Provides an interactive, grid-based interface where users can visually browse thousands of images simultaneously, applying real-time filters and searches.
Automatically groups similar images within a collection using machine learning models, revealing natural patterns and categories without pre-defined labels.
Enables precise slicing of datasets using any column from linked metadata (CSV/JSON), such as date, experimental condition, or measurement value.
Allows users to train their own image classification or regression models directly within the platform using their annotated collections.
Users can create and share saved 'views' of a dataset—specific filter and search states—with colleagues via secure, shareable links.
Researchers in genomics, pathology, or drug discovery use Zegami to manage millions of microscope images. They filter by experimental parameters (e.g., gene knockout, drug concentration) and use AI clustering to identify novel cellular phenotypes or treatment effects. This accelerates the correlation of visual patterns with omics data, leading to faster hypothesis generation and validation in complex biological studies.
Materials scientists and manufacturing engineers upload images of material samples or product surfaces. By filtering based on metadata like composition, processing temperature, or stress tests, they visually correlate microstructural features with material properties. AI models can be trained to automatically detect manufacturing defects, enabling predictive quality control and reducing waste.
Museums and archives use Zegami to create searchable visual catalogs of artifact photographs, manuscript pages, or artwork. Curators can filter by period, artist, material, or provenance metadata. AI clustering helps discover stylistic similarities or conservation issues across collections, making vast archives accessible for research and public engagement without handling fragile originals.
Environmental scientists and agronomists analyze satellite or drone imagery. They import images tagged with location, date, and sensor data. By filtering for specific geographic regions or time periods, they can visually track changes in land use, vegetation health, or urban development. Custom models can be trained to classify land cover types or detect specific events like deforestation or flooding.
University labs and instructors use Zegami to create interactive datasets for student projects. Students can explore complex image datasets—like astronomical images, geological samples, or historical photographs—applying filters and seeing immediate visual results. This hands-on approach teaches data literacy, pattern recognition, and the scientific method in a more engaging and intuitive way than static textbooks or spreadsheets.
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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.
