More successful exits with data-driven methods to evaluate founders

Bet on founders with the highest likelihood of success. Identify the best behavioral patterns for winners and red flags for low-growers

How Simpleem helps to find unicorns

AEI helps to find people who can build company from 0 to 10
Detect and measure
Detect and measure behavioral signals during pitching and Q&A session
Identify behavioral patterns (models) of each startup team
AI Inference*
Build correlations between behavioral patterns and traction to identify evidence-based differences between high-performing and low-growing startups
(*) - AI inference refers to the process of using a trained artificial intelligence (AI) model to make predictions or decisions based on new data. It is the final step in the AI model development process, where the model is used to make inferences about data that it has not seen before. Inference is typically used to generate insights or to make decisions based on the patterns and relationships learned by the model during the training phase.


Monitor founders progress and re-evaluate precisely
  • Make faster and more accurate predictions of success
    Increase the chances of finding a top-performing startup by expanding your pipeline and making the assessment process faster, leveraging the law of large numbers (LLN)*
  • No bias
    Minimize false positives (investments in FTX, Theranos, etc.) and false negatives (pass investments in Airbnb, Apple, etc.) because of human bias
  • Bet big on winners, fold on losers
    Cut your losses early on your bad hands and to keep re-investing in those that still have potential based on the additional information gathered
(*) - The law of large numbers is a statistical principle that states that as the number of observations or trials increases, the average of the observed values will tend to be more representative of the overall population.