Good post Bruce. I’ve run into a lot of the same issues you described, especially working with last-mile and delivery companies where the addresses are a total mess. Things like “123 Main St, red gate, call 555-1234 when you arrive, Apt 4B” are very common, and even Google struggles when there’s too much extra info mixed into the address.<p>Because of that, I ended up building a tool called AddressHub that focuses mostly on cleaning up the input before sending it to geocoders. It tries to remove instructions, phone numbers, weird formatting, and fixes common spelling mistakes or street name variations. Then it sends the cleaned address through multiple geocoders, checks how close the results are to where they should be, and picks the best one.<p>It also caches results when possible (depending on the API terms) and tries open data first to save costs. Only if open data does not have a good result, it falls back to the paid geocoders.<p>This has helped a lot with reducing wrong deliveries caused by addresses that look fine at first glance but are actually way off. If anyone’s interested, the project is at www.address-hub.com. Always happy to get feedback if people have ideas for making it better.