83 Sciences
83 Sciences provides a research platform that captures and structures experimental data from voice notes and laboratory instruments, converting unstructured R&D information into a queryable agentic record. The platform analyzes failed experiments to identify root causes and propose optimized process conditions, supporting workflows from data capture through publications, patents, and scale-up. It serves R&D teams in materials science and chemistry, with vertical applications in critical minerals, energy storage, catalysis, solid forms, and semiconductors.
- New Materials
- Chemical Technology
- Artificial Intelligence
- AI Agents
- Data & Analytics
- Software Only
- Biotechnology
Funding
Raised to date
$500KRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Founder details are not available yet.
Product
Problem
Over 85% of experimental data generated in R&D is discarded, representing more than $100B in lost value annually. This data often remains in unstructured formats like voice notes, lab notebooks, and instrument outputs, preventing researchers from leveraging insights from failed experiments and hindering scientific discovery.
Solution
83 Sciences offers a streamlined research platform that captures and structures experimental data from diverse sources, including voice notes and instrument outputs. The platform functions as a scientific brain, building a structured, queryable agentic-record of every experiment that not only understands failures but also proposes optimized process conditions. By organizing this information, the platform enables researchers to move toward tangible outcomes such as publications, patents, scale-up, and industry-academia partnerships, effectively converting previously discarded data into valuable intellectual property.
Target Audience
Primary customers are R&D teams in materials science, chemistry, and life sciences working in critical mineral extraction, energy storage, catalysis, pharmaceutical solid forms, and semiconductor manufacturing sectors.
Features
- Effortless data capture from voice notes and laboratory instrument outputs
- Automatic structuring and organization of heterogeneous experimental data
- Agentic-record system that is queryable across all experiments
- Failure analysis capabilities that identify root causes and propose optimized process conditions
- End-to-end workflow support from data capture through to publications, patents, and scale-up
- Vertical-specific applications for critical minerals, energy storage, catalysis, solid forms, and semiconductors
- Materials science reinforcement learning environments built with real experimental data