
AlisQI is a Quality Management platform built for process and batch manufacturers who want operational control without adding administrative overhead.
Where many QMS platforms were designed around document storage and event tracking, AlisQI was architected as a data-first system. Quality, laboratory, and production data are structured and connected in a single operational backbone. This enables teams to see deviations earlier, understand performance trends in context, and act before issues escalate into waste, rework, or customer complaints.
The platform includes modular capabilities across document control, training, deviations, CAPA, audits, risk management, supplier quality, SPC, and EHS. These capabilities are deployed through focused, ready-to-use Solvers that combine workflows, logic, dashboards, and analytics to address specific operational challenges without unnecessary scope.
Because the system is built on structured, connected data, manufacturers can apply practical AI directly inside their workflows. This includes automated extraction of supplier COA data without predefined templates, conversational access to quality records, intelligent rule generation, and pattern recognition across incidents to strengthen corrective action effectiveness.
Solvers are production-ready from the outset and evolve as products, processes, or sites change. Improvements do not require custom development or large IT programs, allowing organizations to modernize quality step by step.
Manufacturers across chemicals, plastics, packaging, food and beverage, automotive, and industrial sectors use AlisQI to reduce firefighting, increase predictability, strengthen compliance, and turn quality data into operational intelligence.
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Epicor Connected Process Control (CPC) enables manufacturers to digitize and standardize production processes through a flexible no-code/low-code manufacturing platform. Combining digital work instructions, operator guidance, process control, and real-time data collection, CPC helps teams improve execution, reduce errors, and maintain consistent production across assembly and manufacturing operations.
With support for connected equipment and shop floor devices, manufacturers can capture production and quality data directly from operations while gaining greater visibility into performance, defects, rework, and process compliance. Product traceability capabilities provide a detailed history of each product's build and inspection record, supporting quality initiatives and continuous improvement efforts.
CPC is well suited for manufacturers managing complex product variations, dynamically presenting operators with the appropriate instructions and process requirements for each build. By connecting people, processes, and production data within a single platform, manufacturers can improve quality, increase operational visibility, and drive more consistent outcomes across the shop floor.
Available on-premises or in the cloud, CPC scales from individual production areas to enterprise-wide manufacturing environments.
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MPCPy
MPCPy is a Python-based library specifically created to facilitate the testing and implementation of occupant-integrated model predictive control (MPC) in building systems. This innovative tool focuses on utilizing data-driven, simplified physical or statistical models to predict the performance of buildings and improve control methodologies. It consists of four key modules that offer object classes for tasks such as data importation, engagement with either real or simulated systems, estimation and validation of data-driven models, and optimization of control inputs. While MPCPy acts as a comprehensive integration platform, it relies on a variety of free, open-source third-party software for executing models, conducting simulations, implementing parameter estimation techniques, and optimizing solvers. This includes Python libraries for scripting and data manipulation, as well as specialized software solutions designed for specific functions. Importantly, the tasks involving modeling and optimization of physical systems are currently based on the requirements of the Modelica language, which significantly enhances the package's flexibility and capabilities. Overall, MPCPy empowers users to harness sophisticated modeling methods within a dynamic and cooperative environment, ultimately fostering improved building system performance. Furthermore, it opens up opportunities for researchers and practitioners alike to experiment with cutting-edge control strategies tailored to real-world scenarios.
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Cybernetica CENIT
Cybernetica is dedicated to delivering Nonlinear Model Predictive Control (NMPC) by leveraging mechanistic models. Our cutting-edge software, Cybernetica CENIT, boasts a flexible architecture designed to tackle a wide array of industrial challenges while producing optimal control strategies. This encompasses sophisticated multivariable optimal control, predictive control techniques, and smart feed-forward methods, all while adeptly managing various constraints. Additionally, our adaptive control features utilize state and parameter estimation, allowing for the integration of feedback derived from indirect measurements through the process model. Employing nonlinear models facilitates effective performance across broad operational ranges, significantly improving the management of complex nonlinear processes. Consequently, this approach reduces the dependence on step-response experiments and enhances the precision of state and parameter estimations. Moreover, we provide tailored control solutions for both batch and semi-batch operations, efficiently overseeing nonlinear processes that endure varying conditions. Our technology also guarantees optimal transitions in product grades during continuous operations, ensures the safe management of exothermic reactions, and controls unmeasured variables such as conversion rates and product quality. Ultimately, these advancements lead to decreased energy consumption and a minimized carbon footprint, while simultaneously boosting overall process efficiency. In conclusion, Cybernetica is fully committed to pioneering industrial control solutions that not only enhance performance but also promote sustainability in various sectors. Our relentless pursuit of innovation positions us as leaders in the field, enabling us to adapt to the evolving needs of our clients.
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