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Description
FigEditor is an innovative AI tool designed to transform research concepts, initial sketches, references, and existing graphics into refined, editable illustrations suitable for academic papers, posters, and presentations. Users can begin by providing a descriptive text prompt about a mechanism, pathway, workflow, experiment, or architectural design, and they also have the option to upload a rough drawing or a reference image to inform aspects such as composition, color scheme, density, and visual organization. The platform generates a well-structured draft that includes clear labels, arrows, and an orderly layout, allowing researchers to modify text, shapes, colors, connectors, modules, and specific areas without needing to recreate the entire figure from scratch. Additionally, previously created PNG or JPG images can be converted into vector formats, enabling further manipulation. The process employed by FigEditor involves a comprehensive multi-step pipeline that not only generates an initial draft but also identifies visual components, extracts clean assets, constructs a cohesive SVG, and finalizes the vector output through refinement. This systematic approach ensures that users have a versatile tool at their disposal for producing high-quality scientific illustrations that enhance their presentations. Overall, FigEditor streamlines the creation of scientific visuals, making the process more efficient and accessible for researchers.
Description
At Iris.ai we have spent the last 6 years building an award-winning AI engine for scientific text understanding. Our algorithms for text similarity, tabular data extraction, domain-specific entity representation learning and entity disambiguation and linking measure up to the best in the world. On top of that, our machine builds a comprehensive knowledge graph containing all entities and their linkages to allow humans to learn from it, use it and also give feedback to the system.
The Iris.ai Researcher Workspace is a flexible tool suite that allows to approach a project in a variety of ways. Modules include content based explorative search, machine analysis of document sets, extracting and systematizing data points, automatically writing summaries of multiple documents - and very powerful filters based on context descriptions, the machine’s analysis, or specific data points or entities. The Iris.ai engine for scientific text understanding is a powerful interdisciplinary system that can be automatically reinforced on a specific research field for much more nuanced machine understanding - without human training or annotation.
API Access
Has API
API Access
Has API
Integrations
GPT Image 1.5
Nano Banana
Nano Banana 2
Nano Banana Pro
Pricing Details
$9.50 per month
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
FigEditor
Country
United States
Website
figeditor.ai/
Vendor Details
Company Name
Iris.ai
Founded
2015
Country
Norway
Website
iris.ai/
Product Features
Product Features
Data Extraction
Disparate Data Collection
Document Extraction
Email Address Extraction
IP Address Extraction
Image Extraction
Phone Number Extraction
Pricing Extraction
Web Data Extraction
Natural Language Processing
Co-Reference Resolution
In-Database Text Analytics
Named Entity Recognition
Natural Language Generation (NLG)
Open Source Integrations
Parsing
Part-of-Speech Tagging
Sentence Segmentation
Stemming/Lemmatization
Tokenization
Qualitative Data Analysis
Annotations
Collaboration
Data Visualization
Media Analytics
Mixed Methods Research
Multi-Language
Qualitative Comparative Analysis
Quantitative Content Analysis
Sentiment Analysis
Statistical Analysis
Text Analytics
User Research Analysis
Alternatives
No Alternatives