This company develops AI agents utilizing multiple LLMs and Reinforcement Learning to accelerate software and hardware verification processes. Their tools automatically generate accurate tests, stimuli, and code fixes, significantly reducing manual effort and debug cycles. The platform aims to achieve verification speedups by automating complex tasks across design verification workflows.
Funding
$500K 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
Software and hardware verification processes are often slow and require specialized expertise, leading to bottlenecks in development cycles and increased time-to-market. Traditional methods struggle to keep pace with the complexity of modern systems, resulting in incomplete testing and potential for costly bugs.
Solution
VerifAI accelerates software and hardware verification by leveraging multiple large language models (LLMs) and reinforcement learning (RL) to automate test generation, stimulus creation, and bug fixing. The platform employs AI agents trained on code and historical data to generate accurate tests, identify hard-to-reach code conditions, and automatically cluster similar bugs from unstructured logs. By harnessing the collective intelligence of multiple LLMs, VerifAI eliminates hallucinations and routes each prompt to the optimal LLM for accurate answers, significantly reducing regression times and enhancing overall testing efficiency. The platform's AI-driven approach enables developers to generate usable tests and fix bugs without requiring expert-level knowledge, leading to substantial productivity gains and improved product quality.
Target Audience
VerifAI targets software and hardware development teams, verification engineers, and quality assurance professionals seeking to accelerate testing cycles, improve code coverage, and reduce debugging time.
Features
- Automated test generation using multiple LLMs fine-tuned on proprietary and non-proprietary code
- Reinforcement learning algorithms to optimize input stimulus settings and increase coverage
- Automatic bug clustering from logs using unsupervised machine learning and multiple language models
- Integration with existing bug databases and repositories (e.g., JIRA, GitHub)
- MultiLLM routing for optimal LLM selection and hallucination elimination
- Collaborative features including real-time screen sharing, annotation, and note editor
- WebApp interface for daily results viewing and user feedback integration