{"id":10155,"date":"2025-08-04T02:16:40","date_gmt":"2025-08-04T02:16:40","guid":{"rendered":"https:\/\/www.cogainav.com\/?post_type=listivo_listing&#038;p=10155"},"modified":"2025-08-04T02:16:40","modified_gmt":"2025-08-04T02:16:40","slug":"user-evaluation","status":"publish","type":"listivo_listing","link":"https:\/\/www.cogainav.com\/es\/listado\/user-evaluation\/","title":{"rendered":"User Evaluation"},"content":{"rendered":"User Evaluation\u2019s differentiation begins with architecture. Instead of chaining together discrete third-party models, the company has engineered an end-to-end pipeline that fuses automatic speech recognition (ASR), large language models (LLMs) and multimodal embeddings. The result is a single knowledge fabric that can reason across languages, media types and research objectives.\r\n\r\n \r\n<h3 class=\"wp-block-heading\">Multilingual ASR with Custom Vocabulary<\/h3>\r\n \r\n\r\nThe ASR layer is trained on 57 languages and continuously fine-tuned on domain-specific corpora. Users can override generic vocabularies by uploading glossaries\u2014crucial for healthcare, fintech or any vertical with dense jargon. Speaker diarization and live timestamping occur in real time, reducing post-processing overhead.\r\n\r\n \r\n<h3 class=\"wp-block-heading\">LLM-Driven Synthesis &amp; Tagging<\/h3>\r\n \r\n\r\nOnce audio is transcribed, proprietary LLMs classify utterances by intent, emotion and theme, then auto-generate hierarchical tags. These tags are not static; the model re-clusters insights as new data arrives, ensuring that longitudinal studies remain coherent even when research questions evolve.\r\n\r\n \r\n<h3 class=\"wp-block-heading\">Multimodal Chat Interface<\/h3>\r\n \r\n\r\nThe \u201cChat with AI\u201d feature leverages retrieval-augmented generation (RAG) to ground every answer in source transcripts, video frames or survey rows. Users can ask, \u201cWhat usability friction did first-time mobile users mention?\u201d and receive an annotated clip reel plus a summary table citing exact timestamps.\r\n\r\n \r\n<h3 class=\"wp-block-heading\">Security &amp; Compliance by Design<\/h3>\r\n \r\n\r\nAll processing happens in isolated containers with AES-256 encryption at rest and TLS 1.3 in transit. A zero-retention policy means user data is never reused for model training, addressing GDPR, HIPAA and SOC 2 Type II requirements without extra configuration.","protected":false},"author":1,"template":"","listivo_14":[434],"listivo_8605":"","listivo_8606":[""],"class_list":["post-10155","listivo_listing","type-listivo_listing","status-publish","hentry","listivo_14-ai-other-tools","listivo_8605-freemium","listivo_8606-web"],"listivo_145":["https:\/\/www.cogainav.com\/wp-content\/uploads\/2025\/08\/User-Evaluation-AI-first-user-research-platform-.webp"],"listivo_8661":"https:\/\/userevaluation.com\/","_links":{"self":[{"href":"https:\/\/www.cogainav.com\/es\/wp-json\/wp\/v2\/listings\/10155","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.cogainav.com\/es\/wp-json\/wp\/v2\/listings"}],"about":[{"href":"https:\/\/www.cogainav.com\/es\/wp-json\/wp\/v2\/types\/listivo_listing"}],"author":[{"embeddable":true,"href":"https:\/\/www.cogainav.com\/es\/wp-json\/wp\/v2\/users\/1"}],"version-history":[{"count":1,"href":"https:\/\/www.cogainav.com\/es\/wp-json\/wp\/v2\/listings\/10155\/revisions"}],"predecessor-version":[{"id":10157,"href":"https:\/\/www.cogainav.com\/es\/wp-json\/wp\/v2\/listings\/10155\/revisions\/10157"}],"wp:attachment":[{"href":"https:\/\/www.cogainav.com\/es\/wp-json\/wp\/v2\/media?parent=10155"}],"wp:term":[{"taxonomy":"listivo_14","embeddable":true,"href":"https:\/\/www.cogainav.com\/es\/wp-json\/wp\/v2\/listivo_14?post=10155"},{"taxonomy":"listivo_8605","embeddable":true,"href":"https:\/\/www.cogainav.com\/es\/wp-json\/wp\/v2\/listivo_8605?post=10155"},{"taxonomy":"listivo_8606","embeddable":true,"href":"https:\/\/www.cogainav.com\/es\/wp-json\/wp\/v2\/listivo_8606?post=10155"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}