How I built a data quality API that runs at the edge in milliseconds

Bad data is quiet. That's the problem. Your pipeline doesn't crash. Your tests pass. Three weeks later someone notices the revenue dashboard is wrong, you trace it back, and find that one column started arriving as strings six weeks ago. The ETL swallowed it. The warehouse stored it. Everything looked fine. I've debugged enough of these to know the pattern. The fix is always obvious in retrospect — validate the data before it enters the system. The hard part is actually doing it without adding another tool, another config file, another YAML-driven framework to maintain. So I built DataScreenIQ — a data quality API. You POST rows, you get a verdict back. No setup. No infrastructure. One call. curl -X POST https://api.datascreeniq.com/v1/screen \ -H "X-API-Key: dsiq_live_..." \ -H "Content-Type: application/json" \ -d '{ "source": "orders", "rows": [ {"order_id": "ORD-001", "amount": 99.50, "email": "alice@corp.com"}, {"order_id": "ORD-002", "amount": "broken", "email": null}, {"order_id