Recurrence Technology provides an AI‑driven cloud platform that automates ingestion, normalization, and predictive modeling of sequencing, proteomics, and high‑throughput screening data for biotech R&D. The system generates ranked, explainable hypotheses and integrates via RESTful APIs into LIMS and bioinformatics pipelines, enabling faster target identification and experiment prioritization while maintaining GDPR/HIPAA security.
Funding
Funding not disclosed
Founders
Product
Problem
Biotechnology research relies on large, heterogeneous datasets and complex experimental workflows, which often require extensive manual analysis and iterative testing. This slows discovery cycles, inflates costs, and hampers the ability to translate insights into viable therapeutics or diagnostics in a timely manner.
Solution
Recurrence Technology delivers an AI-driven software platform that automates data preprocessing, predictive modeling, and workflow optimization across biotech R&D pipelines. The system ingests experimental, omics, and assay data, applies machine‑learning algorithms to generate actionable hypotheses, and ranks candidates based on projected efficacy and manufacturability. Results are presented through interactive dashboards that highlight key drivers and confidence intervals, enabling scientists to prioritize experiments with higher success probability. The platform is offered as a cloud‑hosted service with secure API endpoints, allowing seamless integration into existing laboratory information management systems (LIMS) and computational notebooks. By reducing manual data handling and accelerating hypothesis generation, the solution shortens development timelines and lowers overall research expenditures.
Target Audience
The primary customers are biotechnology firms, pharmaceutical R&D departments, and contract research organizations that need to accelerate discovery and improve data‑driven decision making in their pipelines.
Features
- Automated data ingestion and normalization for sequencing, proteomics, and high‑throughput screening datasets
- Proprietary machine‑learning models for target identification, protein engineering, and phenotype prediction
- Explainable AI visualizations that expose feature importance and uncertainty metrics for each prediction
- Scalable cloud compute environment with GPU acceleration and on‑demand resource provisioning
- RESTful API and SDKs for integration with LIMS, ELN, and custom bioinformatics pipelines
- Role‑based access control and end‑to‑end encryption to meet GDPR and HIPAA data‑security requirements
- Continuous model monitoring and automated retraining pipelines to incorporate new experimental results