Debug your training data. Optimize your model
Unlock the full potential of your AI model by removing mislabeled items, outliers and more. Unlearn items quickly and efficiently without retraining.
Data labeling issues hurt accuracy and waste time
Data science teams waste days sifting through data and building makeshift in-house solutions.
Finding faulty data is a time waster
Labeling errors are annoying. But manually looking for issues makes no sense.
Debugging AI models is frustrating
Struggling with overfitting, biases or bad accuracy? Finding the root cause is a challenge.
Retraining takes too long and costs too much
You’re sitting around waiting for your model to retrain. You could have launched the project by now.
Your complete AI debugging toolkit
4 simple steps
Install and link your model and data
Whether you prefer pip or Docker install - 1 line of code and 2 links. You’re ready to go.
Get insights on your data
- Mislabeled items
- Outliers
- Ambiguous items
- Under-sampled areas
- And more...
Debug your model
Input the faulty prediction → Get instant root cause analysis. Find underlying data issues and actionable insights on how to solve them.
Remove faulty data instantly. No retraining.
Unlearning takes <5% the time and cost of retraining. Model accuracy is retained and improved.
Unlock your model’s full accuracy potential
Reach your KPIs and complete projects faster while getting rid of the annoying part of the job
Stop manually sifting through labeled data
Finding faulty data is a universal problem. It makes no sense to improvise a solution every time.
Know what’s really causing the issues
Get certainty and save time debugging complex models and huge datasets.
Seamless integration with your AI stack
No need to change workflows
Leading AI experts trust Hirundo
As AI regulation evolves, cost effective Machine Unlearning technology will become a must.
Avi Tel-Or
CTO, Intel Ignite
I've tried many data quality solutions. Hirundo finds data issues and mislabels at a level I’ve never seen before.
Dan Erez
AI Tech Lead, Taranis