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What is Skimle?
Skimle transforms the process of turning unstructured qualitative data into structured and analyzable datasets through advanced artificial intelligence techniques. Unlike RAG chatbots that merely fetch unrelated snippets, Skimle diligently processes entire document collections from the beginning—assessing each section, extracting insights, and organizing them into a well-defined thematic hierarchy.
Users can upload a variety of qualitative data formats, including interview transcripts, PDFs, audio or video recordings, and reports. The approach Skimle adopts, inspired by rigorous thematic analysis, systematically codes every text segment, identifies recurring themes, and creates a detailed "spreadsheet" that arranges documents as rows and themes as columns. Each insight is meticulously linked to verified quotes, guaranteeing that the information presented is accurate and free from any fabrication.
With support for over 100 languages and the ability to manage more than 1,000 documents in a single project, Skimle adheres strictly to GDPR regulations relevant in the EU, ensuring thorough traceability between themes and their associated quotes. Additionally, users benefit from customizable category options, AI-enhanced chat for rationale, and the ability to export results into Word, Excel, or PowerPoint formats.
What distinguishes Skimle is its unique capability to combine the rigorous methodologies of academic research with the swift processing power of artificial intelligence. Tasks that typically take weeks to complete using NVivo or similar traditional tools can be accomplished in just a few hours with Skimle, all while providing essential audit trails necessary for peer review and validation. This remarkable efficiency not only conserves valuable time but also significantly improves the overall qualitative research experience, making it more accessible and streamlined than has ever been possible before. Furthermore, Skimle’s innovative approach enhances the quality of research outputs, allowing researchers to focus on deriving meaningful conclusions from their data rather than getting bogged down in the analytical process.
What is OpenText Unstructured Data Analytics?
OpenText™ offers Unstructured Data Analytics Products that harness the power of AI and machine learning to assist organizations in uncovering and utilizing vital insights concealed within various forms of unstructured data, including text, audio, videos, and images. By enabling organizations to connect data at scale, they can gain a clearer understanding of the context and content embedded in rapidly growing unstructured content. The platform provides unified analytics for text, speech, and video across more than 1,500 data formats, facilitating the extraction of insights from diverse media types. Utilizing technologies like OCR, natural language processing, and other advanced AI models allows organizations to monitor and interpret the essence of unstructured data effectively. Additionally, leveraging cutting-edge innovations in deep neural networks and machine learning enables a deeper comprehension of both spoken and written language found within the data, ultimately leading to the discovery of even greater insights. This comprehensive approach not only enhances data understanding but also empowers organizations to make more informed decisions based on the valuable information extracted from their unstructured data.
API Availability
Has API
API Availability
Has API
Pricing Information
$0
Free Trial Offered?
Free Version
Pricing Information
Pricing not provided.
Free Trial Offered?
Free Version
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Company Facts
Organization Name
Skimle
Date Founded
2025
Company Location
Finland
Company Website
www.skimle.com
Company Facts
Organization Name
OpenText
Date Founded
1991
Company Location
Canada
Company Website
www.opentext.com/products/unstructured-data-analytics
Categories and Features
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
Categories and Features
Artificial Intelligence
Chatbot
For Healthcare
For Sales
For eCommerce
Image Recognition
Machine Learning
Multi-Language
Natural Language Processing
Predictive Analytics
Process/Workflow Automation
Rules-Based Automation
Virtual Personal Assistant (VPA)
Business Intelligence
Ad Hoc Reports
Benchmarking
Budgeting & Forecasting
Dashboard
Data Analysis
Key Performance Indicators
Natural Language Generation (NLG)
Performance Metrics
Predictive Analytics
Profitability Analysis
Strategic Planning
Trend / Problem Indicators
Visual Analytics
Data Analysis
Data Discovery
Data Visualization
High Volume Processing
Predictive Analytics
Regression Analysis
Sentiment Analysis
Statistical Modeling
Text Analytics
Enterprise Search
AI / Machine Learning
Faceted Search / Filtering
Full Text Search
Fuzzy Search
Indexing
Text Analytics
eDiscovery
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
Text Mining
Boolean Queries
Document Filtering
Graphical Data Presentation
Language Detection
Predictive Modeling
Sentiment Analysis
Summarization
Tagging
Taxonomy Classification
Text Analysis
Topic Clustering