Gestell provides an ETL pipeline that transforms unstructured data into AI-ready databases for large language models. Their platform structures data through enframing and disclosure, enabling accurate and scalable search-based reasoning for LLM applications.
Funding
Funding not disclosed
Founders
Product
Problem
Large language models (LLMs) require structured, AI-ready data to perform effectively, but much of the world's data remains unstructured and difficult to process. Existing ETL solutions often lack the integration and customization needed to prepare diverse data types for optimal LLM performance. This gap hinders the development of accurate and scalable search-based reasoning applications.
Solution
Gestell provides an ETL pipeline specifically designed to transform unstructured data into AI-ready databases for LLMs. The platform employs a process spanning enframing to disclosure, structuring data to enable accurate and scalable search-based reasoning. Gestell handles each step in the structuring process, from chunking and vectorization to graph creation, integrating these steps to ensure scalability. The platform is customizable, allowing users to tailor the structuring process to their specific business needs and data characteristics.
Target Audience
Gestell targets organizations seeking to build scalable GenAI applications that require structured data, including those working with large datasets and diverse data types.
Features
- Comprehensive ETL pipeline for LLMs, handling the entire structuring process from enframing to disclosure.
- Intelligent chunking and LLM-enabled segmentation for optimized search performance.
- Multi-modal data ingestion, supporting diverse file types including PDFs, images, Excel spreadsheets, slides, and videos.
- Customizable vector store tailored for vector embeddings.
- Integrated knowledge graphs to represent contextual relationships within the data.
- Web workspace and API support for flexible implementation.
- Agent-first architecture, utilizing agents throughout processing and retrieval.
- Scalable architecture capable of handling large processing jobs.
- Support for SSO and SAML authentication, role-based permissioning, and data encryption.