Build data foundations for next generation voice AI.
Voice is one of the most important interfaces for AI. Speech systems are embedded throughout global industries. Access to high quality, human validated, ethical ASR training data can be a barrier to launching your model to global users.
DATAmundi helps you build the data foundations and unified workflows required to enable real-world multimodal and speech AI across languages and dialects.
Custom speech collected across languages, cultures, and devices for ASR training. Our multimodal Off-the-Shelf datasets are also available in multiple languages, licensed, and ready-to-use.
Combining proprietary AI transcription and human-in-the-loop for expert linguistic review.
Transform raw speech recordings into AI-ready datasets through multilingual linguistic annotation, semantic labeling, and metadata enrichment.
Translation
Localization
Multilingual Content
Quality Assurance
01
Understand
We learn your content, audience, market, and brand requirements
02
Prepare
We establish terminology, references, workflows, and linguistic requirements
03
Adapt
Expert linguists translate, localize, or create content for your target markets
04
Quality Assurance
Linguistic, functional, & cultural quality checks ensure everything is ready
From Speech Recognition to Speech Reasoning
Building the Data Foundations for the Next Generation of Voice AI
300k vetted AI contributors located across the globe. 30k domain experts for healthcare, finance, legal, technology (and more) AI use cases.
Combining AI-powered transcription with structured human validation to transform complex audio into high-quality, structured speech data. Seamlessly integrating human-in-the-loop workflows to deliver reliable, scalable training data for ASR models.
Our validation frameworks, expert oversight, and AI driven analytics produce the most accurate, fair, and reliable datasets.
Proprietary AI data engine efficiency manages multimodal workflows for the entire data lifecycle. Secure, ethical data workflows.
ASR-ready labelling. Speaker diarization, acoustic event tagging <laughter>, segment-level and file-level metadata.
Consent-based collection and fully documented data provenance for licensing and compliance.
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