TurinTech secures $20M to Tackle ‘Vibe Coding’ Issues with AI-Powered Code Optimization

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TurinTech secures $20M to Tackle ‘Vibe Coding’ Issues with AI-Powered Code Optimization
© Laurence Dutton / Getty Images

UK-based AI startup TurinTech has raised $20 million in funding to refine its AI-driven code optimization platform, Artemis, addressing inefficiencies caused by ‘vibe coding’—a hands-off approach to AI-generated software development.

The funding round includes a previously undisclosed $15M Series A led by Oxford Capital, with participation from Circle Rock and IQ Capital.

Fixing AI-Generated Code Inefficiencies

With one-quarter of Y Combinator startups now relying on AI to generate 95% of their codebases, TurinTech warns that AI-generated code often leads to inefficiencies, security flaws, and technical debt.

Artemis, TurinTech’s AI-powered platform, offers:

  • Automated code validation and optimization to improve performance and scalability.
  • Security checks to eliminate vulnerabilities in AI-generated software.
  • AI-driven “evolutionary coding”, refining codebases for efficiency and reliability.

CEO Dr. Leslie Kanthan explained:
“As AI models generate more code, inefficiencies multiply. Artemis finds and fixes these issues, making software faster, more secure, and resource-efficient.”

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The Growing Demand for AI Code Optimization

  • 75% of enterprise software engineers will use AI coding assistants by 2028 (Gartner).
  • 63% of developers already integrate AI-generated code into their workflow (Stack Overflow).
  • TurinTech has onboarded major blue-chip companies and banks as early adopters.

What’s Next for TurinTech?

With this funding, the company plans to:

  • Expand Artemis’ capabilities beyond optimization to full-scale AI software validation.
  • Grow its engineering and research teams to enhance AI-driven coding tools.
  • Launch Artemis commercially later this year, after a successful early-access program.

With $20M in new backing, TurinTech is positioning itself as a key player in AI-driven software engineering, ensuring AI-generated code is efficient, secure, and enterprise-ready.

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