Average Ratings 0 Ratings
Average Ratings 0 Ratings
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
No
API Access
Has API
No
Integrations
GPT Image 1.5
Yes
Nano Banana
Yes
Nano Banana 2
Yes
Nano Banana Pro
Yes
Integrations
GPT Image 1.5
No
Nano Banana
No
Nano Banana 2
No
Nano Banana Pro
No
Pricing Details
$9.50 per month
Free Trial
No
Free Version
No
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Types of Training
Training Docs
No
Webinars
Yes
Live Training (Online)
No
In Person
No
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
No
Document Extraction
No
Email Address Extraction
No
IP Address Extraction
No
Image Extraction
No
Phone Number Extraction
No
Pricing Extraction
No
Web Data Extraction
No
Natural Language Processing
Co-Reference Resolution
No
In-Database Text Analytics
No
Named Entity Recognition
No
Natural Language Generation (NLG)
No
Open Source Integrations
No
Parsing
No
Part-of-Speech Tagging
No
Sentence Segmentation
No
Stemming/Lemmatization
No
Tokenization
No
Qualitative Data Analysis
Annotations
No
Collaboration
No
Data Visualization
No
Media Analytics
No
Mixed Methods Research
No
Multi-Language
No
Qualitative Comparative Analysis
No
Quantitative Content Analysis
No
Sentiment Analysis
No
Statistical Analysis
No
Text Analytics
No
User Research Analysis
No