ARIA
Medical content, every language, one pipeline
ARIA is an AI-powered transcription, translation, and dubbing platform designed for healthcare organizations with multilingual content needs. It transforms lectures, surgical videos, and conference recordings into professionally dubbed multilingual assets — while simultaneously preparing content for AI-ready knowledge systems.
The problem we solve
Healthcare organizations sit on vast libraries of expert-led content — surgical lectures, conference recordings, training videos — locked in a single language. Traditional translation services are slow, expensive, and produce outputs disconnected from the original timing and delivery. Worse, the content remains trapped as passive media instead of becoming searchable, structured knowledge that could power AI assistants and educational platforms.
The Dubbing Director
ARIA treats translation not as a word-for-word conversion, but as a holistic dubbing direction process. An LLM analyzes the full transcript — semantic grouping, medical terminology consistency, duration matching, and speaker intent — before generating translations that respect the rhythm and meaning of the original content. This architectural choice means ARIA's output sounds natural and maintains clinical accuracy, rather than producing the mechanical results typical of segment-by-segment translation pipelines.
Core Features
AI-Powered Transcription with Speaker Diarization
ARIA generates word-level timestamped transcripts with automatic speaker identification. Whether it's a two-person surgical commentary or a multi-speaker panel discussion, every voice is accurately separated and labeled for downstream processing.
Semantic Translation Engine
Instead of translating sentence by sentence, ARIA's LLM groups semantically related segments, adjusts phrasing to match source duration, and maintains a running medical terminology glossary throughout the entire document. This supports translations that sound natural when spoken aloud.
Professional AI Dubbing
Each identified speaker is assigned a distinct AI voice with customizable voice profiles. The synthesized audio is designed to match original segment timing, producing dubbed content that can be placed directly onto a production timeline without manual adjustment.
DaVinci Resolve Integration
A native plugin for DaVinci Resolve enables video editors to run the full pipeline — transcribe, translate, dub, and place audio — without leaving their editing environment. Dubbed segments are automatically positioned on the correct timeline tracks.
Dual-Mode Workflow
ARIA supports two translation modes: fully automated LLM translation for rapid turnaround, and CSV import for reviewed translations where a human expert has verified the text. In review mode, the approved translations are treated as immutable — no LLM modification is applied during synthesis.
AI-Ready Content Structuring
Every processed file is simultaneously chunked, embedded, and prepared for vector database ingestion. This means translated content can power RAG-based chatbots, searchable knowledge bases, and AI teaching assistants without additional processing steps.
Medical Terminology Consistency
ARIA maintains a per-project glossary that tracks how domain-specific terms are translated across the entire content library. This aims to prevent inconsistencies like alternating between 'anastomosis' and 'junction' within the same course material.
Multi-Language Support
The platform currently supports approximately six language pairs with a focus on European and global medical education markets. Language coverage is designed to expand based on client needs and regional demand.
Key Benefits
Unlock Content Libraries
Transform hours of single-language expert content into multilingual training assets accessible to global audiences.
Preserve Expert Time
Eliminate the need for speakers to re-record or supervise traditional dubbing sessions — ARIA handles the full pipeline autonomously.
Production-Ready Output
Dubbed audio files are timeline-aligned and ready for immediate use in video editing workflows, aiming to reduce post-production effort significantly.
AI-Ready by Default
Every translation simultaneously generates structured, searchable data suitable for powering chatbots and knowledge systems.
Clinical Accuracy
Domain-aware translation with glossary enforcement is designed to maintain terminological consistency across large content libraries.
Flexible Quality Control
Choose between rapid AI translation or human-reviewed import, depending on content sensitivity and turnaround requirements.
How it Works
Ingest & Transcribe
Upload audio or video content. ARIA generates a full transcript with word-level timestamps and automatic speaker diarization, identifying each voice in the recording.
Analyze & Translate
The LLM Dubbing Director analyzes the complete transcript holistically — grouping segments semantically, enforcing terminology consistency, and producing duration-matched translations.
Synthesize & Dub
Each speaker receives a dedicated AI voice. Translations are synthesized into natural-sounding audio files, timed to match the original recording's rhythm and pacing.
Deliver & Structure
Dubbed audio is placed on production timelines or exported as standalone files. Simultaneously, all content is chunked and indexed for vector database ingestion and AI-ready applications.
Technical Specifications
Architecture
Multi-stage pipeline orchestrating specialized AI services: transcription engine for diarized timestamps, LLM for semantic analysis and translation, and neural TTS for voice synthesis. Each stage operates independently, enabling modular upgrades.
Integrations
Native DaVinci Resolve plugin for in-editor workflow. CSV import/export for interoperability with human review workflows. API-based architecture supports integration with custom content management systems.
AI Services
Leverages AssemblyAI and ElevenLabs Scribe for transcription, Anthropic Claude for semantic translation and terminology management, and ElevenLabs for neural voice synthesis and voice cloning.
Security & Compliance
All content is processed through encrypted API channels. No content is stored beyond the active processing session unless explicitly configured for knowledge base preparation. Designed with healthcare data sensitivity in mind.
Deployment
Available as a managed service operated by SurgeSquare, or as a DaVinci Resolve plugin for teams with in-house post-production capabilities. Cloud-based processing with no local GPU requirements.
Multilingual Surgical Training Course
A European orthopedic training center has accumulated 80 hours of Italian-language arthroscopy lectures and live surgery commentary recorded over three years. Their upcoming international cadaver lab requires participants to arrive with foundational knowledge — but most registrants speak English, Spanish, or German. The content exists, but it is inaccessible to the majority of learners.
The training center uploads their video library to ARIA. Within hours, the platform transcribes each recording with speaker diarization — separating the lead surgeon's commentary from assistant remarks and moderator introductions. The LLM Dubbing Director analyzes the full corpus, building a consistent glossary for terms like 'artroscopia diagnostica' and 'lesione del labbro glenoideo' before generating translations in three target languages.
For the surgical commentary — where clinical precision is critical — the center's bilingual faculty reviews the translations via exported CSV files. These reviewed translations are re-imported into ARIA and synthesized without any LLM modification, preserving every approved term. Meanwhile, the general lecture content proceeds through fully automated translation, balancing speed with acceptable quality for preparatory material.
The dubbed videos are delivered with timeline-aligned audio tracks ready for DaVinci Resolve. Simultaneously, ARIA has structured all transcribed and translated content into vector-ready chunks. The training center deploys a RAG-powered chatbot that allows registrants to ask questions about the course material in their own language — arriving at the cadaver lab prepared and ready for hands-on practice, eliminating the need for repetitive theory sessions.