Matt Lab
Matt Lab, a pharmaceutical testing laboratory, came to us with a problem that was risking both their efficiency and their compliance. Their highly qualified scientists were spending more time on data entry than on analysis, creating a bottleneck in their reporting pipeline.
To generate a single Certificate of Analysis (CoA), scientists had to manually gather these disparate outputs and type the results line-by-line into a unified report. This manual process created significant risks: - Transcription Errors: "Fat-finger" mistakes during copy-pasting could compromise data integrity and lead to audit failures. - Reporting Lag: Final reports took hours to compile after testing was finished, delaying product release. - Talent Misuse: PhD-level scientists were being utilized as data entry clerks.
- Universal Data Ingestion: We set up a system that ingests output from any source. Whether it is a scanned PDF from a legacy machine or a direct data stream, the system captures it. - Context-Aware OCR: Unlike standard OCR which just reads text, our AI model was trained on lab reports. It understands the context—identifying specific parameters like "retention time," "absorbance," or "purity %"—regardless of where they appear on the page. - Automated Report Compilation: The extracted data is instantly structured and auto-populated into the final Master Report or LIMS (Laboratory Information Management System), requiring a human only for final verification, not entry. Why This Worked: - Hardware Agnostic: We didn't force them to buy new lab equipment. The AI adapted to the existing outputs, making the solution cost-effective and fast to deploy. - Compliance by Design: By removing manual keystrokes, we eliminated transcription errors. The system creates a digital audit trail linking the final report value directly back to the source document. - Scientist-Centric: The workflow didn't change how scientists ran tests; it only changed how they reported them. This ensured immediate adoption with zero friction.
