AI-Powered
Smart Laboratory
CNWAYLab deeply integrates AI and machine learning across the entire LIMS workflow: from intelligent review and anomaly detection to predictive analytics and natural language report understanding. Lab data evolves from "being recorded" to "generating insights."
Six AI Capability Pillars
From data capture to decision support, AI spans the full lab value chain. Every capability has been validated and iterated in real production environments.
Intelligent Review Engine
Rule engine + anomaly detection model that automatically reviews result plausibility: identifies Out-of-Trend (OOT), Out-of-Specification (OOS), logical contradictions, and entry errors.
Anomaly Trend Detection
Unsupervised learning monitors multi-dimensional quality data, issuing alerts before deviations accumulate into OOS events. Supports cross-batch, cross-instrument, and cross-analyst comparisons.
Predictive Analytics
Time-series prediction models built on historical trends and external factors: stability study trend forecasting, reagent consumption estimation, equipment failure early warning.
NLP Report Understanding
Large Language Models (LLM) parse instrument raw report text, automatically extracting key parameters, conclusions, and anomaly descriptions into structured summaries.
Image Recognition
Computer vision analyzes chromatograms, spectra, electrophoresis images, and other instrument readouts. Automatically identifies peak detection issues, baseline drift, impurity shoulders, and other typical problems.
Intelligent Scheduling
Operations research algorithms dynamically orchestrate sample testing sequences, minimizing instrument idle time and solvent switchover costs, boosting overall lab throughput efficiency.
Intelligent Review: Beyond Rule-Based Checking
Traditional LIMS relies on static rule-based limit checks — trigger an alert when a value exceeds specs. CNWAYLab's AI review integrates a three-tier architecture: rule engine, Statistical Process Control (SPC), and machine learning anomaly detection.
Three-Tier Intelligent Review Architecture
Every test result automatically passes through three progressive review tiers before report generation, filtering risks layer by layer.
- L1 Rule Layer: Specification limits, logic validation, mandatory field checks — millisecond-level judgment, blocking >90% of obvious errors
- L2 SPC Layer: Control chart trend rules (Western Electric Rules), moving range monitoring — detecting slow drifts and cyclical fluctuations
- L3 ML Layer: XGBoost model trained on historical anomaly records, identifying hidden anomaly patterns involving multi-dimensional feature interactions
Explainable AI Decisions
In compliance scenarios, AI cannot be a "black box." Every AI-flagged anomaly comes with an explainable rationale and confidence score.
- SHAP feature contribution analysis: clearly shows which factors triggered the anomaly classification
- Similar historical case retrieval: automatically retrieves the disposition outcomes of similar anomaly patterns within the last 90 days
- Reviewer can "Accept / Reject / Escalate" in one click — every interaction is a continuous learning signal for the model
LLM-Powered LIMS
Deploying Large Language Models (LLMs) within the client's private data plane, unlocking NLP capabilities while ensuring data security.
Automated Report Summarization
LLM parses instrument raw report PDFs/XML, auto-generating natural language summaries of test conclusions. Supports bilingual output (Chinese/English), reducing manual writing time by 40%.
Natural Language Query
Users can query LIMS in natural language: "Which batches of HPLC content testing exceeded 99.5% last month?" — AI automatically converts this into a database query and returns results.
SOP Intelligent Q&A
RAG (Retrieval-Augmented Generation): enterprise SOP documents are vectorized. Analysts can ask in natural language, "What is the dilution factor for this sample?" — AI extracts the precise answer from the SOP knowledge base.
Experience AI-Powered Smart LIMS Today
Book an exclusive demo to see how CNWAYLab's AI capabilities can deeply integrate with your lab business processes,
starting from a pilot scenario and progressively achieving full-workflow intelligence.