AI Solutions for Pharma & Life Sciences

Challenges We Address

Engineering Heavy R&D Workflow

Manual imaging and analysis require significant engineering effort, pulling researchers away from experimentation and slowing insight generation.

Hard to Scale Early Stage Drug Discovery

Traditional discovery workflows often rely on resource-intensive processes that make it challenging to scale promising ideas quickly and efficiently.

Inconsistent and Siloed Data

Fragmented systems and misaligned data standards make it difficult to leverage AI reliably across research, development, and operations.

Our Solutions

✓ AI for Drug Discovery (virtual screening, molecule design, LLMs on chemical and biological data)
✓ AI for Biomedical Image Analysis (toxicology, microscopy, phenotyping, pathology)
✓ MLOps for Life Sciences (reproducible pipelines, scalable infrastructure, model deployment & monitoring)
✓ Data Integrity in GxP (automated cross-validation, audit-ready data, consistent records across SOPs and systems)

We combine domain expertise with strong engineering to deliver production-ready pipelines, not prototypes. Everything we build is reproducible, scalable and designed for real-world scientific workflows.

AI for Drug Discovery

AI is transforming how we design the medicines of tomorrow. By leveraging advanced generative models and Large Language Models (LLMs), we help pharmaceutical teams fast-track drug discovery—from virtual screening to biomolecule design.
Our solutions reduce experimental costs, boost predictive accuracy, and enable data-driven innovation across the entire drug development pipeline. We streamline the discovery process and support smarter, faster decision-making in pharma R&D.

MLOps for Life Sciences R&D

MLOps streamlines the path from data to discovery in Life Sciences R&D. It enables research teams to focus on science, not infrastructure, by automating model versioning, deployment, and monitoring. With integrated tools for reproducibility and scalability, MLOps ensures reliable AI pipelines. Fast to implement, secure by design, and built for collaboration, MLOps reduces time to insight and bridges the gap between research and production.

Featured Case Study

Modernizing Bayer’s Data Landscape with Databricks

Bayer unlocked faster, smarter decision-making by unifying its data on a powerful Databricks Lakehouse. This scalable foundation fuels high-quality data products that drive innovation across the business.

Client: Bayer

Cell Painting: Parallel Image Analysis for Accelerated Toxicological Testing

Datasentics accelerated toxicological testing by running parallel image analysis on high-resolution cell painting data. This enables faster, more accurate detection of toxic effects and speeds up drug discovery.

Client: Leading Pharma Company

Automating Pathology Image Analysis with Deep Learning

Researchers can easily integrate their own deep learning models or train new ones to allow AI to take charge of tedious tasks like segmentation, detection and classification of pathology images.

Client: Leading Pharma Company

Journal Article

In Representations of Lipid Nanoparticles Using Large Language Models for Transfection Efficiency Prediction

Published in Bioinformatics (July 2024, Volume 40, Issue 7), authors from Sanofi and DataSentics present a new approach to predicting the transfection efficiency of lipid nanoparticles (LNPs) using large language models (LLMs). The results show that using LLMs to represent lipids improves the accuracy of transfection efficiency prediction compared to traditional methods.