Real-time lab analytics, without touching validated systems.
A global life sciences research organization running high-throughput labs across China and APAC needed software that kept pace with its instruments.
Software fell behind the instruments.
Data capture, processing and validation lagged the speed of experiments, and the usual fixes, batch processing, generic LIMS and manual monitoring, did not fit.
- Latency. Slow data exchange between plate readers and software delayed assay processing.
- Rework. Inconsistent data handling increased validation effort.
- No scale. Systems could not extend across labs or regions without heavy manual work.
- Hard constraints. Edge-class hardware, no disruption to validated instrument control, minimal downtime and strict compliance.
An analytics layer beside validated systems.
Real-time ingestion
Low-latency data capture from microplate readers without disrupting instrument operations.
Predictive analytics
Pattern detection and quality signals during assay runs, so errors are caught before they spread.
Edge deployment
Optimized for constrained hardware, with offline-ready backup and recovery.
Remote operations
Remote reinitialization and monitoring cut on-site visits.
What changed, with AI kept advisory.
| Area | Before | After |
|---|---|---|
| Data capture | Batch, after the fact | Real-time visibility |
| Quality checks | Manual | Predictive quality signals |
| Recovery | On-site visits | Remote maintenance |
| AI role | None | Prediction, validation and anomaly detection only. No instrument control or validated-workflow execution; researchers make the final call |
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Bring AI to the lab, safely.
Tell us about your instruments and validated workflows. We'll show where real-time analytics fits.