Drug design, lab operations, and scientific workflow automation. Built for the specific constraints of regulated scientific environments.
Drug discovery is a multi-year process where most candidate molecules fail. Scientists need to predict which molecules will succeed before committing to expensive wet lab experiments.
ML models spanning the full pipeline: PK-PD prediction, toxicity screening, synthesizability assessment, and ADME property prediction. Scientists evaluate candidates holistically rather than checking each property separately.
Warehouse staff manually read vendor labels on reagent bottles to capture chemical data and determine storage conditions. Different forms of the same compound have different identifiers.
Vision AI for bilingual label capture, structured data extraction, storage recommendations, and a knowledge graph mapping compound names to all variant forms. Integrates with existing inventory systems.
A contract research organization responds to hundreds of client questionnaires per year, each consuming 50-100+ person-hours. Questions are repetitive but worded differently by each client.
Semantic matching against historical Q&A pairs to produce draft responses with confidence scores and source citations. Client-aware redaction prevents information leakage across clients.
Over 1,000 chemists spend significant time on reports that must conform to patent-standard formats. Each chemist uses a different style, and review is manual.
Locally deployed language model that reformats chemistry reports to the required standard template, preserving all scientific content while standardizing formatting and language.