SepaIQ unifies and contextualizes manufacturing data in real time for analysis, AI, machine learning,
and seamless integration with higher-level business systems.
Introduction to SepaIQ
SepaIQ is a complete manufacturing intelligence solution that unifies and contextualizes production data to strengthen the effectiveness of higher-level AI and business intelligence tools. Leveraging high-performance analysis, real-time predictions, and more, SepaIQ identifies potential issues before they impact productivity and empowers proactive decision-making on the plant floor.
Some features include:
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High-Performance Analysis
Critical operations like HMI, SCADA, and MES can push production servers to their limits, leaving limited compute capacity for analysis. SepaIQ optimizes performance by offloading analytics to a cluster of servers, reducing the risk of overburdening production systems and disrupting manufacturing operations. This ensures that real-time data analysis and historical queries run smoothly without impacting core production processes.
SepaIQ connects time-series, relational, and transactional data to provide a complete, real-time view of operations. For instance, a sudden drop in machine efficiency (time-series data) is more meaningful when linked to the specific work order being produced (relational data) and the availability of raw materials (transactional data). With this level of context, analytics and AI models can distinguish between equipment issues, production inefficiencies, and supply chain disruptions.
Customize data group hierarchies and analytics to match your organization’s structure, whether following ISA-95, a modified standard, or an entirely custom framework. With a no-code, drag-and-drop interface, data groups can be configured for any purpose, including concrete items (e.g., physical equipment), abstract groupings (e.g., logical classifications), or virtual items (e.g., digital twins). These groups can seamlessly integrate time-series, relational, and transactional data, allowing users to analyze real-time performance, historical trends, and business events within a unified structure.
Real-Time Plant Floor Updates
SepaIQ analyzes data, performs predictions, and delivers valuable insights back to the people who can act on them in real time. Whether it’s adjusting processes, reducing downtime, or improving quality, operators and supervisors get the information they need to make quick, informed decisions. Instead of waiting for reports or digging through dashboards, they see the latest analysis right where they need it, helping them respond faster and keep production running smoothly.
Predicting issues before they happen can mean the difference between a minor adjustment and a major disruption. SepaIQ uses machine learning to predict potential future events by analyzing patterns in real production data, including schedules, operator performance, machine cycles, material quality, and environmental factors. By recognizing the conditions that led to past issues, SepaIQ helps manufacturers take preventive action—whether that means adjusting maintenance schedules, fine-tuning processes, or addressing material inconsistencies before they impact production.
Generative AI, powered by Large Language Models (LLMs), allows plant floor staff to access and interact with their production data using natural language instead of navigating complex reports. LLMs process questions like, “What’s today’s production efficiency?” or “Are there any signs of equipment failure?” by interpreting intent, retrieving relevant data, and generating clear, actionable responses. AI can also detect potential losses or process issues and proactively alert operators before they escalate. By making complex data accessible through conversation, LLM-powered AI helps teams stay informed and respond quickly.
As data demands fluctuate, a single server can quickly become a bottleneck. To prevent this, SepaIQ uses a cluster-based architecture that dynamically distributes workloads across multiple servers, keeping analytics responsive even during peak processing times. Configuration is managed at the cluster level, eliminating the tedious task of configuring each server. A detailed event log provides visibility of all server activity and errors within the cluster for streamlined monitoring and troubleshooting.
SepaIQ structures and organizes manufacturing data in a consistent format, making it easier to access, analyze, and share across systems. This supports the Unified Namespace (UNS) concept by transforming raw data into a structured, contextualized format that can be published in real time. By standardizing data across machines, processes, and enterprise systems, SepaIQ eliminates the need for complex integrations and manual data handling.
CI/CD Support
SepaIQ supports Continuous Integration and Continuous Delivery (CI/CD) workflows by enabling configurations to be versioned and managed in repositories like Git. Importing and exporting configurations in the same JSON format as its RESTful API ensures seamless integration and efficient management.
Post-Production Updates
Manufacturing data is never static—downtime reasons change, lab results come in, and records need constant updates. SepaIQ handles these changes seamlessly by saving database values only when they change, automatically adjusting related data and instantly notifying all subscribers. These updates are tracked in a change log to provide a transparent record of modifications, simplifying the process of reviewing, auditing, or reverting changes.
SepaIQ’s machine learning is powered by XGBoost, an advanced algorithm that iteratively trains a series of weak prediction models—typically decision trees—and combines their predictions into a strong ensemble model. Each subsequent model corrects errors from the previous ones, resulting in a highly accurate final prediction. This approach prevents overfitting through built-in regularization, efficiently handles missing data, and highlights key factors influencing predictions.
To accelerate performance, SepaIQ uses vectorized data structures that enable fast computation and smooth integration with XGBoost’s parallel processing capabilities—making it ideal for large, complex manufacturing datasets.
For manufacturers with existing AI initiatives, SepaIQ offers flexibility: prediction models can be imported, or it can simply be used to organize and deliver structured, high-quality data to preferred AI and BI tools.
SepaIQ provides built-in OEE and SPC calculators that deliver instant insights without manual setup, along with prebuilt options for filtering and aggregating data. Custom calculations can be created using drag-and-drop tools and basic JavaScript expressions, making it easy to tailor metrics to your specific needs. All calculations run in real-time and can be applied to both historical and live streaming data.
Evaluate textual data—like operator notes, maintenance logs, and quality inspection reports—to identify emotional tone and patterns tied to production outcomes. Spotting early signs of frustration, training gaps, or unclear instructions allows teams to detect and address inefficiencies before they impact performance. By linking sentiment trends to production data, SepaIQ provides deeper insight into how human factors influence efficiency, quality, and overall operations.
SepaIQ supports widely used connection methods like MQTT, Kafka, and RESTful APIs, enabling seamless data collection from MES, SCADA, ERP, and other business systems—without requiring custom integrations. Whether capturing real-time equipment data, accessing historical production records, or pulling in quality data, SepaIQ simplifies data collection using existing protocols. Results can be easily shared and visualized with Ignition, as well as AI and BI tools like Tableau or Power BI. This ensures teams can leverage their preferred systems for reporting, dashboards, and advanced analytics while always having access to structured, real-time production data.
Handling large volumes of manufacturing data shouldn’t slow down critical operations. SepaIQ optimizes performance by streaming data in smaller, manageable sets while executing calculations in real time. This prevents overloading servers, ensuring that analytics tasks—like historical data queries—don’t disrupt production systems. Additionally, SepaIQ leverages store-and-forward technology to prevent data loss during network failures.
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Discover how SepaIQ is redefining smart manufacturing—explore our in-depth feature post here.