Zutra
Zutra provides an AI-powered memory layer for R&D teams, automatically structuring and linking scattered experimental files, reports, and raw data into a searchable scientific knowledge base. Researchers can ask conversational questions about past work and receive evidence-based answers grounded in their own data, without writing any code. The platform is built for labs in materials, chemicals, semiconductors, and advanced manufacturing.
- Artificial Intelligence
- Data & Analytics
- AI Agents
- New Materials
- Software Only
Funding
Founders
Founder details are not available yet.
Product
Problem
R&D teams generate large volumes of experimental data, reports, and raw files that become scattered across systems and teams. This fragmentation makes it difficult to retrieve prior findings, leading to duplicated efforts, lost institutional knowledge, and slower decision-making.
Solution
Zutra serves as the memory layer for R&D, automatically capturing, structuring, and linking scientific information as research happens. The platform builds a growing knowledge base from existing files without requiring any coding, so each new experiment adds to the organization's collective understanding. Researchers can then ask conversational questions about what has been tried, what happened, and why, receiving answers grounded specifically in their own scientific work. This approach enables cross-project knowledge retrieval, supports patent writing, and helps teams turn accumulated data into products and decisions.
Target Audience
The primary users are R&D teams in materials, chemicals, semiconductors, and advanced manufacturing, along with universities and research institutes that need to preserve and query scientific knowledge across projects.
Features
- Automatic structuring and linking of experiment files, reports, and raw data into a unified scientific knowledge base
- Natural-language query interface that returns evidence-based answers directly from the organization's own research records
- Zero-code setup, enabling non-technical research staff to adopt the platform without engineering support
- Designed for domain-specific workflows in materials, chemicals, semiconductors, and advanced manufacturing
- Supports academic and research institute environments alongside industrial R&D labs