we are iflai
AI for microscopy
Use the latest in artificial intelligence research to deliver state-of-the-art
microscopy image analysis
From enabling R&D teams to expedite their data analysis to empowering researchers to uncover novel discoveries, iflai has the necessary knowledge and expertise to boost the limits in terms of speed, accuracy, reproducibility, and scalability of your project.
What defines iflai?
Our AI
Experience unparalleled AI tools, adept at particle localization, tracking, and characterization, as well as cell counting, classification, and virtual staining.
Our team
We have a team of highly qualified researchers in the fields of biophysics, artificial intelligence, and software development, entirely dedicated to your project to ensure its success.
Reasonably priced
Save money on hiring expensive machine-learning experts or data scientists. Our team of seasoned experts has years of experience and can efficiently and dependably process your data.
Featured solution
Let your diagnostic procedures meet AI
Discover the revolutionary technology of virtual staining through AI that offers a quick, non-invasive, and cost-effective solution to manual chemical staining.
Our AI-powered virtual staining is highly reproducible, ensuring accurate and reliable results every time.
News
iflai has been awarded the prestigious ERC Proof of Concept Grant
This grant will enable iflai to enhance and commercialize our virtual staining technology, aimed at boosting the precision and effectiveness of tissue analysis in pathology.
The team behind iflai has made a significant breakthrough in the study of microscopic motion using by Graph Neural Networks.
"Single-shot self-supervised object detection in microscopy", published in Nature Communications.
iflai researchers introduced an advanced self-supervised microscopic object detection method at the forefront of technological innovation.
Microplankton life histories revealed by holographic microscopy and deep learning
iflai contributed to the development of a new deep learning architecture (RU-Net) with the potential to reveal Microplankton life histories.