Magnus offers an AI‑driven, fully automated laboratory platform that designs, synthesizes, and validates programmable ligands for rapid detection and capture of PFAS, toxins, and other high‑value targets. By integrating decision, execution, and learning layers, the system runs parallel, reproducible experiments around the clock, turning discovery cycles that once took months into deployable assets within weeks. It serves high‑consequence sectors such as defense, infrastructure, food safety, and industrial biotech.
Funding
Funding not disclosed
Founders
Product
Problem
Biotech and industrial labs often rely on manual, bespoke experiments to discover molecular recognition elements, leading to slow, inconsistent, and costly development cycles for detecting and capturing targets such as PFAS and toxins. The lack of scalable, automated workflows hampers rapid translation of promising ligands into deployable detection assets, especially in high‑consequence sectors like defense and infrastructure.
Solution
Magnus provides an AI‑driven, fully automated laboratory platform that designs, synthesizes, and validates programmable ligands in a closed‑loop workflow. The system integrates a decision engine that prioritizes experiments, a robotic execution layer that runs parallel biology assays around the clock, and a learning layer that feeds sequencing and enrichment data back to improve predictive models. Each iteration refines ligand designs until performance criteria are met, at which point the platform ships validated, integration‑ready assets rather than raw research data. By standardizing and automating the entire discovery pipeline, Magnus reduces cycle time from months to weeks and eliminates human variability, enabling rapid deployment of detection and capture solutions for defense, infrastructure, food safety, and industrial biotech applications.
Target Audience
Primary customers are biotech companies, defense contractors, infrastructure monitoring firms, and industrial biotech organizations that need fast, reliable molecular recognition solutions for detecting hazardous chemicals and toxins.
Features
- AI decision engine that ranks ligand candidates using published science and computational simulations
- Fully robotic execution lanes with consistent controls for high‑throughput, reproducible biology experiments
- Real‑time learning loop that incorporates sequencing results, enrichment patterns, and failure modes to continuously improve model accuracy
- Automated synthesis and validation of programmable ligands, delivering evidence‑backed performance specifications
- Shipping layer that provides ready‑to‑integrate detection and capture assets with documented behavior and specifications