It's not all boilerplate of course. But iteration cycle is shorter and the latency matters. We are trying find a formulation, so a lot of experimenting, fine-tuning, checking errors (first via simulations, then real-world with measurements). Then we see what needs to be changed, repeat until error is in desired bounds. But the real-world is messy so we see a problem (lets say adding some certain color does not change the final properties of paint the way predicted) and we go back to the customer, some expert remembers temperature/moisture of the air was different on this day and that affects printing results. Now we need to measure this too and add it to the simulation and add it to the data pipeline. But we may have to wait until the real world has implemented that too (create a process, buy hardware, whatever) and then a while later we finally get real data and can start doing our part. Reducing our latency makes things faster, as you may do something else in between and need to load everything in you head again. The LLM gives results in sometimes minutes: frontend, backend and database. We now have the feature branch where you can upload the data like the customer would do, start the calculation, see the predicted formula. From idea to working prototype. We can focus on making assumptions and checking them quickly. Previously they can make the changes in their Python scripts but getting it in a presentable form took much more work. Now we can go to the customer and present a pretty custom made UI like an end-user would see same day they give us new data or report issues.

The stack certainly could need improvement, but much of the overhead comes from the process being pretty serial because each step depends on the previous. If someone we needs to talk to goes on vacation, sick or has a full calendar this delays it much further. Iteration speed is not linear in such cases. We do a lot of custom solutions and have to implement custom parsers or interface with undocumented ancient software or simply no existing software. Everything is custom, because previously they have no processes, no IT infrastructure or simply only data intended for humans (roughly structured, informally written, ever changing naming schema, typos).