AIxBlock

Description

AIxBlock provides enterprise training data for speech and large language models, covering speech collection, transcription, dialogue annotation, RLHF-style feedback, and off-the-shelf call center audio datasets. Teams use this data to train, fine-tune, and evaluate AI models with production-grade quality across 100+ languages, delivered fast by a global crowd.

Its services span speech and voice data (collection, transcription, phonetic and emotion annotation), real-world sound and environmental audio for acoustic models, text and dialogue services (conversation annotation, intent and entity labeling, RLHF preference data, LLM fine-tuning datasets), and a large off-the-shelf library of call center audio in accents such as US, India, and the Philippines. Quality is managed through multi-tier QA, consensus mechanisms, blind testing, and layered identity controls including KYC and biometric verification. A key differentiator is a self-hosted platform where clients connect their own storage from day one, so collected or labeled data flows directly to their environment and AIxBlock never retains a copy, removing resale risk for regulated sectors.

With seven years of experience serving Fortune 100 companies and unicorns, EU innovation backing, and ISO 27001 and SOC 2 Type II certifications, AIxBlock targets regulated industries like banking, healthcare, and government. Beyond data, it offers a full AI development platform combining a data engine, model training, workflow automation, and a decentralized GPU marketplace.

AIxBlock's Core Features

• Speech, audio, and text training data in 100+ languages

• Voice collection, transcription, diarization, and phonetic/emotion annotation

• Environmental and real-world sound data for acoustic models

• Text and dialogue services including RLHF preference data and LLM fine-tuning

• Large off-the-shelf call center audio library across multiple accents

• Self-hosted platform where data flows to your storage with no copies retained

• Multi-tier QA with consensus, blind testing, and KYC/biometric identity controls

• Full AI development platform with training, workflow automation, and GPU marketplace

How to use AIxBlock?

1. 1

   Define your project: Tell AIxBlock about your speech, sound, or text data needs and target languages.

2. 2

   Connect your storage: Optionally link your own storage so data is delivered directly with no copies retained.

3. 3

   Collect and annotate: A global crowd collects, transcribes, and annotates data under multi-tier quality controls.

4. 4

   Train and evaluate: Use the delivered data, or the self-hosted platform, to train, fine-tune, and evaluate your models.

AIxBlock's Use Cases

• ASR and voice AI training
• LLM fine-tuning and alignment
• Acoustic and sound models
• Regulated-sector data programs
• Custom AI development

FAQ from AIxBlock

• What does AIxBlock provide?

  It provides enterprise training data for speech and LLMs, covering speech collection, transcription, dialogue annotation, RLHF feedback, and off-the-shelf call center audio, plus a full AI development platform.

• How many languages does it support?

  AIxBlock supports speech and audio data collection and annotation across more than 100 languages and accents, powered by a global crowd.

• How does it keep my data private?

  Its self-hosted platform lets you connect your own storage from day one, so collected and labeled data flows directly to your environment and AIxBlock never keeps a copy or can resell it.

• Is it suitable for regulated industries?

  Yes. It is designed for banking, healthcare, and government, with data sovereignty, EU backing, and ISO 27001 and SOC 2 Type II certifications.

• How is data quality controlled?

  Through a multi-tier QA process with consensus benchmarks, blind testing, at least three quality roles per project, and layered identity controls including KYC and biometric verification.

• Does it offer text and dialogue data for LLMs?

  Yes. It provides RLHF-style preference data, conversation annotation, intent and entity labeling, and fine-tuning datasets for LLMs.