Engivent provides an autonomous biology lab platform that turns experimental workflows into software-defined, high‑throughput processes. By integrating standard lab robotics with modular microfluidic systems, it can run hundreds of parallel conditions and close the loop between execution, readout, and iteration, enabling rapid protein engineering, therapeutic discovery, and synthetic biology. This infrastructure compresses design‑build‑test cycles from months to days, delivering fast, data‑rich feedback for AI‑generated molecular candidates.
Funding
Funding not disclosed
Founders
Product
Problem
AI-driven biology generates candidate molecules faster than traditional labs can experimentally validate them, creating a bottleneck in the speed and quality of real-world feedback needed for protein engineering, therapeutic discovery, and synthetic biology.
Solution
Engivent offers an autonomous biology lab platform that merges conventional lab robotics with modular microfluidic systems to execute experiments as software-defined, closed-loop workflows. The system runs hundreds of parallel conditions, automatically captures readouts, and feeds results back into the next experimental iteration, compressing design‑build‑test cycles from months to days. By standardizing protocols and data capture, Engivent produces traceable, model‑ready datasets that can be directly used for AI model training and validation. The platform is scalable across applications such as therapeutic protein screening, enzyme optimization, and AI‑guided protein design, turning experimental biology into a programmable, high‑throughput data engine.
Target Audience
Primary customers are biotech companies, pharmaceutical R&D labs, and academic research groups focused on high‑throughput protein engineering, therapeutic discovery, and synthetic biology projects that require rapid experimental validation.
Features
- Integrated robotic arm and modular microfluidic cartridges enabling fully automated liquid handling and reaction setup
- Software-defined workflow engine that orchestrates experiment execution, data acquisition, and iterative redesign without manual intervention
- Capability to run hundreds of parallel conditions, providing exhaustive coverage of biological parameter spaces
- Real-time data capture and standardized output formats optimized for machine‑learning model ingestion
- Closed-loop feedback loop that automatically adjusts experimental parameters based on previous results
- Compatibility with common laboratory equipment and APIs for seamless integration into existing lab infrastructure