The startup operates a code optimization platform that utilizes artificial intelligence to generate efficient machine-learning model code from raw data and enhance the performance of existing code. This technology reduces development time and computing costs for businesses engaged in data-intensive applications.
Funding
$14.6M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
IIFounders
Product
Problem
Software development and data science teams face challenges in optimizing code performance, managing technical debt, and efficiently building machine learning models. Traditional methods often require significant manual effort, leading to increased development time, higher computing costs, and slower time-to-market for data-driven applications.
Solution
TurinTech AI offers an AI-driven platform designed to automate and enhance code optimization and machine learning model creation. The platform includes two primary products: Artemis AI and evoML. Artemis AI leverages generative AI to analyze, refactor, and optimize existing codebases, improving performance, quality, and security while reducing cloud costs and energy consumption. EvoML automates the entire machine learning pipeline, from data cleaning and feature engineering to model training and validation, enabling faster insights and better decision-making. TurinTech's solutions help organizations reduce technical debt, accelerate model creation, and improve the overall efficiency of software engineering and data science workflows.
Target Audience
TurinTech AI targets software developers, performance engineers, DevOps engineers, data scientists, business analysts, financial analysts, and risk analysts seeking to improve code performance, automate machine learning workflows, and reduce development costs.
Features
- **Artemis AI:**
- Code analysis and refactoring using generative AI
- Optimization for performance, security, and code quality
- Reduction of cloud costs and energy consumption
- Integration with existing repositories and development tools
- Real-time validation and benchmarking for enterprise-grade quality
- **evoML:**
- Automated machine learning pipeline for data cleaning, feature engineering, and model training
- Generation of production-quality ML models
- Support for various use cases, including anomaly detection, time series forecasting, and fraud prevention
- Multi-objective optimization based on custom-defined metrics
- Code download and customization for flexible deployment
- GenAI-based synthetic data generation for quick prototyping