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Gestell
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Gestell: ETL for LLMs.

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This product is listed by LIKETG on behalf of third-party merchants. Products/services/after-sales are all provided by third-party merchants, not official LIKETG products. All activities, benefits, and restrictions are unrelated to LIKETG official. Please identify carefully.

Applicable Scope

Gestell is the ETL for LLMs. We transform unstructured data into AI-ready databases through a revealing process, enabling accurate, scalable, search-based reasoning for your LLMs.

Product Information

What is Gestell?

Gestell is the ETL for LLMs. We transform unstructured data into AI-ready databases through a process of revelation, providing your LLM with accurate, scalable, search-based reasoning.

How to use Gestell?

Gestell is an ETL tool designed for large language models (LLMs), transforming unstructured data into AI-ready databases to enable accurate, scalable search and inference capabilities for LLMs. It also provides software to coordinate and modernize legacy enterprise systems.

Core Functions of Gestell

Transform unstructured data into AI-ready databases
Analyze and map systems with data architecture using AI
Non-intrusive enhancement of mainframe systems
Provide customized solutions to meet business needs
Generate legacy system reports and demo applications
Ensure zero system downtime operation

Usage Scenarios of Gestell

  • Provide accurate, scalable search inference for large language models
  • Coordinate legacy systems with modern business operations
  • Enhance mainframe performance and automation
  • Accelerate the achievement of enterprise-specific strategic goals
  • Deploy intelligent automation and predictive analytics
  • Enhance system intelligence and data architecture analysis

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Disclaimer

This product is listed by LIKETG on behalf of third-party merchants. Products/services/after-sales are all provided by third-party merchants, not official LIKETG products. All activities, benefits, and restrictions are unrelated to LIKETG official. Please identify carefully.
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