Slash turnaround time – Boost ROI and maximize existing resources
Challenge
Operational Bottleneck
Each sample required 60–90 minutes of review time. This meant a single analyst could only process about 6 samples in an 8-hour shift, significantly limiting the lab’s testing capacity. Clients were often waiting over 72 hours for test results due to backlog and analyst fatigue.
Matrix Interference Issues
Food and cannabis products often come with complex matrices (e.g., sugars, fats, flavourings) that interfere with accurate compound detection. Analysts frequently had to correct for enhancements, suppression, or co-eluted peaks manually.
Inconsistent Review Quality
Different analysts applied slightly different criteria when correcting chromatograms. This led to inconsistencies in reporting and flagged several unnecessary outliers for second-level review.
Operational Bottleneck
Each sample required 60–90 minutes of review time. This meant a single analyst could only process about 6 samples in an 8-hour shift, significantly limiting the lab’s testing capacity. Clients were often waiting over 72 hours for test results due to backlog and analyst fatigue.
Matrix Interference Issues
Food and cannabis products often come with complex matrices (e.g., sugars, fats, flavourings) that interfere with accurate compound detection. Analysts frequently had to correct for enhancements, suppression, or co-eluted peaks manually.
Inconsistent Review Quality
Different analysts applied slightly different criteria when correcting chromatograms. This led to inconsistencies in reporting and flagged several unnecessary outliers for second-level review.
Solution
We trained an advanced AI engine on more than 5,000 analyst-reviewed chromatograms and embedded it directly into the client’s first-level review workflow to automate key steps in chromatographic analysis. The system intelligently smooths noisy baselines by learning real-world noise patterns, adjusts retention times to correct for minor instrument-driven RT shifts, and detects common matrix interference such as suppression or enhancement effects in gummies, beverages, and similar sample types to automatically correct them. It also enhances peak integration accuracy by identifying overlapping peaks and precisely separating them to reflect true compound presence. This end-to-end AI enhancement drastically improved consistency, reduced manual effort, and standardized results across the laboratory process.
We trained an advanced AI engine on more than 5,000 analyst-reviewed chromatograms and embedded it directly into the client’s first-level review workflow to automate key steps in chromatographic analysis. The system intelligently smooths noisy baselines by learning real-world noise patterns, adjusts retention times to correct for minor instrument-driven RT shifts, and detects common matrix interference such as suppression or enhancement effects in gummies, beverages, and similar sample types to automatically correct them. It also enhances peak integration accuracy by identifying overlapping peaks and precisely separating them to reflect true compound presence. This end-to-end AI enhancement drastically improved consistency, reduced manual effort, and standardized results across the laboratory process.
Results
We successfully enabled the client to achieve faster project turnaround, higher team productivity, and significantly improved operational visibility. With AI-driven workload insights, automated task allocation, and streamlined communication across teams, the organization was able to maximize its existing resources without increasing headcount. The solution reduced delays, strengthened accountability, and directly contributed to a measurable boost in ROI—ultimately transforming their day-to-day operations into a more efficient, data-driven workflow.
We successfully enabled the client to achieve faster project turnaround, higher team productivity, and significantly improved operational visibility. With AI-driven workload insights, automated task allocation, and streamlined communication across teams, the organization was able to maximize its existing resources without increasing headcount. The solution reduced delays, strengthened accountability, and directly contributed to a measurable boost in ROI—ultimately transforming their day-to-day operations into a more efficient, data-driven workflow.