Digital Products & Engineering
API Integrations
We connect your ERP, CRM, payment, invoicing, and shipping systems with integrations that recover from failures and tell you when something is wrong.
The problem
Our systems are supposedly connected, but the numbers disagree and we usually hear about failures from customers.
Almost any integration works on launch day. Trouble shows up later: a partner API goes down, a token expires, you hit a rate limit, a field format changes without notice. With no retries and no monitoring, records vanish and nobody knows.
The costliest misconception is treating integration as moving data. The real decision is ownership. Which system is the source of truth for an address, for stock, for price? Leave that unwritten and two systems will overwrite each other.
Point-to-point links also pile up, until nobody can draw the full picture or predict what a replacement would break.
You may recognize this
- Someone reconciles two systems by hand every week.
- No current diagram shows which systems talk to which.
- The integration's author left, and nobody wants to touch the code.
How we work
Map systems and ownership
We chart every existing connection and assign a source of truth and flow direction per data field.
Agree on the contract
Endpoints, error codes, pagination, rate limits, and versioning go into an OpenAPI spec both sides approve before code starts.
Engineer for failure
Writes carry idempotency keys, retries use exponential backoff with jitter, and webhooks are signature-verified. Messages that exhaust retries land in a dead-letter queue for inspection and replay.
Reconcile, observe, protect
Scheduled jobs compare records across systems and report differences. Correlation IDs trace requests, alerts watch error rates and queue depth, secrets rotate from a vault, and logs mask personal data under KVKK.
What we deliver
- System map and source-of-truth decisions
- OpenAPI contracts
- Integration services and webhook receivers
- Dead-letter queue with replay tool
- Reconciliation jobs and exception report
- Alert rules and failure runbooks
How we measure
- Dead-letter queue volume, which shows where data gets stuck.
- Time to detect a failure, which tells you whether you see problems before customers do.
- Reconciliation differences, the actual evidence of consistency between systems.
A wholesale distributor
Illustrative scenario: A wholesale distributor exports dealer portal orders to its ERP in a nightly file, and staff fix rejected rows every morning. The flow moves to a queue-based API integration. While the ERP is down for maintenance, orders wait and process in sequence, and unmatched records go to a named owner.
Common questions
What if the other system has no API?
There's usually still a way in: a database view, file exchange over SFTP, or a vendor service. We wrap it in an adapter, so nothing else changes when a proper API arrives.
Should we use an iPaaS tool or custom code?
Make, Zapier, or n8n handle simple, low-volume flows well. Money movement, high volume, or strict ordering usually justify a custom service, because you need direct control over retries and reconciliation.
Who maintains it afterward?
Settle that before launch. Runbooks, alerts, and a replay tool let your team clear routine failures, while upstream API changes need planned maintenance.
Before we work together, let's think together.
Requests are answered by the founder.