Rev AI

Converts pre-recorded audio files (submitted via URL) to text through a job-based asynchronous API that returns speaker-segmented monologues with word-level timestamps. The system processes audio through proprietary models trained on 7M+ hours of human-verified speech data, returning structured JSON with speaker IDs and per-word timing information (ts/end_ts fields). Processing typically completes within ~1 minute for standard files, with results retrievable via polling or webhook callbacks.

Processes live audio streams with low-latency transcription output, enabling real-time caption generation and live meeting transcription. Implementation details (streaming protocol, latency guarantees, output format) are mentioned in documentation but not technically specified. Supports continuous audio input with incremental transcript updates.

Provides transcription service with compliance certifications (HIPAA, SOC II, GDPR, PCI DSS) and security features including encryption at rest and in transit. Supports on-premises and cloud deployment options enabling data residency requirements. 99.99% uptime SLA ensures service reliability for regulated industries. Enables secure handling of sensitive audio content (healthcare, financial, legal).

Integrates with Model Context Protocol (MCP) enabling AI assistants (Cursor, VS Code) to access Rev AI transcription capabilities through standardized protocol. Installable on Cursor and VS Code enabling developers to invoke transcription from within IDE. Specific MCP capabilities and integration details not documented.

Enables direct integration with LLM platforms (ChatGPT, Claude) through 'Copy for LLM' and 'Open in ChatGPT/Claude' options. Allows transcripts to be exported in LLM-compatible format for downstream AI processing, summarization, or analysis. Integration mechanism and export format not documented.

Implements usage-based pricing model where customers pay for transcription based on consumption (billing unit unknown — likely per-minute or per-request). Free tier available for account signup with limits unknown. Enterprise pricing available via custom negotiation. Pricing details not publicly documented in available materials.

Allows users to inject domain-specific vocabulary, acronyms, and terminology into the transcription model to improve accuracy for specialized language (medical, legal, technical jargon). Implementation mechanism (vocabulary file format, injection method, model adaptation approach) not documented. Improves WER for domain-specific terms by providing context to the underlying ASR model.

Generates precise word-level timing information by aligning transcribed text back to the original audio waveform, enabling frame-accurate subtitle generation and video synchronization. Uses forced alignment algorithms to map each word to its exact start/end timestamps in the audio. Output includes ts (start time in seconds) and end_ts (end time in seconds) for every transcribed word element.

Analyzes transcribed text to automatically extract key topics, themes, and subject matter discussed in the audio. Implementation approach (NLP model type, topic taxonomy, extraction algorithm) not documented. Enables automatic categorization and content discovery without manual review.

Analyzes emotional tone and sentiment expressed in transcribed audio content, enabling automatic detection of customer satisfaction, agent performance, or conversation sentiment. Implementation (sentiment model type, granularity level, scoring approach) not documented. Provides sentiment classification at conversation or segment level.

Detects the language spoken in audio content and returns ISO 639-1 language code, enabling automatic routing to language-specific transcription models. Operates on audio stream without requiring pre-specification of language. Supports 57+ languages with automatic detection enabling multi-language batch processing.

Implements job-based asynchronous processing pattern where audio transcription jobs are submitted via POST endpoint, tracked via job ID, and results retrieved when complete. Supports two notification modes: polling via GET endpoint (discouraged in production) or webhook callbacks to user-specified endpoint. Job object includes id, status (in_progress/transcribed), created_on timestamp, metadata field for tagging, and language specification.

Retrieves completed transcription results in structured JSON format with monologues array containing speaker-segmented dialogue. Each monologue includes speaker integer ID and elements array with word-level details (type, value, ts, end_ts). Uses custom Accept header (application/vnd.rev.transcript.v1.0+json) for versioned API response format. Enables direct integration with downstream systems without parsing unstructured text.

Supports transcription in 57+ languages with language specification via ISO 639-1 code parameter. Default language is English ('en'). Models trained on diverse speech data from 7M+ hour human-verified corpus enabling accurate transcription across languages with claimed bias mitigation across ethnic backgrounds, nationalities, genders, and accents. Language parameter specified in job submission and returned in job metadata.