Algolytics

We've just published a report comparing 10 standardization and geocoding services on the same 2,500 real courier addresses, measured against a reference set consistent with TERYT, EMUiA, and PNA.

It is the first time we put these tools head-to-head on the same real data, instead of comparing vendor claims.

We score each service (Azure, Here, TomTom...) on three things. Standardization: is the address clean enough to match records reliably. Geocoding: how accurate the location is. Trust: how often a "high quality" result is wrong.

We also ran a cost simulation: depending on which tool you choose, the extra cost can differ by almost PLN 800,000 a year per 100,000 shipments, before the tool's own price. You can rerun it on your own volume in the Excel we built.

The costliest error is not a missing match. It is a confident-looking wrong one that passes automation and surfaces later, where the fix costs more.

Link to the full report and the Excel in the first comment.

We’ve just published the recording of our recent webinar on running machine learning in production.

If you’re working with ML in a real environment, you’ve probably seen that the hardest part isn’t building models - it’s deploying them, connecting them with data and decision logic, and keeping the whole setup reliable at scale.

That’s exactly what we covered during the session.

You can now watch the full recording here:
👉 https://eu1.hubs.ly/H0vlRqQ0