AlxBlock Build-AI

Auto Annotation

Optimize your AI development time with
AIxBlock's robust tool that automatically labels data. It taps into pre-trained models marketplace to effortlessly label data across various AI domains, such as NLP, CV, ASR, LLM.

Multimodal-Powered Data Crawler

Unleash data from diverse sources
Collect diverse formats including traffic
camera footage
Intelligently contextualizeS data requirements

Consensus-driven Labeling Built On Chain

Utilize crowdsourced human feedback and consensus-driven processes for model reinforcement learning, or QA or labeling, ensuring accuracy, transparency, and scalability on-chain.

Supported Annotation Types

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Low-Code MLOps Platform

Your one-stop shop for AI building and monitoring, offering flexible
infrastructure setup for seamless scalability and performance

Auto and Distributed Training

Unlock unparalleled efficiency with our auto and distributed training feature, harnessing the power of parallel computing

Complete Control with
self-hosting option

Take full control by self-hosting our comprehensive platform on your own infrastructure, ensure autonomy and customization of your computation. The whole process is automatic, zero manual setup and no initial setup fees.

Fractional Computation Cost

Or dramatically reduce costs by accessing our P2P decentralized compute marketplace, renting the power of your choice.

Seamless Integration and Dynamic Scaling

Fully equipped to support your growth, our platform seamlessly integrates with essential MLOps tools such as Docker, Kubernetes, JP Notebook, and provides robust environments for both demos and deployments.

Monitor AI

Consensus-driven Labeling Built On Chain

Address the critical need for ongoing, real-time human involvement in AI model quality assurance with our On-Chain Consensus-Driven Live Model Validation.

Smooth Process

Onboarding a diverse, globally distributed crowd in real-time for AI model monitoring.

Blockchain Boost

Blockchain's decentralized nature adds security, transparency, and trustworthiness.

Consensus mechanism

Validation tasks are randomly assigned to verified contributors. The final result is based on consensus.