Reliability

Quartic's Reliability is an AI-driven reliability centered maintenance (RCM) application designed for manufacturing operations teams in life sciences, food and beverage, chemicals, and other process industries. It transforms traditional FMEA documentation into live, machine learning-powered monitoring agents that track asset health degradation in real time, enabling reliability and maintenance teams to shift from reactive failure tracking to proactive, risk-based decision-making — without requiring deep data science expertise or extensive historical failure data.

Many predictive maintenance initiatives fail to scale because they demand custom model development, rely on failure histories that do not exist, generate excessive nuisance alerts, or conflict with established RCM methodologies. Quartic's Reliability is purpose-built to address these gaps, offering a no-code environment that reliability engineers can own and operate independently, while still providing advanced customization options for data analysts and digital transformation teams.

Core Capabilities

• Asset Degradation Monitoring: Reliability teams can use an intuitive, no-code UI to convert failure modes directly into live monitoring agents, enabling continuous asset health degradation tracking without writing code.
• Agent-Based Fault Detection: Deploy a combination of rule-based agents, predictive ML models, and anomaly detection agents to provide comprehensive, multi-layered failure monitoring across assets.
• Actionable Recommendations: A unified view displays risk rate, anomaly frequency, and failure trajectory together, enabling informed maintenance planning and making planned maintenance intervals shorter and more effective.

Key Benefits and Differentiators

• Detect asset degradation trajectory using predictive risk scoring — plan and act based on measured risk, not eventual failure.
• No historical failure data required; the system builds anomaly detection models by learning normal operation patterns and simulating statistically likely failure patterns for validation.
• Models are trained once per asset class (e.g., pumps, bioreactors) and can be rapidly retrained and reused across similar units, enabling scalable deployment across plants.
• Empower reliability engineers with no-code AutoML tools, while also supporting customization through embedded Jupyter notebooks and integration with Quartic libraries.
• Shift from time-based to condition-based maintenance by combining condition sensor data, route-based data, and operational data in a single platform.
• Implement P-F curves for critical failure modes using abnormal operation gradients.
• Downtime, failure, and maintenance periods are tracked as events and can be included or excluded during model testing for improved accuracy.
• Failure risk is estimated through failure rate trends and risk events, with RUL (Remaining Useful Life) predictions improving significantly when historical failure data is available.

Who It Is For

• Process Engineers: Build RCM programs from FMEA logic, replace guesswork with data-driven risk scores, and minimize unplanned downtime through early risk detection.
• Data Analysts: Access ready-to-use industrial datasets, apply models with faster validation cycles, and reduce model-to-impact lag time using clean, contextualized OT/IT data.
• Quality & Compliance Teams: Gain real-time visibility into quality metrics, automate CPV and deviation detection, shorten batch release and investigation cycles, and ensure CFR21 and GMP traceability.
• Reliability & Maintenance Teams: Convert FMEAs into AI agents, implement P-F curves for critical failure modes, and make planned maintenance intervals shorter and more effective through early risk detection.
• Digital Transformation Leaders: Enable cloud or hybrid deployments for site-specific needs, reuse models across similar assets with no code, align plant operations with corporate reliability goals, and avoid pilot fatigue with scalable ML tools that reliability teams can own.

Proven Results

• Significant reduction in unplanned failures across manufacturing operations.
• High model reuse rates across asset classes, accelerating enterprise-wide scaling.
• In life sciences, customers have achieved 7x higher mean time between failures (MTBF) and 100% reduction in unnecessary maintenance in pharma operations.
• 5% cycle time recovery and 5x faster repair times in egg-based vaccine production.
• 15% faster freeze drying and 50% less downtime with AI optimization in additional deployments.

Quartic's Reliability supports cloud and hybrid deployment models to meet site-specific infrastructure requirements. The platform integrates AutoML with embedded Jupyter notebooks for teams that require deeper customization, and connects condition sensor data with operational and route-based data sources. ML-backed asset reliability solutions can be deployed in weeks, enabling manufacturers to move beyond condition monitoring and implement a fully digital RCM strategy at scale.