Amazon Comprehend
Enterprise-grade natural language processing to extract insights and relationships from unstructured text.
Transform unstructured customer feedback into actionable business intelligence with AI-driven synthesis.
Keatext is a sophisticated AI-powered text analytics platform engineered for enterprise-grade customer experience (CX) management. By 2026, its technical architecture has evolved to leverage a hybrid LLM-NLP framework that goes beyond simple sentiment scoring to perform 'Impact Analysis'—quantifying how specific product features or service issues correlate with overall NPS and CSAT scores. The platform is designed to ingest massive datasets from disparate sources, including support tickets, social media, survey responses, and call transcripts. Its core differentiator lies in its 'Synthesis' engine, which utilizes generative AI to condense thousands of qualitative comments into coherent, multi-paragraph executive summaries that highlight root causes and recommended actions. Positioned as a mission-critical tool for CX leaders, Keatext focuses on reducing the 'time-to-insight' by automating the manual coding of data, allowing organizations to pivot strategies in real-time based on the voice of the customer. The platform's 2026 iteration emphasizes seamless API-first integration and SOC2-compliant data handling, making it a staple in the tech stacks of global retail, finance, and SaaS companies.
Statistical correlation engine that identifies which specific topics have the highest numerical impact on overall customer satisfaction metrics.
Enterprise-grade natural language processing to extract insights and relationships from unstructured text.
The world's most sophisticated NLP engine for omni-channel conversational intelligence.
Transform unstructured text into objective visual intelligence with Bayesian concept mapping.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
LLM-powered summarization layer that converts clusters of customer comments into natural language executive briefs.
Proprietary NLP models that support 20+ languages without the need for pre-translation, preserving local nuances.
Uses historical data patterns to forecast future sentiment shifts before they become widespread issues.
Architecture that identifies if a customer is complaining about the same issue across social media and support tickets simultaneously.
Unsupervised learning algorithms that flag sudden deviations in feedback patterns that don't fit existing categories.
The system automatically suggests new categories and tags as customer language evolves over time.
Identifying why a new feature has high usage but low satisfaction.
Registry Updated:2/7/2026
High volume of repetitive tickets straining the support team.
Customers churning without leaving specific low NPS scores.