An audience-intelligence decision engine, co-founded and designed end to end
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slateroom.ai
The Venture
SlateRoom predicts how an audience will respond to a piece of content and turns that prediction into a decision a team can actually use. One read on any asset (an idea, a rough cut, or a published episode) tells every role what to do next: the recommendation, the evidence, the key risk, and the next actions. I co-founded SlateRoom in 2026 as a joint venture between SOAR Productions and Buildcraft, and I am the architect of the engine.
The Design Problem
A prediction is worthless if the person reading it trusts it too much or too little. Every SlateRoom report has to show not just a recommendation but its evidence: what the read draws on, where attention falls away, and how confident the engine is. I designed the brand (an editorial cream-and-ink system built for evidence-led credibility), the site, and the decision reports themselves: one engine serving three role views (Editor, Showrunner, Publisher), each translating the same model output into recommendations, retention curves, and scorecards a non-expert can calibrate their own judgment against.
Calibration
On the model side, I designed the four-axis calculation, built the pipeline that produces every report, and calibrated the predictions against real behavioral data: a +0.043 lift measured across a corpus of 2,400+ videos. Every claim carries a validation tier (Validated, Calibrating, or Directional), and the engine never ships a number it can't stand behind: once a show airs, the forecast is held against what viewers actually did, so every read sharpens the next.
The Research Question
SlateRoom is where my applied work became a research question: how should an AI system represent its evidence and uncertainty so a person calibrates trust correctly and stays accountable for the decision? I have lived that question here as a builder, on both sides of the model at once, and it is the question I now want to study as research. It is the clearest expression of the thing I care about most: making complex, quantitative systems feel obvious to the people who rely on them.
Co-founder
Product Design
Brand
Data Visualization
ML Calibration
