LLMs behave in undesired and unexpected ways
Hallucinations, biases, toxicity and jailbreak vulnerabilities are embedded in any LLM. Guardrails and finetuning are masking it - but that's not enough.
Pre-trained or finetuned, your LLM misbehaves
Any LLM includes inherent traits that are prone to cause risks when deployed
Guardrails act as flawed filters
Guardrails and monitoring solutions add significant inference costs and act as mere filters, while the model itself remains problematic
Finetuning masks issues, it doesn't remove them
Finetuning the model takes too much time and money. Instead of removing problems, it masks them
Unlearn any behavior from your LLMs
Hirundo’s solution unlearns customized or pre-defined behaviors from any LLM, ensuring they are removed from the model itself.
55% reduction in hallucinations
Ensure accuracy in any output.
*HaluEval Benchmark
85% reduction in successful attacks
Safeguard your model from jailbreaks.
*PurpleLlama Benchmark
70% reduction in biases
Responsible and fair outputs.
*Bias Benchmark Q&A
Seamless integration with your AI stack
No workflow changes needed. Our SOC-2 certified solution runs as an API or platform, with deployment available via SaaS, VPC, or air-gapped on-premises.
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