Streamline Your GxP Processes Using AI
From predictive analytics to automated software validation, xLM’s AI solutions intelligently simplify and amplify your operations


Our Services
Our Process
01 Assess
We assess your processes to upgrade them with AI enabled automation.
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02 Deploy
We deploy our AI Enables Services with best practices baked in. All services are Continuous Validation enabled.
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03 Manage
We ensure your apps are running with continuous governance enabled.
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Continuous Intelligent Validation (cIV)
The definition: "Continuous validation is providing documented evidence to certify that an app not only met the pre-established acceptance criteria, but “continuous” to meet thus mitigating the risk of unknown changes."
Continuous validation is not just a "point in time" validation. It is a type of validation which connects various points in time (initial, patch, upgrade validation) with continuous smoke and regression testing. This feature provides the documented evidence that the an app worked well not just at discrete points in time in the past, but continues to function as expected in the present. This also mitigates the risk of any change in either the IaaS/PaaS layer (for cloud apps) or the underlying IT infrastructure (for on-prem apps) that can potentially alter its behavior.
xLM's QMS is based on industry standards as well as applicable GxPs. xLM's QMS is a robust framework based on ISO 9001:2015, GAMP 5 and ASTM E2500 as well as FDA 21 CFR Part 11, EudraLex Annex 11. xLM's QMS enables us to deliver our managed services that not only meets, but exceeds the expectations of regulatory agencies in the USA, Europe and Japan.
cIV is an AI-powered continuous validation platform that aims to revolutionize software validation by introducing autonomous agents that can generate an URS, develop test cases and execute them with very minimal human input. It employs advanced AI algorithms to enhance test coverage, reduce false positives and negatives while adapting to application changes. The platform automates the entire Software Development Life Cycle (SDLC), from generating User Requirements Specifications (URS) to executing tests, thereby minimizing manual effort and increasing reliability. cIV is organized into three (3) distinct modules: URS Generation, Test Case Generation, and Test Automation - all accessible via the browser.
cIV leverages advanced language models to automate the creation of User Requirements Specifications (URS), generate detailed test cases, and produce Test Plan Execution (TPE) reports. cIV is built on xLM’s Continuous Intelligence platform. The input can be as simple as an user manual. Our agents can navigate the System Under Test (SUT) leveraging its auto exploration capabilties. The accuracy of the output can be further increased by simply walking thru test case scenarios using the built-in recorder function. The above inputs are catalogued into a knowledge graph which forms the basis for the agents to build the artifacts.
Step 1: Autogeneration of the knowledgebase by cIV agents: Inputs - URS Template, Software User Manuals, Manual Test Cases (if available), auto exploration of the SUT
Step 2: cIV Agent-1 generates the URS in minutes. Human-in-the-loop should be used to refine/approve the URS.
Step 3: cIV Agent-2 takes the approved URS and generates detailed test cases. It ensures coverage of the requirements to fullfil the traceability.
Step 4: cIV Agent-3 Executes the test cases and delivers a GxP compliant TPE in PDF format.
Leveraging advanced language models and Retrieval-Augmented Generation (RAG) technology, cIV delivers exceptional User Requirement Specification (URS) documentation. The system combines sophisticated AI capabilities with vector databases to accomplish two critical objectives: retrieving pertinent information and transforming it into meticulously structured, GxP-compliant specifications. This innovative automation not only ensures consistency across documents but also significantly reduces human error while capturing all user requirements with precision and thoroughness.
After URS validation, cIV streamlines the requirements management process through sophisticated AI-driven tools that effectively categorize and consolidate specifications. The system then transforms these consolidated requirements into comprehensive test cases, structured in both BDD and web-action formats.
Leveraging its robust knowledge repository and specialized information extractors, the platform systematically generates a step by step test cases to fulfill the corresponding requiremetns. This methodical approach ensures thorough test coverage while maintaining precise alignment with user expectations and requirements.
The cIV Test Automation Module streamlines test execution through sophisticated parsing of uploaded test cases and seamless interaction with the System Under Test (SUT). Leveraging advanced cross-browser testing tools, it meticulously validates application behavior while generating comprehensive, GxP-compliant Test Plan Execution (TPE) reports. Through its agentic framework, the module ensures exceptional precision and operational efficiency throughout the entire testing lifecycle.
cIV demonstrates exceptional future-readiness through its sophisticated AI-driven architecture. The platform dynamically adapts to evolving application environments, consistently maintaining precision in User Requirement Specifications (URS), test cases, and automation scripts. This inherent adaptability establishes cIV as a powerful and sustainable solution for long-term software validation needs. During the execution step, the test automation code is dynamically generated based on the current SUT and does not rely upon any pre-generated code.
Continuous Service Management (cSM)
ContinuousSM is a comprehensive, AI-powered service management solution designed to help life sciences companies streamline processes, improve efficiency, and maintain GxP compliance. It's built on the Atlassian platform and delivered as a managed service, meaning it's continuously validated and requires minimal setup time.
ContinuousSM provides a centralized platform for capturing, tracking, and managing requests from various channels. It allows for automated workflows with approvals, customizable queues, and insightful performance reports, enabling teams to optimize resource allocation and identify bottlenecks.
ContinuousSM's Asset Management goes beyond traditional CMDBs, offering a flexible structure for managing any asset type. It helps track asset lifecycles, ownership, and validation status. This enables informed decision-making, proactive impact analysis for changes, and faster incident resolution.
ContinuousSM streamlines change management with automated change requests, risk assessment tools, and integration with ContinuousDM for collaborative change planning. This allows teams to accelerate approvals for low-risk changes, while ensuring thorough review and documentation for high-risk changes.
ContinuousSM leverages AI for various tasks, including:
Virtual Agent: Provides automated, conversational support through Slack, answering questions and resolving basic requests, freeing up human agents for more complex issues.
AI-Powered Insights: Analyzes data to identify patterns and generate actionable insights, improving decision-making for service optimization.
ContinuousSM is offered as a managed service. This means the application is continuously qualified, and each customer's instance is continuously validated. This eliminates the need for extensive validation documentation and testing on the customer side, allowing for rapid deployment and assured compliance.
ContinuousSM offers a centralized knowledge base with powerful search functionalities, including machine learning-powered relevance and federated search across various content types. This, combined with the AI-powered virtual agent, enables efficient self-service, deflecting common requests and empowering users to find answers quickly.
Continuous Predictive Maintenance (cPdM)
ContinuousPdM: ContinuousPdM represents a groundbreaking advancement in predictive maintenance technology, seamlessly combining sophisticated data analytics, machine learning algorithms, and real-time monitoring capabilities. This innovative approach transforms traditional maintenance paradigms by replacing reactive measures with proactive strategies, ultimately leading to significant improvements in both equipment reliability and operational efficiency.
Step 1: Industrial sensors continuously track critical operational parameters, including vibration levels, temperature fluctuations, and pressure readings. This comprehensive monitoring system streams collected data to a centralized platform, enabling immediate analysis and actionable insights in real-time.
Step 2: Leveraging a secure cloud infrastructure, the system processes data to develop sophisticated predictive models that accurately anticipate equipment failures and proactively identify maintenance requirements.
Step 3: GxP-compliant dashboards deliver comprehensive business intelligence through actionable insights, detailed analytical reports, and robust audit-ready documentation, ensuring regulatory compliance while streamlining operational oversight.
Through this innovative strategy, organizations can significantly reduce unplanned downtime while maximizing asset utilization and enhancing overall operational productivity.
ContinuousPdM harnesses sophisticated machine learning technologies, specifically Isolation Forests and Long Short-Term Memory (LSTM) networks, to revolutionize predictive maintenance. These cutting-edge models perform three critical functions:
Advanced anomaly detection within sensor data streams.
Accurate prediction of equipment failure patterns.
Data-driven maintenance schedule optimization through actionable insights.
Through its dual capability of processing both historical trends and real-time operational data, ContinuousPdM delivers highly accurate predictive analytics. This precision enables maintenance teams to implement targeted interventions at optimal times, significantly boosting operational efficiency while extending equipment lifespan.
Key Benefits:
Reduced Downtime: Sophisticated monitoring systems proactively detect potential issues, preventing unexpected equipment failures.
Optimized Maintenance: Strategic scheduling of maintenance activities during planned downtimes minimizes operational disruptions and maximizes productivity.
Cost Savings: Intelligent resource management eliminates unnecessary maintenance procedures while optimizing spare parts inventory levels.
Improved Compliance: Comprehensive audit trails and GxP-compliant dashboards ensure regulatory adherence and streamlined reporting.
This data-driven approach not only enhances manufacturing efficiency but also drives sustainable profitability through smarter asset management and reduced operational costs.
Continuous Predictive Maintenance (cPdM) leverages Reinforcement Learning (RL) technology to significantly improve maintenance forecasting accuracy. This advanced approach continuously refines its predictive models through systematic feedback loops, enabling increasingly precise maintenance recommendations over time. Here's how it works:
Feedback Loop Integration: Reinforcement Learning (RL) improves decision-making by incorporating real-world maintenance outcomes into its framework. Analyzing results like reduced equipment downtime and cost savings, the model refines its predictive accuracy and strategic recommendations, leading to a more adaptive maintenance solution.
Dynamic Adaptation: Reinforcement Learning (RL) shows great adaptability by analyzing and responding to changing operational and sensor data patterns. This capability ensures the model remains effective despite fluctuations in equipment conditions and evolving usage patterns throughout its lifecycle.
Optimal Decision-Making: Through advanced reinforcement learning (RL), maintenance strategies are optimized by analyzing scenarios that balance risk management (including unexpected equipment failures) and cost optimization (like avoiding premature repairs). This approach leads to more accurate maintenance scheduling, enhancing operational reliability while minimizing costs.
Improved Pattern Recognition: Reinforcement Learning (RL) excels at analyzing complex relationships among variables like vibration, temperature, and load conditions. This advanced analytical ability allows the model to detect subtle patterns and correlations that traditional static algorithms often miss.
Continuous Improvement: Reinforcement Learning (RL) refines predictive accuracy iteratively, adapting to new data while aligning current operations with historical performance patterns.
Through Reinforcement Learning (RL), predictive maintenance systems evolve into adaptive solutions with high accuracy. This approach reduces equipment downtime, optimizes costs, and extends industrial machinery's service life.
Continuous Temperature Mapping (cTM)
cTM is a service designed for the MedTech, Biotech, and Pharma sectors that offers continuous temperature mapping through automation, data handling, and machine learning. It streamlines the temperature mapping process by automating everything from data collection to dashboard presentation with almost negligible human effort.
Step 1: Calibrated RF dataloggers are placed in their mapping locations and linked to a gateway via the guest wireless network. These loggers can measure Temperature, RH.
Step 2: Data automatically flows into xLM’s validated cloud. User can log into xLM’s Intelligent Portal, specify some basic parameters and upload the data from the fixed BMS sensors. cTM automatically generates all the dashboards needed to validate the mapping study.
Step 3: User can download GxP compliant reports in PDF format to attach them to the summary report.
cTM employs data transformation techniques to convert raw data into a readable format standardizing information the dataloggers. The process includes data pre-processing with normalization methods like min-max scaling and z-score normalization, along with feature engineering to extract valuable insights from the data. Machine learning models facilitate predictive analytics for forecasting temperature trends and detecting anomalies, utilizing algorithms such as Isolation Forests and Local Outlier Factor.
cTM provides three types of dashboards, each serving a specific purpose:
Temporary Sensors Dashboard: Designed for comprehensive analysis of NFC and RF datalogger information, this tool specializes in short-term data mapping. The software offers three essential capabilities: sophisticated deviation analysis, intuitive timeline visualization, and statistical T-test comparisons. These features enable users to effectively monitor, analyze, and interpret temporal data patterns.
Fixed Sensors Dashboard: Utilizing data from permanently installed sensors, this dashboard delivers comprehensive thermal analysis, temperature pattern monitoring, and advanced visual tools designed for efficient anomaly detection and diagnosis.
Sensor Mapping Dashboard: This dashboard includes analysis of data by comparing readings between fixed and temporary sensor installations. Through proximity-based clustering and detailed deviation graphs, it delivers comprehensive environmental surveillance and performance tracking.
cTM offers numerous advantages to streamline operations in highly regulated environments:
Time Savings: Automated processes streamline operations by eliminating the need for manual data logging and reporting, resulting in enhanced efficiency and reduced human intervention.
Error Reduction: Machine Learning technology revolutionizes temperature mapping by significantly improving data accuracy and substantially reducing measurement errors. This advanced computational approach ensures more precise and reliable mapping analysis results..
Scalability: Highly adaptable and versatile, cTM seamlessly integrates into warehouses of all sizes, effectively managing even the most complex storage environments.
Proactive Monitoring: Predictive analytics empowers organizations to implement proactive measures, enabling timely interventions that prevent potential deviations before they occur..