About

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

a placeholder illustration

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, 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.

NALA for Art Lovers

Vision

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.

a placeholder illustration

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.

NALA for Artists

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.

Team

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 part of nala's teamBenjamin GulakFounder
Lucas Amaral part of nala's teamLucas AmaralSoftware Engineer
Penelope Sonder part of nala's teamPenelope SonderChief Operating Officer
Paulo Brancher part of nala's teamPaulo BrancherLead Designer
Tim Wood part of nala's teamTim WoodData Scientist
Nick part of nala's teamNickSoftware Engineer
Igor Wendt part of nala's teamIgor WendtSoftware Engineer
Michael Ulguim part of nala's teamMichael UlguimSoftware Engineer
Fakhar Mahmood part of nala's teamFakhar Mahmood-
Caleb Rollins part of nala's teamCaleb RollinsComputer Scientist