The thing I did makes the call unnecessary. So there's no speech-to-text involved. Detail: deliveries in the Middle East. There's no zip codes, street names ar

The thing I did makes the call unnecessary. So there's no speech-to-text involved. Detail: deliveries in the Middle East. There's no zip codes, street names are iffy, people don't know their address, there's multiple names for everything (at least english and arabic), there's no "database" for lookups (Gmaps/OSM does not have good enough coverage/accuracy). So delivery companies, when they accept a package for delivery, get the (customer_name, customer_phone, customer_address) tuple (and the physical package), where everything is totally freetext, sometimes the address is like "I live in the X laborcamp, near the gate, call me when you get here". So by default there's a large call center which calls the recipient, and the CC agent tries to figure out where the driver should go based on the conversation. The outcome of the phonecall is a (lat, lon) that the CC agent drops on Gmaps. Then, the next day, packages are assigned to drivers based on the (lat, lon), each driver has a zone (cities are broken into zone polygons). Challenge: given the historic delivery data, can you (how well can you?) automate the (customer_name, customer_phone, customer_address) -> (lat, lon) prediction? Answer: as I wrote above, it's doable for 40-80% of orders, depending on what accuracy we're willing to accept, with fairly simple ML.