Gladia

WebSocket-based live transcription engine that converts audio streams to text with <300ms end-to-end latency, supporting continuous audio input without fixed context windows. Implements partial transcript delivery (<100ms) via a 'Partials' feature that streams intermediate results before final transcription is complete, enabling responsive UI updates and real-time user feedback during active speech.

HTTP-based async transcription API that accepts pre-recorded audio files (via file upload or URL), queues them for processing, and returns results via polling or webhook. Implements server-side processing with claimed 'no hallucinations' guarantee, supporting 100+ languages with automatic language detection and code-switching (mixed-language) handling within single files.

Post-transcription feature that generates abstractive or extractive summaries of transcribed content, condensing long audio into key points, action items, or executive summaries. Processes transcribed text to identify salient information and generate concise summaries without requiring manual review of full transcripts.

Transcription feature that automatically detects the language(s) spoken in audio and handles code-switching (mixing of multiple languages within single utterance or file). Solaria-1 model identifies language boundaries and switches recognition models or language contexts mid-stream, enabling accurate transcription of multilingual content without pre-specification of language.

Feature that connects transcribed audio output directly to large language models (LLMs) for downstream processing, enabling structured data extraction, question answering, or content generation from audio. Provides integration patterns for piping transcription results into LLM APIs (OpenAI, Anthropic, etc.) with optional structured output schemas (JSON, function calling).

Post-transcription feature that automatically segments long-form audio content into chapters or sections based on topic changes, speaker transitions, or temporal boundaries. Generates chapter markers with timestamps and optional titles, enabling navigation and content discovery in podcasts, audiobooks, or long meetings.

API rate limiting and concurrency management system that varies by subscription tier: Starter tier (25 async, 30 real-time concurrent requests), Growth tier (flexible concurrency), and Enterprise tier (unlimited concurrency). Enables cost-conscious developers to start small and scale to unlimited throughput as demand grows, with transparent tier-based pricing ($0.61/hr Starter, $0.20/hr Growth, custom Enterprise).

Enterprise privacy feature that enables immediate deletion of audio files and transcripts after processing, with no data retention for model training or analytics. Available on Enterprise tier with explicit 'zero data retention' option, combined with GDPR/HIPAA compliance certifications (SOC 2 Type II) across all paid tiers. Enables privacy-sensitive use cases (healthcare, legal, financial) without data residency concerns.

Post-transcription audio intelligence feature that identifies and segments distinct speakers within a single audio file, labeling transcript segments by speaker identity (Speaker 1, Speaker 2, etc.). Processes transcribed audio to assign speaker labels to word-level timestamps, enabling conversation analysis and multi-speaker meeting transcripts.

Post-transcription content filtering that identifies and masks personally identifiable information (PII) categories within transcribed text, replacing detected PII with placeholder tokens or redaction markers. Operates on completed transcription output to sanitize sensitive data before downstream processing or storage.

Post-transcription translation feature that converts transcribed text from source language to specified target language(s), enabling multilingual content distribution. Operates on completed transcription to produce translated text while preserving word-level timestamps and speaker attribution from original transcription.

Post-transcription feature that generates subtitle files (SRT, VTT, or similar formats) with word-level timestamps and speaker labels, enabling video/audio content to be captioned. Converts transcribed text and timing metadata into standard subtitle formats compatible with video players and streaming platforms.

Pre-transcription configuration feature that injects domain-specific vocabulary, acronyms, or proper nouns into the Solaria-1 model's recognition pipeline, improving accuracy for specialized terminology. Accepts custom word lists or phrase mappings that bias the model toward recognizing specific terms with higher confidence, reducing misrecognition of technical jargon.

Post-transcription text normalization feature that applies custom spelling rules, phonetic mappings, or abbreviation expansions to transcribed text. Enables standardization of variant spellings, acronym expansion, and domain-specific spelling conventions without re-transcribing audio.

Post-transcription NLP feature that identifies and extracts named entities (persons, organizations, locations, dates, etc.) from transcribed text, tagging them with entity type and confidence scores. Enables structured data extraction from unstructured transcription output for downstream processing, search indexing, or knowledge base population.

Post-transcription feature that analyzes emotional tone and sentiment polarity (positive, negative, neutral) of transcribed speech segments, potentially with speaker-level granularity. Processes transcribed text and optionally audio features to classify sentiment and assign confidence scores, enabling conversation analytics and customer satisfaction measurement.