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Our History
Our History
Millions invested in R&D. Years of Research. 200 person year of development from some of the brightest minds in AI and technology, lead us to a major breakthrough.
That invention was the foundation that allowed us to incorporate in February, 2023.
We were nominated as MIT Top 10 AI Startups in 2023.
We were the Selected Vendor for Recommendation Engines by Gartner.
We raised the first seed round in June, 2023.
Signed our first eCommerce customer in Q1 2024.
Signed our second and third eCommerce customers in Q2 2024.
Our Mission
Our Mission
Sparkdit mission is to teach computers to make decisions like humans. We set this objective, not because it is easy, but because it is hard. Because this goal will serve to organize and measure the best of our energies and talent, and will enhance humankind by stimulating progress.
Making the right decision is pertinent and valuable in almost any field; whether making the right purchasing decision in eCommerce, or deciding when to launch the next space shuttle, or selecting the location to build a factory, or choosing the medical treatment that fits best a patient needs, or swapping airplanes to minimize the delays impact, the goal is to drive better outcomes.
However, to make the right decision, it must be void of bias, greed, and should be as objective as feasible. This is not to say that human emotions should not be taken into consideration. It simply means that the factors to be considered into a decision should not stem from hidden agendas thus our model and culture is founded on transparency.
Why making the right decision matters? Because as Lincoln put it: "Right is Might"
Our Executive Team
Our Executive Team
Our Executive Board
Our Executive Board
Our Advisory Board Dream Team
Our Advisory Board Dream Team
Testimonials
Testimonials
Michael Schrage - Research Fellow at MIT
World Authority in Recommendation Engine and author of
Recommendation Engines and The Innovator’s Hypothesis
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
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