Build vs. buy vs. fine-tune
When a company needs an AI capability, there are three broad paths: buy an existing tool/product, build a custom app on top of an existing model's API (prompting + maybe RAG), or fine-tune/train something bespoke. Most real business problems are solved with the middle option - bespoke fine-tuning is rare and usually unnecessary.
The decision usually comes down to: how differentiated does this capability need to be (generic tools are fine for generic problems), how much data/control do you need over the exact behavior, and what's the cost of being wrong (a bought tool is easy to abandon; a fine-tuned model is a sunk investment). This exact framework applies directly to Kasper's GPS-as-a-Service build-vs-vendor decisions you've already been working through.
Take one open decision from your actual Kasper GPS work (e.g. Wialon/Navixy vs. custom build) and explicitly run it through this three-way framework - you'll likely find you've already been reasoning this way intuitively.
Which path solves most real business AI needs?