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IvyCheck

Guard Against AI Risks with Real-time Checks

IvyCheck offers an API designed for classifying both user- and AI-generated texts, along with extracting sensitive data. Our specialized machine learning models ensure the safety and quality of your AI app in real time.

IvyCheck
Founded:2022
Team Size:2
Location:Berlin, Germany
Group Partner:Michael Seibel

Active Founders

Tammo Rukat

Tammo is co-founder of Ivycheck. As Applied Science Manager at Amazon, he led a team of Scientists and Engineers, developing algorithms to improve the product search experience. Prior to that, Tammo worked as Applied Scientists focusing on ML for data quality. There, he experienced first-hand the importance of data observability for ML based products. Tammo has a research background in Medical Physics and holds a PhD in Statistical Machine Learning from the University of Oxford.

Tammo Rukat
Tammo Rukat
IvyCheck

Dustin Lange

Dustin is co-founder of IvyCheck. Dustin has 8.5 years of industry experience at Amazon as Machine Learning Scientist and Applied Science Manager. He founded a team that built tools for data quality measurement and cleaning, including Deequ (2.4k+ stars on GitHub). Dustin later owned data products powering the Amazon product search page, used by millions of users every day. Dustin holds a PhD in database systems from Hasso Plattner Institute, Potsdam, Germany.

Dustin Lange
Dustin Lange
IvyCheck

Company Launches

TL;DR: IvyCheck offers an API designed for classifying user- and AI-generated texts, along with extracting sensitive data. Our specialized machine-learning models ensure the safety and quality of your AI app in real-time.

What are AI risks?

AI-powered interfaces such as chatbots emerge and pose new AI risks:

  • Prompt Injection Attacks: Attackers try to manipulate language models to execute unauthorized actions or access sensitive data. These attacks exploit the way AI interprets inputs as natural language text. Prompt injection attacks create a risk that spans from data exfiltration to influencing decision-making processes.
  • PII Data Leakage: Customers may include sensitive data in requests that are then sent to LLM providers. Companies must ensure that sensitive data is detected and redacted from requests.
  • Hallucinations: Chatbots in RAG applications use context retrieved from document stores, posing the risk of hallucinated, inaccurate, off-topic, or made-up information to users. IvyCheck evaluates whether chatbot answers are faithful to the provided context.

How does IvyCheck help?

IvyCheck provides a library of checks for managing AI risks in your application. IvyCheck is:

  • Flexible: You can add checks to your LLM app with just 2 lines of code and protect your users and your data from prompt injection attacks, PII data leakage, and hallucinations. Works regardless of which LLM provider you use in your app.
  • Secure: We use customized, fine-tuned models for the checks. We self-host all our models and your data is not sent to third-party model providers.
  • Fast: Since the models are small and specialized, you can expect a latency under 100-200 ms. If you require even faster endpoints, please get in touch!

The founding team

We’re Tammo (left) and Dustin (right). We met at Amazon, where we were part of the Core Machine Learning team in Berlin. We worked on systems for large-scale data quality checks, missing value imputation, and search filter optimization. We’re both obsessed with delighting our customers and solving their toughest challenges! Let’s create a safer world with AI.

How do I get started?

Visit https://ivycheck.com or book a call directly with us. We’d love to get in touch and help you make your AI app safe today!

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