Cleora PRO

Cleora.AI | Create Your Enterprise Embeddings with 1 Click is an AI-powered embedding tool designed for data science and analytics teams to generate high-quality graph embeddings quickly without requiring expensive hardware. Cleora focuses on fast, scalable embeddings for large graphs, enabling enterprise-grade recommendations, segmentation, propensity modeling, and more. It produces behavioral embeddings from relational data (e.g., purchases, clicks, page views, transactions) and supports deployment in both open-source and enterprise environments.

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What it does

• Builds embeddings for entities in large graphs (clients, products, stores, accounts, etc.) using fast, stable, and scalable random projections.
• Handles diverse data types such as ecommerce events, banking transactions, clickstream data, textual data, and more.
• Produces embeddings that reflect behavior history, suitable for downstream tasks like recommendations and propensity modeling.
• Offers Cleora PRO (Enterprise) and Cleora Open Source, with Cleora 2.0 delivering automatic scaling, ease of use, performance optimizations, and new features like item attributes support.

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Key benefits

• 200x faster than DeepWalk; 4x–8x faster than PyTorch-BigGraph by Facebook.
• Two orders of magnitude faster than Node2Vec or DeepWalk; capable of embedding graphs with billions of edges on a single machine without GPUs.
• Inductive capability: embeddings for new entities can be computed on-the-fly.
• Cross-dataset compositionality: embeddings from multiple datasets can be averaged to yield meaningful vectors.
• Deterministic starting vectors ensure stability across similar datasets.
• Extreme parallelism and Rust-based implementation for performance.

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Use cases

• Recommender Systems
• Client Segmentation
• Propensity Prediction
• Lifetime Value Modeling
• Churn Prediction
• Other enterprise graph-based models

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How it works

• Embeds entities by leveraging relational data and behavioral history.
• Uses efficient graph embedding techniques to generate n-dimensional vectors.
• Automatic graph detection and minimal data requirements (as few as 3 columns extracted from your DB in Cleora 2.0).
• Produces embeddings that can be used directly for downstream ML tasks, analytics, and recommendations.

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Technical highlights

• Implemented in Rust with thread-level parallelism for all calculations (input loading excluded).
• Dim-wise independence allows flexible merging and integration with downstream models.
• Highly scalable: capable of handling extremely large graphs on commodity hardware.
• Open-source availability with a GitHub repository and a vibrant community.

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How to choose between editions

• Cleora Open Source: for teams who want control, transparency, and local deployment.
• Cleora PRO (Enterprise): for organizations needing structured support, governance, and scalability in an enterprise setting.

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Core Features

• 1-click embedding creation for enterprise graphs
• Extremely fast embedding generation (2x+ faster than comparable methods; up to 200x faster than some baselines)
• Handles large-scale graphs without GPUs on a single machine
• Inductive embeddings for new entities
• Cross-dataset averaging for stable multi-source embeddings
• Automatic graph detection and minimal data requirements
• Rust-based implementation with high parallelism
• Open Source and Enterprise (Cleora 2.0) with improved scalability and item attribute support

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Safety and Compliance

• Designed for enterprise use with emphasis on scalability, reproducibility, and governance. See the official GitHub and documentation for licensing and usage terms.

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Related Insights & Resources

• Open-source repository and community discussions
• Enterprise deployment guides and benchmarks
• Research articles comparing Cleora to other graph embedding methods