Branewave
Branewave provides a unified discovery environment for biomarker research, consolidating experimental workflows to eliminate fragmented tools and data silos. The platform features validated AI workflows, knowledge graphs automatically updated from research data, and AI agents that answer complex queries and generate novel insights. It enables end-to-end traceability from raw data to analysis, bridging the wet-lab/dry-lab divide for research teams.
- Biotechnology
- Artificial Intelligence
- AI Agents
- Data & Analytics
- Software Only
Funding
Founders
Founder details are not available yet.
Product
Problem
Existing methods for biomarker discovery are inefficient because data is fragmented across shared drives, storage buckets, and personal laptops with inadequate version control and provenance tracking. A wet-lab/dry-lab divide slows the feedback loop between experiments and analysis, and there is no unified knowledge base to compare results across current and past experiments or query against public datasets and literature.
Solution
Branewave provides one unified discovery environment where researchers can track, analyze, and reason over their data to accelerate biomarker discovery. The platform consolidates the full experimental workflow, eliminating the need to context-switch between separate tools or wait for data scientist handoffs. Validated AI workflows enable rigorous statistical analysis, model training, and discovery, while AI agents reason over knowledge graphs that are automatically updated from research data. These agents answer complex queries and generate novel insights, allowing researchers to maintain a continuous feedback loop between experiments and findings.
Target Audience
Primary customers are research organizations and laboratory teams—including bench scientists and data analysts—working on biomarker discovery who need to streamline experimental workflows and accelerate the path from data collection to scientific insight.
Features
- Unified platform replacing fragmented tools including shared drives, storage buckets, spreadsheets, and personal laptops with inadequate version control
- Direct data entry at the bench, reducing transcription errors and bridging the wet-lab/dry-lab divide
- Validated AI workflows for rigorous statistical analysis, model training, and biomarker discovery
- AI agents that reason over automatically updated knowledge graphs, answer complex queries, and generate novel insights
- Ability to query and reason across all internal data and compare against public datasets and literature
- End-to-end traceability from raw data to analysis to insight, addressing reproducibility concerns in research settings