What is AIxBlock?

AIxBlock provides enterprise training data for speech and large language models, offering voice, audio, and text datasets across 100+ languages.

The platform supplies ready-to-license audio catalogs, hundreds of thousands of hours of real-world call center and environmental recordings, and custom collection for exclusive use.

Services include multilingual collection, transcription, annotation, professional or natural-speaker recordings, and sound/environment capture for audio classification and noise detection.

Text and dialogue offerings cover conversation annotation, intent/entity labeling, RLHF preference data, and fine-tuning datasets for LLM training.

A self-hosted deployment option and storage connectors enable data sovereignty by keeping customer data on their infrastructure and preventing platform retention of collected data.

Enterprise-scale language coverage and integration options support ML engineers, data scientists, and AI teams building voice AI and LLM applications.

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AIxBlock's key features

Self-hosted AI development platform (data engine, training, GPU marketplace) deployable on customer infrastructure with direct storage connection and no provider data retention
Multilingual data collection and annotation across 100+ languages
End-to-end speech/audio pipeline: collection, transcription, and annotation with professional voice talent and natural speakers across accents
Real-world sound and environment audio collection (environmental sounds, background/machine noise, acoustic scenes) for audio classification and recognition
Text and dialogue data services: conversation annotation, intent/entity labeling, RLHF preference data, and LLM fine-tuning datasets

AIxBlock use cases

Create high-accuracy multilingual speech recognition systems for global customer support using AIxBlock's licensed catalogs and custom call-center recordings across 100+ languages, combined with transcription and annotation services to capture domain-specific vocabulary
Build enterprise-grade conversational agents and RLHF-tuned chatbots using AIxBlock's dialogue and preference datasets and fine-tuning language collections, while leveraging self-hosted storage for data sovereignty and compliance
Develop noise-robust audio understanding for IoT, environmental monitoring, and safety systems by training models on AIxBlock's environmental audio recordings and annotated datasets, accelerating model training and deployment with curated fine-tuning datasets