Vector complexity audit for design educators

A design instructor assigns students a branding project. Half the class is on school computers, half on personal laptops of wildly varying specs. By week three, a third of the class is falling behind not because they lack skill but because their machines are choking on files they built by following the instructor's own tutorials — tutorials that demonstrated techniques on a high-end machine where nothing lagged. The instructor has no visibility into why specific students are struggling until they show up to office hours in frustration.

This gap exists because the people who write vector software curriculum and teach it are overwhelmingly on powerful machines — they've self-selected into creative careers where hardware investment is normal. They genuinely don't know their students' files are 400MB with 2,000 unnecessary anchor points because nobody has ever shown them that information in a usable form. The software itself gives no feedback on file complexity relative to hardware load; it just slows down and crashes without explanation.

What's needed is a file analysis layer that reads a student's .ai or .afdesign file and produces a plain-language report: path count, redundant anchor density, embedded raster resolution, live effect complexity, and an estimated RAM floor to render it smoothly. The instructor can review this alongside student work and give concrete feedback — 'your embedded logo is a 12MB PNG, replace it with a linked 300dpi version and your file will run 60% lighter' — instead of just telling struggling students to get more RAM.

This is a business and not a feature because Adobe and Affinity have no incentive to tell users their files are overbuilt — complexity is a sign of engagement, and engagement metrics justify subscriptions. An educator-facing audit tool, sold to design programs and bootcamps, recurs every semester with new cohorts of students on underpowered hardware. The buyer (the institution or instructor) is different from the user (the student), which means complaints from students never reach the software vendor as purchasing pressure — exactly why this has stayed broken.

What to build

Build a web-based file analyzer that accepts uploaded .ai and .afdesign files, parses path count, embedded asset resolution, live effect layers, and anchor point density, and outputs a one-page complexity report with specific reduction recommendations tied to estimated RAM savings.

Where to start

Launch specifically for bootcamp instructors who teach Affinity Designer (lower-income students more likely to own budget hardware, and Affinity's open format is easier to parse) — one partnership with a mid-sized design bootcamp gives you a cohort of 30–50 files per intake to validate your complexity model.

The hard part

Parsing .afdesign and especially .ai files server-side accurately enough to produce trustworthy RAM estimates requires building and validating a complexity-to-memory model across many real-world files — getting this wrong early (overestimating or underestimating) destroys instructor trust before word-of-mouth can build.

How it makes money

Annual license per instructor ($120–180/year) with bulk pricing for institutions buying 5+ seats; a free tier limited to 3 file uploads per month lets instructors experience the report before committing.