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CGFT AI Finetuned On Your Codebase

CGFT
Contact for Pricing

Home: https://www.cgft.io

  • #Machine Learning
  • #code completion
  • #enterprise software
  • #custom models
  • #Software Development
  • #VPC

What is CGFT?

CGFT specializes in creating custom-trained Large Language Models (LLMs) designed specifically for engineering teams' unique needs. The platform focuses on secure deployment within Virtual Private Clouds (VPCs) and optimizes various tasks throughout the software development lifecycle.

The service employs repo-specific finetuning techniques that have demonstrated a 50% improvement in code completion accuracy. This approach is particularly valuable for enterprise-grade codebases that contain unique patterns and frameworks, making it an essential tool for organizations seeking to enhance their development workflow with AI assistance.

Features

  • Custom LLM Training: Finetunes models specifically for your codebase
  • Secure Deployment: Operates within your VPC for maximum security
  • Enhanced Accuracy: 50% more accurate code completions through repo-specific finetuning
  • Enterprise Integration: Optimized for complex codebases and frameworks

Use Cases

  • Code completion for enterprise codebases
  • Custom AI model development for engineering teams
  • Secure AI deployment in corporate environments
  • Software development lifecycle optimization
  • Repository-specific code pattern learning

FAQs

  • What improvement in code completion accuracy can be expected?
    According to CGFT's research, their repo-specific finetuning pipeline yields 50% more accurate code completions compared to standard models.
  • Where are the AI models deployed?
    The custom LLMs are securely deployed within your organization's Virtual Private Cloud (VPC).
  • What types of codebases is CGFT suitable for?
    CGFT is particularly suited for complex, enterprise-grade codebases that have unique patterns and frameworks.

Related Queries

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CGFT Uptime Monitor

Average Uptime

100%

Average Response Time

163.71 ms

Last 30 Days

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