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| 1h | 24h | 7d | 30d | 60d | 90d |
|---|---|---|---|---|---|
| 0.13% | 0.36% | 0.89% | 35.56% | 43.97% | -- |
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Data source: CoinGecko
SN33 is the token of the ReadyAI subnet, used for compute incentives, staking, and governance of data-labeling and AI-data-market activities, and is released per the emission mechanism.
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About ReadyAI
ReadyAI is a marketplace for agentic data that also runs as subnet 33 (SN33) on Bittensor. It turns unstructured web content into machine-readable structured intelligence through three access paths: Domain Intelligence, which produces structured summaries of more than 10,000 web sources that are searchable, queryable via MCP or downloadable; Transcript Intelligence, which processes more than 5,000 technical podcasts into training-ready datasets; and Custom Enrichment, private pipelines that process a customer's own data at scale.
Delivery takes three forms - an MCP server, HuggingFace datasets and a custom API - over a four-step flow of sourcing raw data such as web content, transcripts, documentation and filings, AI-powered extraction and enrichment, structured output with quality scores, and channel delivery. The project claims its custom enrichment runs 86% more accurate than human annotation at roughly one six-hundred-and-sixtieth of the cost, and lists Ipsos, Gelt and Archway among enterprise customers, with HuggingFace downloads above 300,000.
In the week of May 18, 2026, ReadyAI (Bittensor Subnet 33) rolled out x402 payment rails, moving enterprise data calls to USDC settlement and reopening its Jobs API to outside users. Paying customers include NYSE-listed SmartStop Self Storage REIT, a $1.8 billion company with more than 460 properties, plus Concord Communities, Gelt and Archway, each with over $1 billion in assets under management.
That month ReadyAI also launched a public revenue dashboard and committed 75% of enrichment pipeline revenue to buying back SN33 alpha on the open market.
ReadyAI will shift emphasis from real estate text to coding intelligence datasets, serving coding agents with version-difference data, and pursue agent-to-agent purchasing by benchmarking quality first and selling data second.