fail repeatedly, this is designed in from the architecture phase, not patched in after a production incident.
What happens when one of our systems (a marketplace API, a carrier) goes down or changes without notice?
The integration layer is built to degrade gracefully: failed calls go to a retry queue with backoff, persistent failures land in a dead-letter queue and trigger an alert rather than silently dropping data, and other integrations keep running unaffected. When a third-party API changes its contract, the isolated connector for that system is the only piece that needs updating — it does not cascade into the rest of the pipeline.
Will we be able to see what is happening inside the integration, or is it a black box once it is built?
You get a connected client portal showing sync status, error logs, and data flow for every integration in the system — so your team can see exactly what synced, what failed and why, and how current your data is, without needing to file a ticket to find out.