Izitext
AI-driven transcription and subtitling engine for high-speed content localization.
Enterprise-grade AI transcription and clinical documentation intelligence.
Inscripta is a high-performance AI platform specializing in automated speech recognition (ASR) and natural language processing (NLP), specifically architected for high-stakes environments like healthcare, legal, and government sectors. By 2026, Inscripta has positioned itself as a market leader in 'Contextual Transcription,' moving beyond simple text conversion to structured data extraction. The platform utilizes a proprietary transformer-based architecture that integrates deep acoustic modeling with domain-specific linguistic layers. This allows it to handle complex terminology, diverse accents, and noisy environments with lower word error rates (WER) than general-purpose models. Technically, Inscripta provides a robust REST API and WebSocket support for real-time streaming, enabling developers to build low-latency voice applications. Its 2026 roadmap emphasizes 'Clinical Intelligence,' where the AI doesn't just transcribe but automatically populates EMR/EHR fields, identifies medical billing codes (ICD-10/11), and highlights critical patient sentiment. The architecture is designed for zero-trust environments, supporting on-premise deployment and strict data residency compliance, making it the preferred choice for enterprises requiring sovereign data control.
Separates up to 12 distinct speakers in a single audio stream using spatial audio processing and vocal fingerprinting.
AI-driven transcription and subtitling engine for high-speed content localization.
The AI-powered media editor that allows you to edit video and audio as easily as a text document.
AI-powered text-based audio editing that turns high-fidelity production into simple document editing.
The knowledge-focused podcast player for capturing and sharing insights in real-time.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Uses NER (Named Entity Recognition) to identify and redact Protected Health Information in real-time.
Sub-200ms latency for live captioning via optimized C++ core processing.
Allows users to upload audio samples to fine-tune the model against specific background noises (e.g., sirens, clinic bustle).
Deep learning model that predicts punctuation and casing based on semantic intent rather than just pauses.
Maps medical transcripts directly to ICD-10 and CPT codes using a proprietary medical ontology.
Detects and switches language models mid-sentence for bilingual speakers.
Doctors spending more time on charts than with patients.
Registry Updated:2/7/2026
High cost and slow turnaround for certified legal transcripts.
Need for instant, accurate data for market sentiment analysis.