Pathway

Pathway positions itself as the first post-transformer frontier model that solved continual learning, introducing a massively parallel post-Transformer reasoning architecture that enables generalization over time. The platform powers enterprise-grade AI applications including RAG (Retrieval-Augmented Generation) and ETL pipelines, allowing organizations to build apps that serve real-time features, live vector search, and anomaly alerts without requiring separate vector databases or complex technology stacks.

The framework is designed for data engineers, AI developers, and enterprises that need to process live data from 300+ sources with automatic synchronization. Target users include organizations requiring accurate AI insights from terabytes of connected documents and data tables, as well as teams building LLM applications that fetch real-time data. The company has gained traction with major enterprises including DB Schenker, Intel, NATO, Formula 1 teams, La Poste, Transdev, and CMA CGM.

• Real-time data ingestion: Pathway enables easy setup of data ingest from 300+ sources with automatic synchronization, ensuring that AI applications always work with the most current information without manual pipeline updates.

• Live vector search: The platform provides live vector search capabilities without requiring a separate vector database, reducing complexity and fragmentation commonly associated with LLM infrastructure.

• Post-Transformer reasoning architecture: Pathway introduces BDH (Bipartite Dynamic Hierarchy), a massively parallel architecture described as "the missing link between the Transformer and models of the brain," enabling generalization over time and solving continual learning.

• Real-time anomaly alerts: Users can build applications that automatically detect and alert on anomalies in streaming data, enabling proactive responses to critical business events.

• Continual learning capability: The first frontier model architecture to solve continual learning, allowing AI systems to adapt and improve from new data without catastrophic forgetting or requiring full retraining.

• Scalable ETL processing: Pathway handles ETL operations at scale, processing terabytes of connected documents and data tables with high performance even on standard hardware.

• Local deployment support: The framework can run fully locally on standard hardware, with users reporting successful deployment on 11th generation Core CPUs within one hour.

• Access the framework: Visit pathway.com/framework to download the Pathway tooling and access the open-source framework with 119k+ GitHub stars.

• Follow the setup guide: Navigate to the setup guide for installation instructions and environment configuration.

• Explore app templates: Browse the app templates to find pre-built solutions for common use cases like RAG, real-time data pipelines, and anomaly detection.

• Configure data sources: Set up connections to your data sources from the 300+ supported options, enabling automatic synchronization for live data ingestion.

• Build your application: Develop your AI application using Pathway's Python-based framework, implementing real-time features, vector search, or ETL pipelines as needed.

• Deploy and monitor: Deploy your application locally or in your infrastructure, with built-in observability for monitoring data pipelines in production.

• No separate vector database required: Pathway eliminates the need for complex, fragmented infrastructure by integrating vector search directly into its processing engine, significantly reducing operational overhead.

• True real-time processing: Unlike batch-oriented systems, Pathway processes data incrementally and continuously, enabling immediate updates to LLM answers as source data changes.

• Continual learning without catastrophic forgetting: The BDH architecture represents a fundamental advance over Transformers, enabling models to learn continuously from new data without losing previously acquired knowledge.

• Simplified technology stack: Users can build sophisticated LLM applications without managing multiple separate components, reducing implementation delays and system complexity.

• Proven enterprise scalability: Trusted by major organizations including NATO, Formula 1 teams, and global logistics companies for critical, large-scale production deployments.

• Research-backed innovation: Developed by a team including co-authors of Nobel Prize winner Geoff Hinton and the first person to apply attention to speech, with backing from Lukasz Kaiser, co-inventor of Transformers.

• Documentation and setup guide: Comprehensive documentation is available at pathway.com/developers/user-guide/introduction/installation covering installation, configuration, and development patterns.

• App templates library: Pre-built templates at pathway.com/developers/templates provide starting points for common use cases with working code examples.

• GitHub community: Access the open-source framework with 119k+ stars on GitHub for community support, issue tracking, and contribution opportunities.

• Careers and team contact: For direct inquiries, career opportunities, or partnership discussions, use the contact options and careers page available on the website.

• Waitlist for updates: Join the waitlist to receive updates about new features, research developments, and product announcements.

• Pathway Framework: Download the open-source Python framework at pathway.com/framework — requires Python environment, compatible with standard x86 hardware including 11th generation Intel Core CPUs and above.

• Web application: Additional tools and documentation accessible directly in browser at pathway.com, with framework installation via pip or conda.