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The fastest way to query and visualize data using natural language.
Visually explore and understand your data with AI.
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Visually explore and understand your data with AI.
Graphext is a data analysis platform that helps users explore, visualize, and understand complex datasets using a combination of visual interfaces and AI-powered insights. It allows users to bring in data from various sources, perform tasks like customer segmentation, sentiment analysis, and trend identification, without requiring coding or advanced data science skills. The platform supports multiple languages and can be integrated with existing tools. Graphext is designed to assist in data exploration, offering AI-driven pattern detection and enabling users to gain insights in minutes. It caters to various use cases, including news and social media research, survey analysis, AI support chat analysis, and advertising.
Visually explore and understand your data with AI.
Quick visual proof for Graphext. Helps non-technical users understand the interface faster.
Graphext is a data analysis platform that helps users explore, visualize, and understand complex datasets using a combination of visual interfaces and AI-powered insights.
Explore all tools that specialize in data exploration. This domain focus ensures Graphext delivers optimized results for this specific requirement.
Explore all tools that specialize in data visualization. This domain focus ensures Graphext delivers optimized results for this specific requirement.
Explore all tools that specialize in ai-driven insights. This domain focus ensures Graphext delivers optimized results for this specific requirement.
Open side-by-side comparison first, then move to deeper alternatives guidance.
AI-powered chat interface that helps users navigate the platform and uncover insights through natural language interactions.
Interactive and customizable plotting options for in-depth data visualization and analysis.
Automatically enrich datasets with additional information and context using AI-driven data augmentation techniques.
Dynamically filter and analyze data across multiple dimensions to uncover hidden relationships and patterns.
Utilizes graph-based data structures to represent relationships between entities, enabling network analysis and pattern discovery.
Automatically analyze text data to determine the sentiment expressed, providing insights into customer opinions and attitudes.
Identifying key topics, trending audiences, and emerging trends from news articles and social media data.
Import news articles and social media data into Graphext.
Use sentiment analysis to identify the prevailing sentiment towards different topics.
Apply network analysis to identify influential users and communities.
Visualize trends and patterns using interactive graphs and charts.
Segmenting survey respondents and identifying drivers of Customer Satisfaction (CSAT) and Net Promoter Score (NPS).
Upload survey data into Graphext.
Use clustering algorithms to segment respondents based on their answers.
Apply correlation analysis to identify factors that influence CSAT and NPS.
Visualize segments and drivers using interactive dashboards.
Segmenting support conversations and identifying drivers of escalation.
Import support chat logs into Graphext.
Use topic modeling to identify common themes and issues in support conversations.
Apply sentiment analysis to gauge customer frustration levels.
Analyze conversation paths to identify drivers of escalation and areas for improvement.
Building contextual segments and explaining advertising campaign performance.
Import advertising campaign data into Graphext.
Use clustering to segment audiences based on demographics and behavior.
Apply attribution modeling to understand the impact of different touchpoints on campaign performance.
Visualize campaign performance and segment characteristics using interactive reports.
Analyzing and improving data quality from tables/CRM, outputs via UI/APIs.
Import data from tables or CRM systems into Graphext.
Use data profiling tools to identify inconsistencies, errors, and missing values.
Apply data cleaning and transformation techniques to improve data quality.
Visualize data quality metrics and track improvements over time.
Upload data via UI or API.
Visually explore data using interactive graphs and plots.
Use AI Chat to guide analysis and uncover insights.
Customize visualizations and reports.
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The fastest way to query and visualize data using natural language.
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