Automatically logs every model inference, data input, and configuration change, creating an immutable record of the AI system's decisions and lifecycle events.
Continuously tracks model performance metrics, data drift, concept drift, and outlier detection in production, alerting teams to degradation.
Generates explanations for individual model predictions and assesses models for potential biases across protected attributes.
Allows administrators to define and enforce custom governance policies, such as required approvals for model changes or automatic blocking of non-compliant inferences.
Provides a unified view of all monitored models, their health, active alerts, and compliance status, with tools to generate standardized reports.
A bank uses Monitaur to monitor its AI-driven credit scoring and fraud detection models. The platform logs every decision, detects drift in applicant data, and generates audit trails to prove compliance with regulations like fair lending laws (e.g., ECOA) and model risk management guidelines (SR 11-7). This reduces manual audit burden and provides evidence to regulators.
An insurance company deploys Monitaur to govern AI models that automate policy pricing and claims triage. The tool monitors for unintended bias in risk assessments, explains individual premium calculations to customers, and maintains a record of model versions and decisions for internal risk committees and external auditors.
A healthcare provider uses Monitaur to oversee diagnostic support algorithms. It tracks model performance against real-world outcomes, ensures patient data is handled appropriately, and creates the necessary documentation for FDA submissions or internal ethics board reviews, facilitating safer clinical deployment.
An enterprise ML team integrates Monitaur into their CI/CD pipeline. It automatically validates new model versions against governance policies before promotion to production, monitors them post-deployment, and links performance issues back to specific training data or code changes, streamlining responsible AI development.
A company procuring AI services from external vendors uses Monitaur to monitor the black-box models provided. It establishes a governance layer to track the vendor model's performance, data usage, and decision patterns, ensuring they meet the company's ethical and operational standards despite being externally built.
Sign in to leave a review
15Five operates in the people analytics and employee experience space, where platforms aggregate HR and feedback data to give organizations insight into their workforce. These tools typically support engagement surveys, performance or goal tracking, and dashboards that help leaders interpret trends. They are intended to augment HR and management decisions, not to replace professional judgment or context. For specific information about 15Five's metrics, integrations, and privacy safeguards, you should refer to the vendor resources published at https://www.15five.com.
20-20 Technologies is a comprehensive interior design and space planning software platform primarily serving kitchen and bath designers, furniture retailers, and interior design professionals. The company provides specialized tools for creating detailed 3D visualizations, generating accurate quotes, managing projects, and streamlining the entire design-to-sales workflow. Their software enables designers to create photorealistic renderings, produce precise floor plans, and automatically generate material lists and pricing. The platform integrates with manufacturer catalogs, allowing users to access up-to-date product information and specifications. 20-20 Technologies focuses on bridging the gap between design creativity and practical business needs, helping professionals present compelling visual proposals while maintaining accurate costing and project management. The software is particularly strong in the kitchen and bath industry, where precision measurements and material specifications are critical. Users range from independent designers to large retail chains and manufacturing companies seeking to improve their design presentation capabilities and sales processes.
3D Generative Adversarial Network (3D-GAN) is a pioneering research project and framework for generating three-dimensional objects using Generative Adversarial Networks. Developed primarily in academia, it represents a significant advancement in unsupervised learning for 3D data synthesis. The tool learns to create volumetric 3D models from 2D image datasets, enabling the generation of novel, realistic 3D shapes such as furniture, vehicles, and basic structures without explicit 3D supervision. It is used by researchers, computer vision scientists, and developers exploring 3D content creation, synthetic data generation for robotics and autonomous systems, and advancements in geometric deep learning. The project demonstrates how adversarial training can be applied to 3D convolutional networks, producing high-quality voxel-based outputs. It serves as a foundational reference implementation for subsequent work in 3D generative AI, often cited in papers exploring 3D shape completion, single-view reconstruction, and neural scene representation. While not a commercial product with a polished UI, it provides code and models for the research community to build upon.
