AI & Predictive Analytics

Invene created a machine learning algorithm that identified motifs of interest for SAR target identification.

Invene invented an algorithm for analysis on polypeptide chains to determine small molecule binding affinity.

Invene leveraged advanced LLM optimization techniques, including Model Ensembling with Self-MoA, fine-tuning of classification models, and multiple prompt engineering techniques, to enhance decision support and automate workflow processes in a clinical setting.

Invene implemented a Natural Language Processing powered system that transcribes provider-patient conversations, generates structured AI summaries, and seamlessly integrates them into the EMR for billing and clinical workflow efficiency.

Invene improved existing pricing algorithms with machine learning and built many ETL jobs to automate medical procedure estimation.

Invene established a model to predict pharmacy inventory volume based on segmented population data.

Invene created a HIPAA compliant application that uses computer vision to distinguish between orders and other faxes to create transformed data values that represent each order.

For an FDA-approved, low-powered device, Invene built firmware for occluded facial detection using computer vision.