Art has spread worldwide, and our aim is to expand the art market by connecting artists, galleries, and collectors. Founder Ben Gulak envisions informed connections between creators and collectors, fostering unexpected and unique matches.

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Core of NALA

At the core of NALA (Networked Artistic Learning Algorithm) lies the recommender engine, built using the world's largest art database. NALA employs data science to optimize pairings of artists with collectors and galleries, aiming to create good fits between them. The system utilizes three data sources, combining in-app user activity with global market trends through a Hybrid Recommender that utilizes Deep Learning, content filtering, and collaborative filtering.

Filtering & Recommendations

Content filtering recommends similar items in known categories, while collaborative filtering personalizes recommendations, finding unique synergies between genres to offer appealing suggestions beyond the obvious choices. User feedback continually improves the system, and as more users join, NALA becomes smarter, refining its matching process for greater precision.


Our goal is to declutter and bring clarity to the rapidly changing art industry using New Technologies and Data Science. NALA, an data science platform developed in-house, bridges the gap between data science and artistic expression. It generates personalized art suggestions for Art Lovers, Collectors, and Arts Professionals, facilitating engaging and profound connections.

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Machine Learning

For artists, NALA considers over 20 unique data points, including market movements, gallery partnerships, art fair attendance, and auction records. It uses Machine Learning to identify connections and driving factors. Even artists without gallery representation are considered based on auction records and social media trends, allowing more artists to participate in the open market.

Expanding Market

Similarly, NALA analyzes galleries using data like past shows, art fair attendance, average selling price, and geolocation preferences. The platform's ultimate aim is to expand the market by fostering connections between artists, galleries, and collectors, resulting in unexpected and meaningful pairings between creators and collectors.


Our team combines art enthusiasts and computer scientists, led by our founder who is a painter and an M.I.T. Computer & Data Scientist. With background Data Science, we also have a Community Manager experienced in London's premier Street Art Galleries. Together, we share a passion for creating unique and powerful connections.

Benjamin Gulak


Lucas Amaral

Software Engineer

Penelope Sonder

Chief Operating Officer

Paulo Brancher

Lead Designer

Tim Wood

Data Scientist

Tim Lizardi

Artist Relations

Igor Wendt

Software Engineer

Michael Ulguim

Software Engineer

Caleb Rollins

Computer Scientist