In 2017 the word was machine learning and the job was quieter than it sounds: let a student photograph a paper answer sheet with a phone and get a real score back. Bubbles, shadows, lighting, a page that will not lie flat.
Five years before anybody was arguing about chatbots.
A year of work and it bled into… another school platform. The testing platform. It worked well with them, their app… a big scholastic platform.
Phil · Innovation Theory, on the record
The Ideas By Nature client roster lists won deals by organisation. One row reads, in the organisation column, Wise/College Board, with two won deals against it. Not two rows. One. Internally it was filed as a single continuous relationship, which is the documentary form of “it bled into”.
The memory and the spreadsheet agree, and neither knew about the other.
In November 2019 the College Board logo was added to our own website’s client strip, alongside ShapeShift, Komodo, SALT, CoinDesk, KeepKey and Neblio. A client we put on the front page.
Printed on the practice answer sheets themselves:
Download the College Board SAT Practice app to instantly score this test.
College Board’s printed instruction
If you’re using our mobile app keep in mind that bad lighting and even shadows cast over the answer sheet can affect your score.
The hard problem, stated by the client, on the paper
Computer vision doing optical mark recognition on a camera frame, in 2017.
That sentence is our inference from the fixtures and the printed instructions, not a claim from a surviving technical document. We are labelling it as inference because no document in our hands names the technique.
Every file in the drive whose title contains WISE is a financial or entity document for a company, and all of it was out of scope. There is no product brief, no wireframe, no screen, no spec and no roadmap for WISE the application anywhere we can reach. The year Phil remembers left nothing we can show you.
We can prove the College Board half. We cannot show you the WISE half.
Both sentences stay here. This page carries no pictures for the same reason: none survive in our hands, and we are not going to borrow a screenshot of somebody else’s app and imply it is ours.
Product · iOS · computer vision · 2017 to 2018 · engagement type: PRODUCT
This page is built from the project drive, and where the record is thin it says so. The fourteen-week College Board workstream is established by a billing file and its tracked hours; that file was not mined for anything else. The client roster it sits beside was last modified January 2020.
No screens from this engagement survive in our hands. The diagram on this page is our own schematic, not an artifact. The instructions quoted are printed on the College Board’s own practice answer sheets.
Everything above this line is in a file somebody can open. Nothing below it is. This is how we would run the same engagement today, with AI in the work — written as an opinion, because that is what it is.
This is the one case on the wall where the thing we built is the thing that got good. Reading a bubbled sheet off a phone camera in 2017 meant hand-built vision work and a stack of known-answer fixtures to test it against. A model does that now, out of the box, in worse lighting than we could handle, and it will read the handwriting in the margin too.
The fixtures would still be the job.
Three fully bubbled sheets with known answers is how you find out whether the machine actually read the paper, and that discipline gets more important as the reader gets more confident, not less. A 2017 vision pipeline failed loudly and obviously. A model fails fluently, hands you a plausible score, and never mentions the shadow across question forty.
The question was whether a student could photograph a paper answer sheet with a phone and get a real score back. Bubbles, shadows, lighting, a page that will not lie flat — and it shipped, in 2017. An assessment finds out what holds before you fund the whole thing. Two days, no charge, answered in writing.
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