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A leading tools and equipment retailer struggled with erratic inventory cycles due to demand unpredictability. By deploying advanced forecasting models, such as Random Forest and XGBoost—trained on weekly sales trends—we helped them automate predictions, reduce overstocking, and improve revenue retention.
In regulated testing environments, accuracy isn’t a luxury—it’s the baseline. However, ensuring precision comes at the cost of time. In this case, a lab faced a familiar bottleneck: analysts were spending close to 1.5 hours per sample reviewing chromatograms manually—a tedious process that involved identifying peaks, correcting noise, adjusting for co-elutions, and ensuring every tiny anomaly was accounted for. These reviews were then passed on to a second-level reviewer, further stretching the turnaround.
With AI stepping into the workflow, things changed dramatically. Built using historical review data, our AI model learned how analysts performed corrections and began replicating those decisions. It handled the repetitive, error-prone first-level review in minutes, not hours.
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.
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.
Different analysts applied slightly different criteria when correcting chromatograms. This led to inconsistencies in reporting and flagged several unnecessary outliers for second-level review.
We trained an AI engine on 5000+ analyst-reviewed chromatograms and integrated it directly into the first-level review workflow. It resulted in :
Automatically smooths noisy baselines by learning real-world noise patterns.
Adjusted for small RT shifts due to instrument variability, ensuring peaks appeared at expected locations.
Recognized common suppression/enhancement patterns from sample types like gummies or beverages and auto-corrected them.
Identified overlapping peaks and accurately split them where needed to reflect the real compound presence.
“AI model trained on 5,000+ historical reviews replicates your analysts’ judgment.”
Maya, one of the lab’s senior analysts, could review around 6 cannabis gummy samples in a full day. Each required her to manually inspect noisy chromatograms, flag overlapping peaks, and struggle with matrix interferences caused by sugar and flavoring agents. By 4 PM, she would be exhausted, with more samples still waiting. With fatigue, her each subsequent review went slower and was more error-prone.
With our AI engine in place, Maya arrives to find 12 pre-reviewed chromatograms waiting in her queue. She only needs to validate the AI’s decisions—a quick 10 minute check per sample. She spends the rest of the day either clearing more complex samples or assisting junior analysts. For the lab, this means double the output without doubling the headcount. The same instruments. The same team. Just smarter workflows.
Let’s set up a 30-minute demo to learn,
How AI can cut turnaround time and boost ROI—no new hires required.
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