Symflower provides a platform that combines static, dynamic, and symbolic analyses with large language models (LLMs) to enhance software development efficiency and quality. By benchmarking and fine-tuning LLMs for specific programming environments, the company improves code generation accuracy and reduces execution times by an average of 29%.
Funding
$773.6K 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.
Founders
Product
Problem
Large language models (LLMs) in software development often produce inaccurate code, suffer from hallucinations, and exhibit slow execution times, hindering developer productivity and code quality. Selecting the optimal LLM for a specific programming environment and use case remains a challenge due to the vast number of available models and their varying performance characteristics.
Solution
Symflower provides a platform that combines static, dynamic, and symbolic analyses with LLMs to enhance software development efficiency and code quality. The platform benchmarks and fine-tunes LLMs for specific programming environments, identifying the best-fitting model for a given language, framework, and use case. By applying automatic pre- and post-processing techniques, Symflower improves the usefulness and quality of LLM-generated code. The platform also optimizes Retrieval-Augmented Generation (RAG) to provide LLMs with the necessary context, suppressing hallucinations and improving accuracy.
Target Audience
Symflower targets software development teams and organizations seeking to improve the performance and reliability of LLMs in their development workflows.
Features
- LLM benchmarking across various programming languages, frameworks, and use cases, comparing approximately 80 models across 10 categories with 50 functional and non-functional metrics.
- Automatic pre- and post-processing of LLM-generated code, including code repair and linting fixes, resulting in an average 26% improvement in functional score.
- Optimal RAG implementation to provide LLMs with relevant context, reducing hallucinations and improving accuracy.
- Continuous benchmarking on real-world use cases to ensure compatibility with the latest models and identify potential issues.
- Data curation and accelerated fine-tuning for pre-release models, including automated fixes for identified problems.
- Optimized function calling for faster and more accurate results, reducing test execution times by an average of 29%.