User Evaluation

User Evaluation’s 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.

Multilingual ASR with Custom Vocabulary

The ASR layer is trained on 57 languages and continuously fine-tuned on domain-specific corpora. Users can override generic vocabularies by uploading glossaries—crucial for healthcare, fintech or any vertical with dense jargon. Speaker diarization and live timestamping occur in real time, reducing post-processing overhead.

LLM-Driven Synthesis & Tagging

Once 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.

Multimodal Chat Interface

The “Chat with AI” feature leverages retrieval-augmented generation (RAG) to ground every answer in source transcripts, video frames or survey rows. Users can ask, “What usability friction did first-time mobile users mention?” and receive an annotated clip reel plus a summary table citing exact timestamps.

Security & Compliance by Design

All 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.