ProviGenAI is an AI-driven platform that accelerates biological and chemical process optimization through closed-loop experimental campaigns. The system integrates active learning with standard laboratory automation hardware, automatically designing experiments, learning from delayed and heterogeneous data, and proposing next steps to reduce manual intervention. It helps teams optimize assay development and biomanufacturing processes in up to 5x fewer iterations than traditional Design of Experiments (DoE) approaches.
Funding
Funding not disclosed
Founders
Product
Problem
Life science research and biomanufacturing teams face slow, labor-intensive experimental optimization cycles that rely on manual feedback loops and fragmented data. Relevant signals arrive at different times—immediate QC data, delayed assay results, and weeks-later downstream characterizations—making it difficult to make informed decisions efficiently. Most teams lack the tools to systematically balance exploration, uncertainty, and exploitation across complex, high-dimensional experimental spaces.
Solution
ProviGenAI provides a closed-loop optimization platform that uses active learning to design, execute, and refine experiments autonomously. The system integrates with existing lab automation infrastructure like liquid handlers, plate readers, bioreactors, and imaging systems, adapting to each team's specific workflows. It updates its understanding of what works and what remains uncertain after every experiment, selects the next most informative experiments, and drives them directly on available hardware—without waiting for all delayed data to arrive. By incorporating both immediate QC results and slower downstream readouts, the platform keeps campaigns moving while continuously refining predictions. The result is faster protocol optimization, improved assay robustness, and fewer failed biomanufacturing batches, all with significantly less manual intervention.
Target Audience
Primary users are life science R&D teams, assay development scientists, and biomanufacturing process engineers working in biotech, pharma, and industrial research settings who need faster, more robust experimental optimization.
Features
- Active learning engine that balances exploration, uncertainty, and expected improvement to select optimal next experiments across high-dimensional design spaces
- Closed-loop campaign management that executes experimental batches automatically on standard automation hardware including liquid handlers, plate readers, bioreactors, and imaging systems
- Multi-signal data integration handling immediate QC data, assay readouts, continuous sensor streams, mass spec analytics, and delayed downstream characterization in a unified model
- Plate-level and condition-level evaluation metrics that capture spatial effects, edge effects, CV, Z′, and other robustness indicators
- Works with partially-specified objectives, including natural language targets or example outcomes, rather than requiring hand-written scoring functions
- Iterative refinement process that stays adaptive—broader exploration in early rounds, more selective targeting in later rounds as uncertainty shrinks