- Specialization
Scientific Discoveryand 2 more specialization valuesLess
- Data Engineering
- Materials Science
- Business
Software Applicationsand 1 more business valuesLess
- Scientific Research
- Market
Scientific Researchand 1 more market valuesLess
- Manufacturing
- Ecosystem
- AI for science
- Customers
Enterprisesand 1 more customers valuesLess
- Researchers
More details
- Organization type
- Commercial Company
- Ecosystem
- AI for science
- Business
- Software Applications, Scientific Research
- Specialization
- Scientific Discovery, Data Engineering, Materials Science
- Market
- Scientific Research, Manufacturing
- Customers
- Enterprises, Researchers
- Delivery models
- Professional Services
- Revenue models
- Not classified
- Listing status
- Not classified
- Latest equity round
- Seed
- Reported funding amount
- Not classified
About
83 Sciences builds Dalton, an AI platform that mines discarded or unpublished experimental data to help research labs discover materials and improve experiments. It appears to sell through partnerships with laboratories, differentiating itself through access to otherwise discarded data and rapid conversion of partner data into co-authored scientific manuscripts.
Market
83 Sciences competes in AI-native scientific R&D and materials-discovery software, adjacent to the self-driving-laboratory market. Its data-first positioning combines capture of unpublished experiments and historical failed batches with a queryable scientific brain and Dalton AI, then connects discoveries to papers, patents, scale-up, and commercialization. Compared with platforms centered more heavily on robotics, LIMS/ELN integration, or closed-loop autonomous labs, its clearest differentiation is extracting value from existing experimental data through discovery contracts and partnerships.
83 Sciences targets industrial materials and chemistry R&D organizations seeking faster commercialization of new materials, especially teams with large stores of unpublished or discarded experimental data. It also serves academic labs and industry-academia partnership teams; likely users and buyers include materials scientists, lab leaders, and heads of R&D.
At a Glance
Problem
Materials and industrial research labs generate large volumes of experimental data, but 83 Sciences says more than 83% of it is discarded, representing over $100 billion in lost R&D value each year. The pain is both economic and scientific: researchers repeat work, miss insights in failed experiments, and struggle to turn scattered notes and instrument outputs into commercially useful knowledge. The core use case is recovering value from unpublished or unsuccessful experiments to identify new materials and optimized process conditions, then convert those findings into papers, patents, scale-up opportunities, and faster, cheaper commercialization.
Product / Service
83 Sciences combines AI-native research software with a partnership-led discovery service. Its system captures and structures information ranging from voice notes to instrument output, creating a searchable, agentic record of experiments that can interpret failures and propose improved process conditions. For industrial customers, the company offers discovery contracts; for academic labs, it offers a way to uncover additional papers and commercialization opportunities in existing data. The stated benefit is to move labs from raw experimental history to concrete outputs such as manuscripts, patents, optimized processes, and industry partnerships, with the company claiming it can turn a partner’s discarded data into a co-authored manuscript in less than two months.
Market
83 Sciences operates at the intersection of AI-native materials discovery, scientific research software, and laboratory data integration, serving materials scientists in industrial R&D and academia. Its approach is differentiated by mining unpublished experimental data rather than relying primarily on simulation or new experiments. Adjacent competitors include Schrödinger’s molecular-modeling platform for materials prediction, CuspAI’s AI materials-discovery system, and Benchling’s cloud platform for organizing scientific data and experiments; these overlap with parts of 83 Sciences’ workflow but are not shown in the available evidence to be direct like-for-like competitors.
The company appears to be at a very early commercial stage. Y Combinator lists it as founded in 2026, part of the Summer 2026 batch, active, and a three-person team, while its public profile shows launch activity, recruiting, and early visibility rather than disclosed customer or revenue metrics. The website says it is backed by Y Combinator and invites industry partners to scope discovery contracts, but no public customer names, revenue, or funding amount appear in the available evidence; accordingly, it is best characterized as pre-revenue or early commercialization rather than a company with demonstrated market traction.
Founders & Leadership
Funding History
Y Combinator
Recent News
On September 22, 2026, Y Combinator featured 83 Sciences in its Launch YC program, describing materials-discovery models trained on experimental data. The feature says the models aim to improve manufacturing performance and help academics publish more.
A Y Combinator job listing identified 83 Sciences as a YC S26 company building an intelligence engine for research and materials discovery. The role description says the company structures raw laboratory signals and experimental data to surface new discoveries.
Active Roles
2Business Model
83 Sciences appears to use a B2B partnership model, working with laboratories and research organizations to analyze unused experimental data through its AI platform. Its public website promotes partnership inquiries and does not disclose specific pricing or subscription terms.