To achieve Agility in manufacturing – embrace Variability – Part 1
Posted 1551 days ago
Deliver connected, responsive, and agile operations by linking data, AI, and teams for confident, real-time decisions across plants.
See how Quartic helps manufacturers cut costs, improve quality, and boost throughput across Life sciences, Chemicals, CPG, and more.
Access technical guides, case studies, and best practices to make faster, more confident manufacturing decisions.
Smarter manufacturing, one decision at a time.
eXponence unifies rules engines, AutoML, mechanistic models, and agentic AI into one industrial intelligence platform. It empowers domain experts with no-code tools and supports data scientists with open interfaces—accelerating insights across your plant.
Trigger alerts based on setpoints, thresholds, and process deviations using an intuitive, no-code interface.
Build custom KPIs such as OEE, yield, or energy efficiency using calculated soft tags—no scripting required.
Harness open source machine learning in manufacturing for anomaly detection, optimization, and experimentation.
Empower SMEs to develop supervised learning models for quality prediction, anomaly detection, or forecasting.
Track, categorize, and contextualize deviations or batch events for accelerated root cause analysis.
Deploy and monitor ML, mechanistic models, and digital twins in manufacturing operations for continuous improvement.
eXponence integrates rule-based logic, complex event processing, and advanced analytics into a unified platform. Designed for both process experts and data scientists, it seamlessly creates and deploys intelligent workflows and models without the delays of traditional tools.
No-code or pro-code—eXponence flexibly adapts to how your teams work while scaling across lines, assets, and sites.
With embedded AutoML and support for custom modeling, eXponence empowers both process engineers and data scientists. Configure alerts, build reports and deploy models—all in one place.
Compatible with open source ML libraries, mechanistic models, and digital twins in manufacturing. Extend or integrate as needed—without vendor lock-in.
eXponence allowed us to detect and address process inefficiencies without needing constant support from data scientists.
Model steady-state operations using small-data AI to benchmark and monitor without extensive labeled datasets.
Use autoML in manufacturing to detect subtle process drifts or failure modes in real time.
Apply multivariate analysis and predictive models to forecast quality outcomes mid-batch and optimize setpoints.
Combine mechanistic modeling and AI in manufacturing operations to recommend optimal process parameters live.
Monitor production KPIs in real time, triggering early interventions to prevent quality or efficiency losses.
Capture and analyze excursions with automated event frames linked to operations, material flow, and asset context.
Build and deploy AI-powered logic with no scripting or code required.
Works with sparse, high-variance industrial data—no need for thousands of labeled samples.
Minimize delays between model development and operational deployment.
Unifies logic, predictions, and event tracking in one scalable industrial platform.
Designed for SMEs, data scientists, and operations teams with tailored tools for each.
Whether it’s predicting batch outcomes or optimizing performance, eXponence helps you deploy AI in days—not months. Built for the complexity of industrial data.
eXponence is Quartic’s Industrial Intelligence platform. It provides no-code and pro-code tools for building models, rules, and insights on top of manufacturing data.
No. eXponence supports small-data and unsupervised models, ideal for industrial environments with limited labeled data.
Yes. Engineers can build rules, track events, and even deploy machine learning using the no-code ML Model builder.
Yes. You can bring your first-principles models or develop digital twins for process optimization inside eXponence.
Absolutely. Use Jupyter Notebooks or import models via the ML Ops pipeline for deployment and monitoring.
