Hirundo offers a Machine Unlearning Platform that enables users to identify and remove unwanted data from AI models without the need for retraining. This technology addresses data labeling issues that compromise model accuracy and efficiency, allowing data science teams to optimize their datasets and maintain compliance with regulations.
Funding
$1.7M 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
AI model accuracy is often compromised by data labeling errors, outliers, and biases present in the training dataset. Data science teams spend significant time and resources manually sifting through data to identify and correct these issues, leading to inefficiencies and delayed project timelines. Traditional retraining methods to address these problems are computationally expensive and time-consuming.
Solution
Hirundo offers a machine unlearning platform that enables users to identify and remove unwanted data points from AI models without requiring full retraining. The platform allows users to input faulty predictions to pinpoint problematic data items and provides insights into data labeling issues, such as mislabels and under-sampled areas. By selectively unlearning faulty data, Hirundo helps data science teams optimize their datasets, improve model accuracy, and maintain compliance with data regulations, all while significantly reducing the time and cost associated with traditional retraining methods.
Target Audience
Hirundo's primary customers are data scientists, AI engineers, and machine learning teams across various industries who are responsible for building, maintaining, and optimizing AI models.
Features
- Identification of problematic data points by inputting faulty predictions.
- Scanning capabilities to detect mislabels, outliers, and under-sampled areas in datasets.
- Machine unlearning technology to remove faulty data without full model retraining.
- Dataset optimization to increase model accuracy and improve results.
- Compliance tools to address Data Subject Access Requests (DSARs) and maintain regulatory compliance.
- Seamless integration with existing AI stacks and workflows.