A 10-step guide to stages, expertise, time, and ROI — decide whether to build or buy your visual AI solution.
Building visual AI in-house is a genuine option for some teams — and an expensive detour for others. This page captures the decision frame we use with customers before anyone commits headcount to a multi-year platform build.
What the checklist covers
You will work through ten steps that force specificity: problem definition, data rights and retention, labelling and QA economics, model lifecycle ownership, on-device versus cloud inference, latency budgets, security review load, release discipline, and how you will prove ROI to finance.
The goal is not to “score” build versus buy in the abstract. It is to end with a clear picture of calendar time, seniority mix, and recurring cost for a credible v1 — then compare that to buying a layer that already absorbed those lessons.
The artifact
The actual checklist lives in a short PDF so you can share it with procurement, security, and your ML lead without reformatting threads.
Download the PDF — Decision checklist for building visual AI
