Your Guide to Quality Data Science Products

The conversation around AI readiness typically centers on data quality, talent acquisition, and executive buy-in. While these factors matter, they overlook a more fundamental question: does the organization have the technical and cultural infrastructure to operationalize AI at scale? 

Imagine your team needs evidence-based answers to clinical questions like "Which medications can be combined with ibuprofen to help treat back pain?" The traditional approach means weeks of work. We built a working conversational AI assistant that queries this data in hours.

AI-powered rapid prototyping turns vague dashboard ideas into interactive prototypes in hours, which cuts requirements iteration by up to 90% and prevents costly misalignment.

What if you could go from concept to working clinical dashboard prototype in 48 hours instead of 4-6 weeks?

Explore how researchers analyzed vast cellular data with a visualization tool that reveals patterns hidden in 700,000 cells.

Generate and automate Quarto reports with LLMs. Build once refine with your team and deploy to Posit Connect for fresh data.

Learn how to build flexible LLM apps in Shiny for Python, handle API changes, and keep your project future-proof.

A practical guide to embedding machine learning models in Shiny apps with tips on deployment, performance, and usability.

Struggling to understand Principal Component Analysis (PCA)? This guide will demystify the concepts and demonstrate practical implementation in R programming language.

Do you feel machine learning in R leaves a lot to be desired? You’re not alone. Try R tidymodels - it’ll feel right at home if you’ve used tidyverse before.

Discover how AI/ML are transforming drug design, drug development, and clinical trials, enhancing efficiency, safety, and patient outcomes in the pharmaceutical industry.

Discover how machine learning enhances RNA-ligand binding predictions and outperforms traditional methods through our collaboration with IIMCB.