Company

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 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

In 2026 the Company set its course to re-organize along business lines:

SPARKDIT
eCommerce

Brands are doubling their conversion rates and boosting sales by 20%, without intruding on customer privacy. Transforming a digital agent into a true personal shopper needs hyper-personalization.

PAXDI
Healthcare

Doctors are revolutionizing healthcare with Patient-Centered Shared Decision Making (SDM) solution that harnesses tradeoff-based decision intelligence. When it comes to selecting a treatment, 'good enough' simply isn’t!

STAY TUNED
Aerospace and Defense

A leader in aerospace has developed a solution powered by human-like intelligence, reducing the impact of flight delays by 50% and setting a new benchmark in air transportation. This invention was enabled by seamlessly combining expert knowledge with advanced data science.

STAY TUNED
Financial Services

Young investors are trading off sustainability, climate impact, and corporate ethics over short-term profits and revenue growth in their strategic investment decisions.

Our Executive Board

Our Advisory Board Dream Team

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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