Amplifying Human Intelligence in the Lab
Clinlab.AI is the first agentic AI platform built for clinical laboratories, empowering scientists and managers with intelligent automation. Our agentic AI platform predicts demand, monitors quality in real time, and prevents costly errors. The result is faster turnaround, stronger compliance, and millions in potential savings.
70% of Clinical Decisions Rely on Lab Results, Yet Labs Still Operate Without AI’s Advantage
Delayed results, quality failures, and costly retesting increase operational expenses and compromise patient care. Manual quality control and outdated systems struggle to meet modern laboratory demands, creating compliance risks and inefficiencies. The future of diagnostics requires intelligent systems that anticipate and prevent problems before they impact patients and operations.
One Platform. Three Powerful AI Co-Pilots.
Across 200+ laboratories and 300,000+ monthly tests, Clinlab.AI demonstrates that smarter technology, not additional staffing, is the key to overcoming modern laboratory challenges. Our agentic AI platform empowers scientists and managers through three specialized copilots designed to enhance human expertise, not replace it.
Together, these copilots anticipate operational demands, optimize staffing, monitor quality in real time, and identify emerging health trends. By transforming laboratory data into actionable intelligence, Clinlab.AI enables faster, safer, and more efficient operations while reducing costs, delays, and compliance risks.
Stanley: Manager-Assist
Your lab’s operations co-pilot. Predicts daily test volumes, optimizes staffing and schedules, and prevents reagent waste or stockouts, ensuring resources are ready when and where they’re needed.
Marie: Scientist-Assist
Your lab’s quality co-pilot. Real-time QC monitoring with Westgard & Six Sigma logic, plus real-time patient QC (RTPQC). The right issues are flagged before patients are affected.
Elara: Biller-Assist
Your lab’s billing co-pilot. Reviews claims before submission, detects coding errors, and achieves 99% accuracy, guranteeing clean, compliant claims go out for faster reimbursement.
Proven in the Field, Not Just Theory
Nationwide Vitamin D Recall
In April 2024, Clinlab.AI's Marie flagged irregularities in low-end sample recoveries during testing of a large diagnostic company’s Vitamin D Assay calibrator. While traditional release testing had verified the calibrator within specifications, Clinlab.AI's proprietary algorithms detected subtle inconsistencies that could impact low-range diagnostic accuracy. This early detection underscored the powerful role of AI in identifying quality control gaps that may not be immediately evident through conventional testing processes.
IT Failure: Analyzer Drift
A missed calibration on a CBC analyzer caused hemoglobin values to steadily drift upward, producing inaccurate results that could have gone unnoticed for days. Traditional monitoring offered no immediate warning, leaving patients at risk and creating the potential for widespread retesting. Marie detected the deviation early, identifying the drift before results were released, and enabling rapid troubleshooting. By restoring accuracy without disrupting care, Marie not only prevented compromised outcomes but also protected the lab from costly delays and compliance scrutiny.
QC Setup Failures
Manual QC processes missed critical errors across multiple points of setup. Inactive QC lots were never activated after testing, incorrect SD values went undetected for weeks, and wrong QC transmissions created compliance exposure. Each of these issues risked compromised results and regulatory violations that would have gone unnoticed under conventional oversight. Marie surfaced the failures instantly in real time, flagging the violations and preventing costly errors before they could impact patient care or compliance audits.
Pre-Analytical Error: Mislabeled Sample
A mislabeled PSA sample introduced the risk of reporting incorrect results to a patient. In a manual process, such pre-analytical errors are difficult to detect and often remain hidden until after results are delivered, creating potential harm and reputational risk. Elara identified the inconsistency instantly, flagging the mismatch before results left the lab. By catching the problem at its source, Elara prevented misdiagnosis, reduced unnecessary repeat testing, and underscored the value of AI in safeguarding patients where manual systems frequently fall short.