eXponence

eXponence is Quartic's Industrial Intelligence platform designed to bring agentic AI, real-time predictions, and anomaly detection to manufacturing operations. It is built for both process engineers and data scientists, offering no-code and pro-code tools that enable teams to build models, define rules, and generate actionable insights directly on top of plant data—without the delays associated with traditional analytics tooling.

By unifying rules engines, AutoML, mechanistic models, Jupyter Notebooks, and agentic AI into a single platform, eXponence empowers domain experts and data scientists alike to accelerate decision-making across lines, assets, and sites. The platform is designed to work with sparse, high-variance industrial data, supporting small-data and unsupervised models so that manufacturers do not need thousands of labeled samples to get started.

Core Capabilities

• Rules Engine: Trigger alerts based on setpoints, thresholds, and process deviations using an intuitive, no-code interface.
• Expressions: Build custom KPIs such as OEE, yield, or energy efficiency using calculated soft tags—no scripting required.
• Jupyter Notebook: Harness open-source machine learning for anomaly detection, optimization, and experimentation within the platform.
• AutoML Builder: Empower subject matter experts to develop supervised learning models for quality prediction, anomaly detection, or forecasting without a data science background.
• Event Frames: Track, categorize, and contextualize deviations or batch events to accelerate root cause analysis.
• MLOps Pipeline: Deploy and monitor ML models, mechanistic models, and digital twins in manufacturing operations for continuous improvement.

Key Applications

• Baseline Performance: Model steady-state operations using small-data AI to benchmark and monitor without extensive labeled datasets.
• Anomaly Detection: Use AutoML to detect subtle process drifts or failure modes in real time.
• Quality Prediction: Apply multivariate analysis and predictive models to forecast quality outcomes mid-batch and optimize setpoints.
• Process Optimization: Combine mechanistic modeling and AI to recommend optimal process parameters live.
• KPI Tracking: Monitor production KPIs in real time, triggering early interventions to prevent quality or efficiency losses.
• Event Tracking: Capture and analyze excursions with automated event frames linked to operations, material flow, and asset context.

Distinctive Features

• No-Code Intelligence: Build and deploy AI-powered logic with no scripting or coding required, making the platform accessible to process engineers and SMEs.
• Small Data Friendly: Works effectively with sparse, high-variance industrial data without requiring large volumes of labeled samples.
• Faster Innovation Cycles: Minimizes delays between model development and operational deployment so insights reach the plant floor quickly.
• Built for Convergence: Unifies logic, predictions, and event tracking in one scalable industrial platform.
• Role-Based Usability: Tailored tools for SMEs, data scientists, and operations teams, enabling each role to work efficiently within the same environment.
• Open Architecture Support: Compatible with open-source ML libraries, mechanistic models, and digital twins—allowing teams to extend or integrate as needed without vendor lock-in. Python models can be imported via Jupyter Notebooks or the MLOps pipeline.
• Built-In Collaboration: Embedded AutoML and support for custom modeling allow process engineers and data scientists to configure alerts, build reports, and deploy models all in one place.

Proven Real-World Results

• AI-driven fermentation optimization delivered a 10% yield improvement and 3x faster deviation detection in biopharma.
• Intelligent batch optimization achieved a 20% increase in average beer yield and a 49% reduction in batch variability in food and beverage manufacturing.
• Reliability applications in egg-based vaccine production recovered 5% of cycle time and enabled 5x faster repair times.
• Predictive quality optimization cut lab costs by 80% in CPG manufacturing.
• Predictive harvest forecasting delivered 15% more production capacity in life sciences.

eXponence is part of Quartic's broader Industrial Intelligence layer, sitting alongside the iLuminator Industrial DataOps product and a suite of applications including Process Optimizer, Reliability, Batch MVDA, PD Optimizer, and Automated PAT. The platform is designed to serve manufacturers across life sciences, chemicals, consumer packaged goods, and food and beverage, enabling AI deployment in days rather than months.