Technology

"as a ‘research fellow’ at the mit sloan school’s initiative on the digital economy and author of a popular text on ‘recommendation engines,’ i have the opportunity to see and review many excellent efforts and initiatives at the intersection of algorithmic innovation and advice….as an advisor to the company, i am pleased to say how impressed i am with the progress and substance made here…..happy to answer any questions….

Sparkdit delivers demonstrable disruption in applied AI: real-time, user-driven trade-off modeling at scale. Fusing generative AI, expert logic, and statistical inference, This platform provisions  interactive, explainable decision that mimic/emulate human judgment. The core achievement: Encoding preference behavior as parameterized utility curves and exposing them through intuitive interfaces—sliders, bubbles, and voice-guided prompts. These creates dynamic feedback learning loops between user intent and system output. That’s novel.

Unlike most conventional recommender systems, Sparkdit can infer why decisions are made—reverse-engineering trade-offs from outcomes or behaviors, with or without data. This allows/enables personalized, justifiable recommendations that users can interrogate, test and learn to trust.

Technically sharp; Commercially validated. In a recent A/B test ‘bake-off,’ it significantly outperformed Salesforce, SAP, and Google rivals—achieving 1.84x conversion.

So this goes beyond AI with a glittering UX veneer: we have a virtuous cycle where UI becomes intelligence amplifier. AI doesn’t just predict choices—it explains them, negotiates their trade-offs and advises how they can be - and learn to be - better.

respectfully"

michael schrage
mit sloan school