We're committed to shedding light on AI's decision-making process, fostering trust and comprehension through explainability.

Amidst the surge of cutting-edge AI models and the intricacies of automated decision-making processes, the pursuit extends beyond mere accuracy. Understanding the rationale behind a model's decisions and pinpointing areas of potential error are paramount. It's the clarity and depth provided by prioritizing the explainability of your models that genuinely empowers us to make more informed automated decisions with unwavering confidence.

At Linear Logic we're building the go-to Data Engine for Explainable AI, completely open source! Get started benchmarking your models and achieve a better understanding of their performance.

- Team Linear Logic

Meet the team

Miguel

Miguel Pieters

CEO & Co-founder

Luc

Luc Wentholt

COO & Co-founder

Torrey

Torrey Leonard

Raoul

Raoul Wentholt

Akanksha

Akanksha Menon

Our story

Linear Logic was originally developed in 2018 by Miguel Pieters to more efficiently complete university assignments. Existing data annotation services heavily relied upon manual labour which made the process of obtaining an annotated dataset time intensive. With the aim to accelerate A.I. development, the co-founders set out to build a platform capable of meeting today's needs for fast-growing global businesses.
The founding team called the company Linear Logic. Linear logic is a refinement of classical and intuitionistic logic. Instead of emphasizing truth, as in classical logic, or proof, as in intuitionistic logic, linear logic emphasizes the role of formulas as resources. We focus on building state-of-the-art infrastructures to manage the entire Machine Learning lifecycle.