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Evolution.INC
The AI-for-AI Platform That Builds a Forever Evolving AI

Get the most optimal AI solution, which is often unattainable by human AI engineers,
using an AI that does all the required data science work for you, so you don’t have to.

AI-FOR-AI MAXIMIZES BUSINESS IMPACT

The KPIs of almost any AI system can be improved… but not necessarily by humans.

HARDWARE & SOFTWARE FOR EVOLVING AI

'Earth' is a parallelized platform running Genetic Algorithms that evolve AI systems.

PROVIDING A SPECTRUM OF SOLUTIONS

From basic access to ‘Earth’ to customized implementation by our network of partners.

How genetic algorithms do AI-for-AI

EVOLUTION.INC Leadership

Evolution co-founders have gone through quite some evolution...

Evolution.inc was founded by three entrepreneurs with a shared vision to reshape the AI space, and boasts over a decade of success working together in both research and commercial arenas. Together they have applied AI systems across various industries, such as healthcare, eCommerce, FinTech, gaming, LegalTech, and higher education.

Our Customers

Our Team

Dr. Ronen Tal-Botzer

Co-Founder & CEO

Gil Kotton

Co-Founder & CTO

Dr. Lee Ben-Ami

Co-Founder & Chief Scientist

Dr. Nir Pour

VP product

Beny Rubinstein

Senior Strategic Advisor

Dr. Adir Sommer

Director of MedTech

Prof. Anna Zamansky

Scientific Content Advisor

Ran Homri

Senior Data Scientist

Dean Vitenberg

Product & Operations Manager

Roey Daniel

Software Engineer

Tal Maierovicz

Data Scientist

Roy Abend

ML-Ops Engineer

Elad Goldberg

Software Engineer

Matan Medina

Sr. Algorithms Developer

Amir Ben Izhak

Data Scientist

Katya Novgorodov

Knowledge Engineer

Nir Peled

Data Engineer

Dr. Lee Ben Ami

Co-Founder &
Chief Scientist

Dr. Ronen Tal-Botzer

Co-Founder &
CEO

Gil Kotton

Co-Founder &
CTO

Dr. Nir Pour

VP product

Beny Rubinstein

Senior Strategic Advisor

Dr. Adir Sommer

Director of MedTech

Prof. Anna Zamansky

Scientific Content Advisor

Matan Medina

Software Engineer

Tal Maierovicz

Data Engineer

Elad Goldberg

Data Scientist

Dean Vitenberg

Product & Operations Manager

Ran Homri

Senior Data Scientist

Roey Daniel

Data Scientist

Roy Abend

ML-Ops Engineer

Amir Ben Izhak

Data Engineer

Nir Peled

Data Engineer

Katya Novgorodov

Knowledge Engineer

AI/ML Features

Data Aggregation & Enrichment

Treat data itself as a product. Have dedicated algorithms improve it before the ML parts.

Assess the impact of your data properties regarding size, noise and sparseness levels.

Employ various simulation techniques in order to overcome data challenges.

Feature Selection/Engineering

Transform raw data into more meaningful representations to enhance machine learning.

Reduce big data jungles to the optimal combination of the most informative features.

Generate new, creative feature relations by automatic mathematical manipulations.

Split Data Between Different Models

Instead of having one model to treat the whole dataset, split data into several packages.

Run a variety of algorithms, each on its own data package, to combine their advantages.

Create new boosting mechanisms of various datasets and algorithms working in parallel.

Classification - Evolved

Evolve traditional structures of classification models to express complex behaviors.

Improve ML training processes to reach the globally-optimal tuned model.

Create more free-form classification rules to increase inference accuracy.

Clustering - Evolved

Free your algorithm from restricted centroids or linkage methods.

Integrate more domain expertise into cluster definitions.

Match data points based on real proximity rather than clustering procedures.

Pattern Recognition - Evolved

Catch hidden phenomena by considering novel pattern definitions.

Discover more authentic associations between patterns.

Formulate new, more predictive distance metrics between patterns.

Hyperparameter Optimization

Evolve your current AI system automatically by tuning its parameters for better results.

Run a powerful trial-and-error search of multiple parameters and combinations at once.

Repeat this process over and over again (24/7 GA), to always strive for perfection.

Prediction & Association Rules

Project time series into either less or more complex dimensions.

Align historical events to each other according to flexible rules.

Find hidden relationships between features to better understand data process behavior.

Optimized ML Pipelines & Architectures

Mix regression models with themselves or with classification models.

Make large-scale expeditions to draw insights from raw data.

Predict better by finding unexpected relationships between hypotheses.

Our Little Family

Let Evolution Do The Work

Mail: contact@evolution.inc

Phone: +972-73-7373737

Office: 40 Tuval St., 40th Floor, Ramat-Gan, Israel

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