REIMAGINE DISCOVERY. REDEFINE CARE.

Clinicians spend hours extracting data from unstructured clinical notes (EHRs) to complete lengthy insurance forms, a major bottleneck that contributes to physician burnout and delays patient access to specialty medications. We built a "Clinical Co-pilot," a secure web application that uses a Multi-Agent System in which specialized AI agents collaborate to interpret patient notes, analyze the insurance form, and intelligently populate it for the clinician's final review.

As part of our involvement in the European deCYPher project, we developed an AI-powered platform to steer the design of laboratory experiments aiming to optimize a synthetic biological construct such as  a protein chimera, a modified genome, or an industrial bioprocess. The core engine trains a surrogate model of the experimental process and infers the next best experiments to run to achieve a given optimization goal with minimal total experimental cost and maximal speed. This core engine can be applied to any optimization problem, and we equip it with specialized tools to handle a specific use case optimally. For instance, it was extensively customized with protein-sequence generation and feature-extraction tools to engineer domains for the functional expression of membrane-bound enzymes.

We semi-automate lesion classification using AI (e.g., Benign vs. Malignant). This can increase the number of cases per histopathologist by an estimated 5x, improving revenue and scalability.

Our advanced deep learning models create tumor probability heatmaps, detecting tumors with 90% accuracy in whole slide images.

Replace manual, error-prone inspections with Vision AI. We deploy automated solutions for defect detection—such as identifying microscopic fibres and structural defects—allowing you to reject faulty items in real time and reduce waste.

Break down complexity with self-healing data pipelines that ensure 100% accuracy, creating a unified intelligence layer for your operations.

Generate reliable insights from real-world data, including electronic health records, registries, imaging, and claims. We build governed AI pipelines that clean, harmonize, and analyze complex healthcare data to support post-market surveillance, quality monitoring, and evidence generation—ensuring transparency, traceability, and regulatory readiness.