Pranav Singh

Improves speech recognition accuracy by automatically rewriting low-confidence inputs using forward and backward language models, grammar scoring, and graph-based optimization.

Uses sequence-to-sequence models and embedding-based clustering to transform user queries into canonical representations, enabling scalable training and improved intent normalization.

A platform for publishing, discovering, testing, and composing reusable natural language modules to accelerate development of conversational systems.

Generates realistic example user phrases using fine-tuned language models and domain data, speeding assistant configuration and improving intent coverage.

Creates “thought maps” by combining natural language understanding with location and contextual signals to analyze collective intent, predict behavior, and enable location-aware applications.

Identifies speech and language errors using query similarity, semantic embeddings, and interaction signals, enabling automated data labeling and continuous model improvement.

Automatically detects and replaces low-confidence transcription tokens using neural confidence scoring, while exposing confidence signals to support labeling and vocabulary enhancement.