Byterat

• Automated synchronization of raw battery test data from multiple lab cyclers
• Unified data pipeline for battery experiment metadata
• Scientific feature extraction for AI modeling
• Predictive modeling for cycle life and state of health
• Degradation forecasting under uneven usage
• Anomaly detection in battery tests
• Interactive dashboards and visualization tools
• Secure, scalable architecture for large data volumes
• Audit trails and version history of data
• Exportable insights and reports for downstream teams

Byterat is a specialized AI battery data platform built to revolutionize how battery research laboratories and manufacturers manage, analyze, and predict performance of energy storage systems. In the battery industry, experiments generate vast and complex datasets from cycling tests, diagnostics, impedance measurements, and environmental stress testing. Fragmented tools, siloed data, and manual workflows often slow down progress and introduce risk of error.

Byterat simplifies that by providing an integrated data pipeline: raw outputs from various lab cyclers and diagnostic instruments are automatically ingested and normalized, creating a unified “single source of truth.” On this foundation, Byterat applies scientifically informed feature extraction to derive metrics and signals relevant for modeling battery health. Through machine learning models trained on these features, Byterat can forecast cycle life, estimate state of health, detect anomalies or deviations, and even predict outcomes of future experiments under nonuniform usage.

Users access interactive dashboards and visualizations that show trends, comparisons, and projected performance over time. The platform supports detailed filters, drill‑downs, and exportable reports to share with stakeholders from R&D, product, or management teams. It maintains versioning and audit logs to ensure data integrity and traceability, critical in regulated or high-stakes environments.

Byterat is designed for scalability and security: it can handle high volumes of test data while preserving privacy and access control. Its architecture is suited for labs running many parallel experiments or multi‑site facilities. The predictive insights accelerate decision making: instead of waiting months for test cycles to complete, researchers can act earlier using model projections, optimizing device designs, test planning, or material selection.

Because it bridges raw measurement tools and advanced analytics, Byterat reduces manual overhead, minimizes errors, and shortens development cycles. It is especially valuable to battery development teams, testing facilities, cell manufacturers, and energy storage innovators seeking to make data‑driven choices with clarity and speed.