Sylvy offers a private AI assistant for wet‑lab teams that ingests a lab’s own experimental data and metadata, allowing researchers to ask natural‑language questions about past experiments and receive sourced, citation‑ready answers. The platform automatically compares results across conditions, generates new protocols based on historical successes, and surfaces patterns and insights, all while running securely on‑premise so that proprietary data never leaves the lab.
Funding
Funding not disclosed
Founders
Product
Problem
Wet‑lab researchers often spend excessive time searching through notebooks, spreadsheets, and protocols to retrieve details of past experiments, compare results, or design new procedures, which slows progress and increases the risk of errors.
Solution
Sylvy provides a private AI assistant that ingests a lab’s own experimental data and metadata, enabling natural‑language queries about previous work. Users can ask for specific results, compare outcomes across conditions, and receive automatically generated protocol drafts based on what has succeeded in the lab before. The AI runs within the lab’s secure environment, ensuring that proprietary data never leaves the institution. As more experiments are logged, the system continuously refines its knowledge base, surfacing patterns and insights that help scientists plan more efficiently.
Target Audience
Primary customers are academic and industry wet‑lab teams—such as molecular biology, biochemistry, and cell‑culture groups—that manage large volumes of experimental data and need faster access to actionable knowledge.
Features
- Natural‑language interface for querying past experiments with sourced, citation‑ready answers
- Automated cross‑experiment comparison of conditions, parameters, and outcomes
- Protocol generation that leverages the lab’s historical success data
- Secure, on‑premise deployment guaranteeing that all data remain within the lab’s infrastructure
- Insight engine that identifies trends and hidden relationships across the entire experimental record
- Continuous learning model that improves accuracy as new data are added