Leverage our GenAI-powered test case generation tool to automate the creation and validation of test methods in Laboratory Information Management Systems. By integrating AI models and a private multimodal Large Language Model LLM, this solution significantly reduces manual effort while enhancing accuracy, consistency, and compliance across various validation steps.

The LLM plays a crucial role by interpreting diverse data sources—such as text documents, method protocols, technical standards, and structured data within LIMS—allowing it to cross-validate test methods, parameter lists, and configurations. This intelligent automation ensures that the entire process adheres to industry standards, improving operational efficiency and accuracy.

Test Method and Naming Verification:

The solution begins by validating naming conventions and parameter lists for test methods by analyzing the Naming Guide and comparing it with LIMS metadata. The LLM flags inconsistencies, ensures all required fields (e.g., Parameter List ID, Repeat Count, Equipment links) are completed correctly, and confirms compliance with naming standards, reducing human errors and maintaining procedural accuracy.

Parameter and Configuration Validation:

The solution extracts relevant parameters (e.g., Data Type, Units, Replicates) from the test method documentation and compares them to LIMS configurations. This guarantees that the parameter structures align with the test method flow, ensuring data consistency. The LLM also automates sample creation in LIMS to validate data integrity and alignment.

Consumables and Instrument Integration:

The multimodal LLM interprets the test method documents to identify the required consumables and instruments, verifying their availability and status (active or expired) within LIMS. This ensures that only available, compliant items are used in test setups, further enhancing accuracy and efficiency.

Validation of Conditional Statements and Calculations:

One of the most complex tasks involves verifying conditional logic and calculated components within test methods. The LLM extracts parameters, rules, and conditions from LIMS master data and generates test data to validate various pathways. Through simulations (using Design of Experiment or LLM methods), it checks for boundary conditions and exception cases, ensuring that calculations and workflows operate as expected.

Unit Consistency and Standards Compliance:

The LLM ensures that the units in Lab Vantage result parameters match the technical standards, preventing errors caused by incorrect units. In cases requiring manual calculations, such as using the Master Verification template, the LLM automates complex calculations, generates reports, and uploads them as evidence for documentation.

In summary, this end-to-end automated solution provides seamless test method validation in LIMS by integrating AI to handle both structured and unstructured data. Its ability to validate workflows, interpret complex calculations, and ensure regulatory compliance significantly reduces human error, boosting efficiency and ensuring accurate test method verification.