
Pipefy is a low-code Business Orchestration and Automation Technologies (BOAT) platform designed to act as a modern middleware layer for the enterprise stack.
Rather than replacing existing Systems of Record (SORs) like SAP, Oracle, or Salesforce, Pipefy wraps them in an agile orchestration layer. This architecture allows technical teams to modernize legacy operations and extend the life of core systems without the risks associated with "rip and replace" projects. Pipefy provides the infrastructure to sanitize data inputs, manage complex business logic, and orchestrate API calls between fragmented endpoints.
Technical & Architectural Highlights:
• Adaptive Governance Framework: Pipefy solves the "Shadow IT" problem by establishing IT-sanctioned "Safe Zones." Business users can build workflows within these guardrails, while IT retains control over critical data, integrations, and permissions via a centralized console.
• Agentic AI Engine (BYOLLM): The platform features a governable AI Agent Studio. Unlike "black box" solutions, Pipefy supports a Bring Your Own LLM approach, allowing enterprises to integrate preferred models (Azure OpenAI, AWS Bedrock) securely to automate document analysis (OCR) and decision-making.
• Robust Connectivity: Built with an API-first philosophy, Pipefy offers a GraphQL API, Webhooks, and enterprise-grade iPaaS capabilities to ensure seamless data interoperability across the stack.
• Security & Compliance: Engineered for regulated industries, the platform is ISO 27001, ISO 27701, and SOC2 Type II certified, supporting compliance with GDPR and SOX standards.
Pipefy empowers IT leaders to eliminate technical debt and clear development backlogs by safely delegating low-complexity builds to business units.
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Epicor Connected Process Control (CPC) is a flexible no-code/low-code MES and manufacturing operations platform that helps manufacturers standardize production and assembly processes, improve quality, and increase visibility across the shop floor.
Using digital work instructions with multimedia content, manufacturers can guide operators through critical tasks, enforce process control, and improve consistency across products, workstations, and production lines.
CPC connects equipment and devices to collect production and quality data in real time, helping manufacturers identify issues faster, reduce waste, and support continuous improvement.
CPC provides product traceability with a complete historical record of each product's build and inspection history. Manufacturers use CPC to support assembly verification, error proofing, quality inspections, operator guidance, and other connected manufacturing initiatives.
For manufacturers with complex product variations, CPC can dynamically present the correct work instructions based on the product or configuration being built, helping ensure products are assembled accurately and consistently.
Available on-premises or in the cloud, CPC scales from a single work cell to enterprise-wide deployments.
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Model Predictive Control Toolbox
The Model Predictive Control Toolbox™ offers a comprehensive suite of functions, an intuitive app, Simulink® blocks, and practical reference examples to facilitate the development of model predictive control (MPC) systems. It caters to linear challenges by enabling the creation of implicit, explicit, adaptive, and gain-scheduled MPC strategies. For more complex nonlinear scenarios, users can execute both single-stage and multi-stage nonlinear MPC. Additionally, this toolbox includes deployable optimization solvers and permits the integration of custom solvers. Users can assess the effectiveness of their controllers through closed-loop simulations in MATLAB® and Simulink environments. For applications in automated driving, the toolbox also features MISRA C®- and ISO 26262-compliant blocks and examples, allowing for a swift initiation of projects related to lane keep assist, path planning, path following, and adaptive cruise control. You have the capability to design implicit, gain-scheduled, and adaptive MPC controllers that tackle quadratic programming (QP) problems, and you can generate an explicit MPC controller derived from an implicit design. Furthermore, the toolbox supports discrete control set MPC for handling mixed-integer QP challenges, thus broadening its applicability in diverse control systems. With these extensive features, the toolbox ensures that both novice and experienced users can effectively implement advanced control strategies.
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MPCPy
MPCPy is a Python library designed to support the testing and execution of occupant-integrated model predictive control (MPC) within building systems. This tool emphasizes the application of data-driven, simplified physical or statistical models to forecast building performance and enhance control strategies. It comprises four primary modules that provide object classes for data importation, interaction with real or simulated systems, data-driven model estimation and validation, and optimization of control inputs. Although MPCPy serves as a platform for integration, it depends on various free, open-source third-party software for model execution, simulation, parameter estimation techniques, and optimization solvers. This encompasses Python libraries for scripting and data manipulation, along with more specialized software solutions tailored for distinct tasks. Notably, the modeling and optimization tasks related to physical systems are currently grounded in the specifications of the Modelica language, which enhances the flexibility and capability of the package. In essence, MPCPy enables users to leverage advanced modeling techniques through a versatile and collaborative environment.
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