Modern systems must communicate—without turning your team into translators.
You can only do business with a customer if you communicate. Today, that means your systems must speak the same language. Integration (APIs, ETL, eventing) and scraping (when no stable API exists) can make that happen—but they require discipline to avoid brittle solutions.
Manual methods—printing reports, re-entering data, copy/paste “sync”—are slow and error-prone. Done right, automation reduces rework, improves decision speed, and lowers operational risk.
Integration is often oversimplified
Moving data from point A to point B is rarely as simple as it seems. Success depends on validation, formats, timing, error handling, and cleanliness. Miss those details and the failure happens at the worst possible moment.
Define Goals
Clarify the business problem, the measurable outcome, and what “done” looks like.
Map Current Processes
Document workflows, data sources, owners, and where the “shadow process” workarounds live.
Design & Proof
Choose the approach (API, ETL, events, scraping), build a proof, and surface hidden requirements early.
Validate Mapping
Test on paper and with a small dataset first—confirm correctness before scaling and automating.
Execute & Monitor
Run production-like loads, implement retries and alerting, and monitor drift—especially for scraped sources.
Get clarity in a free 30-minute consult
Bring one messy integration or scraping challenge. We’ll map the safest approach, flag risks early, and outline a fast proof-of-concept you can actually execute.
Trust—and verify
Automation multiplies results—both good and bad. Automating a strong process creates leverage fast, but automating a flawed process multiplies errors and customer impact.
That’s why “flawless integration on the first try” is rare. Reliable solutions require careful mapping, controlled rollout, and continuous verification—especially when scraping unstable sources.