BrandMap® 10
Worldwide, professional researchers prefer a user-friendly and swift tool for the analysis and creation of presentation-ready biplots, correspondence maps, mdpref, and MCA maps. This powerful 64-bit software is compatible with both PCs and MACs. Our unique Brand Projector I enables users to compute and visually represent the necessary attribute alterations for repositioning a brand (column) to any desired location on a map. In addition, Brand Projector II offers researchers the ability to modify attributes (rows) interactively, allowing them to observe the corresponding movement of the brand (column) in real-time. This dynamic interaction enhances the understanding of brand positioning strategies significantly.
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Pipefy
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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QMSys GUM
The QMSys GUM Software is designed for assessing the uncertainty inherent in physical measurements, chemical analyses, and calibration processes. It employs three distinct methodologies to compute measurement uncertainty. The first, GUF Method for linear models, targets linear and quasi-linear models, aligning with the GUM Uncertainty Framework. This approach calculates partial derivatives, representing the initial terms of a Taylor series, to ascertain sensitivity coefficients for the equivalent linear model, followed by the determination of combined standard uncertainty using the Gaussian error propagation law. The second, GUF Method for nonlinear models, caters to nonlinear models where results exhibit symmetric distribution. This method incorporates various numerical techniques, including nonlinear sensitivity analysis and higher-order sensitivity indices, as well as quasi-Monte Carlo simulations utilizing Sobol sequences. With its multifaceted approach, the software provides comprehensive tools for uncertainty analysis across different measurement contexts.
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ndCurveMaster
ndCurveMaster, a specialized curve fitting software, is designed to fit curves with multiple variables. It automatically applies nonlinear equations to your datasets. These can be observed or measured values. The software supports curve and surfaces fitting in 2D 3D 4D 5D ..., dimensions. ndCurveMaster is able to handle any data, no matter how complex or how many variables there are.
ndCurveMaster, for example, can efficiently derive the optimal equations for a dataset that has six inputs (x1-x6) and a corresponding output Y. For example: Y = a0 - a1 - exp(x1)0.5 + a2 ln(x2)8... + a6 x65.2 to accurately match measured value.
ndCurveMaster uses machine learning numerical methods to automatically fit the most suitable nonlinear regression function to your dataset, and discover the relationships between inputs and outputs. This tool supports various curve fitting methods, including linear, polynomial, and nonlinear methods. It also utilizes essential validation and goodness-of-fit tests to ensure accuracy. Additionally, ndCurveMaster provides advanced assessments, such as detecting overfitting and multicollinearity, using tools like the Variance Inflation Factor (VIF) and the Pearson correlation matrix.
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