What is DataHive?

DataHive is a decentralized data factory for AI — a platform that collects, cleans, and labels real-world web data at scale, delivering it ready for model training.

What kind of datasets can DataHive deliver?

We provide large-scale, domain-specific datasets such as: text, image, video, and audio, сollected from real-world sources. For example: 

• E-commerce: product details, reviews, and pricing data from sites like Amazon or Walmart
• Video: TikTok and YouTube datasets for multimodal and generative models
• Audio: speech samples, podcasts, and user-generated audio data for voice and LLM fine-tuning
• Real Estate: Millions of media files related to residential and commercial properties including images, panoramas, floor plans 
• Q&A / Knowledge: structured data from platforms like structured data from specialized in programming, system administrations, etc. 
• Custom domains: upon request, we build datasets tailored to your model requirements

How is DataHive different from other data providers?

Most current providers rely on centralized crawling or manual scraping, both limited in scale and costly to maintain. DataHive’s distributed model offers:

• Scalability: no central bottlenecks, easy to scale across geographies
• Lower cost: decentralized infrastructure cuts dataset costs by 10–20x
• Dynamic content: capable of accessing JavaScript-rendered or infinite-scroll data that traditional crawlers miss
• Ethical and compliant sourcing: we collect only from vetted websites and publicly accessible sources, ensuring legal safety for enterprise clients

In short: we deliver hard-to-get web data, ethically and efficiently.

What is the process to get a dataset?

We start by understanding your model’s needs: the domain, structure, and scale of data required. Then we deliver a free pilot dataset to validate quality and structure. Once confirmed, full-scale collection begins, with options for ongoing updates and labeling pipelines integrated directly into your ML workflow. The dataset will be delivered either in an industry-standard format or in a custom format tailored to your specific requirements. 

How do you ensure data quality?

Every dataset goes through multi-step validation:

• Collection filtering: removing duplicates, irrelevant pages, or low-quality content.
• Cleaning and normalization: ensuring consistent structure and metadata.
• Human-in-the-loop labeling: distributed annotators verify and label complex data.
• Benchmarking: internal testing against client-specified metrics before delivery.

What’s the business model?

We offer datasets as a service: priced by scale, complexity, and labeling requirements.
Clients can choose from:

• Pre-collected datasets (ready-to-train and already validated)
• Custom dataset collection (based on domain requests)
• Labeling-only services for in-house data teams

Our decentralized model allows cost savings that we pass directly to clients enabling enterprise-grade data at startup-friendly prices.

Who’s behind DataHive?

The DataHive team previously founded Profitero, big data for eCommerce company that processed 400–600 TB of data daily and was acquired by Publicis Group for $210M.

We’ve raised $3.5M from top-tier investors including 6th Man Ventures, Solana Ventures, and Wave GP, and we’re now focused on building the world’s most efficient decentralized data infrastructure for AI.

How can my company get started?

Simply reach out via datahive.ai or request your free pilot dataset. We’ll help you identify the right data domain, validate quality, and integrate it into your model pipeline.