Ensemble builds representation-learning technology to help organizations train accurate ML models on limited, sparse, and high-dimensional data without extensive feature engineering. The company’s product, Dark Matter, is an embedding model that creates rich representations of data to improve any model’s performance across metrics. Its team developed a net-new representation learning algorithm that outputs embeddings to approximate hidden relationships and distill signal from noise. Feeding models statistically optimal, learned features is intended to improve performance metrics and reduce engineering time. Ensemble said it will use the new funding to expand operations and accelerate development efforts.

Total raised

$3M

Funding rounds

1

Latest round

Seed

Latest activity

Sep 2024