GxP Compliant Platform to Train, Validate, and Deploy AI Models
We make it easy to upload and manage data from collected from production lines while maintaining data integrity, as required in 21 CFR parts 210, 211, and 212.
Use data from production to train state-of-the-art deep learning algorithms. We host a wide variety of models for classification, segmentation and anomaly detection tasks.
Deploying to production
With just a few clicks, deploy trained models either online or offline. Online models are exposed via RESTful interfaces while offline models are served by TensorFlow Serving.
We have developed intelligent tools for validation, which let you leverage the industry's best practices in evaluating and validating the performance of your models.
We have built in compliant practices all throughout the platform so that you don't have to worry about compliance—the platform does that for you automatically.
Automate critical tasks with AI and reduce manual labor
Algorithms in the field of deep learning have made tremendous advancements in recent years and, now, critical tasks such as controlling, inspecting and sorting products can be automated by models based on AI. This releases enormous potentials for increasing efficiency across production processes.
Criterion AI delivers a full-featured platform to reap the rewards of these technological advancements for drug manufacturers.
Enhance quality control and increase patient safety
For specific tasks (such as analyzing images, NIR/Raman spectra, time series and other types of structured data), AI models have now surpassed human performance levels.
That means that, by employing AI, drug manufacturers can enhance quality control by boosting detection rates of product defects, which ultimately leads to increased patient safety.
It also leads to reduced customer complaints, fewer potential recalls and less administration around these issues.
Strengthen the feedback loop across production processes
Because most quality control processes are carried out manually, a lot of information is lost across operations.
By making use of AI rather than manual labor to solve QC tasks, one can enable the flow of realtime feedback to prior processes up the production stream. This way, root causes for product defects can be identified in real time and resolved right away.
Strengthening the feedback loop can significantly lower scrap rates, increase OEE rates and improve overall product quality.