Slash turnaround time – Boost ROI and maximize existing resources

A leading food-testing lab faced 75-minute review cycles that capped throughput. We deployed an AI review engine, trained on 5000+ historical chromatograms, that halved review times and doubled daily output without adding staff.

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.

1. 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.
2. Matrix Interference Issues
   Food and cannabis products often come with complex matrices (e.g., sugars, fats, flavorings) that interfere with accurate compound detection. Analysts frequently had to correct for enhancements, suppression, or co-eluted peaks manually.
3. 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.

We trained an AI engine on 5000+ analyst-reviewed chromatograms and integrated it directly into the first-level review workflow. It resulted in :

• Baseline Corrections
  Automatically smooths noisy baselines by learning real-world noise patterns.
• Retention Time Adjustments
  Adjusted for small RT shifts due to instrument variability, ensuring peaks appeared at expected locations.
• Matrix Interference Corrections
  Recognized common suppression/enhancement patterns from sample types like gummies or beverages and auto-corrected them.
• Peak Integration and Separation
  Identified overlapping peaks and accurately split them where needed to reflect the real compound presence.

Quantifiable Results
Within weeks of deployment, the lab realized dramatic efficiency gains: doubling output, slashing review times, and delighting clients with faster reports.

1. 2x Increase in Sample Throughput
   Analysts who could earlier process 6–7 samples per day could now manage 11–12 with the same accuracy. The lab achieved a 2x improvement in daily output without hiring new staff.
2. 70% Time Savings per Sample
   AI reduced the average review time from 1.5 hours to just 25 minutes, freeing up analysts for more value-added tasks.
3. 30% Reduction in Retesting
   With consistent peak detection and reduced subjective errors, the need for retesting due to unclear results dropped significantly.
4. Faster Turnaround, Happier Clients
   The lab’s average report delivery timeline dropped from 4 days to under 2, improving client satisfaction and enabling faster go-to-market timelines for their customers.

Before AI, 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.

Ready to transform your lab’s operations?  

Let’s set up a 15-minute demo to learn how AI can cut turnaround time and boost ROI-no new hires required. Let’s talk.