Subnet Directory

With the amount of new subnets being added it can be hard to get up to date information across all subnets, so data may be slightly out of date from time to time

Subnet 33

ReadyAI

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ABOUT

What exactly does it do?

ReadyAI provides creators and businesses with cost-effective, time-saving tools to build the most accurate AI applications for their audience. Built on the Bittensor network, they’ve transformed data tagging and structuring for text data, significantly improving AI accuracy through metadata and synthetic data generation for vector databases. These databases give you control over your data while leveraging top-tier models like OpenAI, Anthropic, and Meta.

ReadyAI offers more precise metadata tagging at a lower cost than competitors like Scale AI, which rely on expensive, less reliable human labor. Their use of Bittensor’s decentralized validation ensures higher quality control over metadata, and they implement a privacy-preserving system for processing both public and proprietary data. As they continue to evolve, ReadyAI is committed to protecting data privacy, fostering collaboration in AI research, and advancing their dataset to meet the growing demands of conversational AI, helping create seamless, human-like interactions for emotional support, personalized learning, and beyond.

Afterparty, the team behind ReadyAI, aims to enable creators to fully own and monetize their AI personas and creations, highlighting the need for creators to have true ownership of their AI platforms. Afterparty recognizes a lack of quality in open-source foundational models for conversational natural language fluency.

 

ReadyAI provides creators and businesses with cost-effective, time-saving tools to build the most accurate AI applications for their audience. Built on the Bittensor network, they’ve transformed data tagging and structuring for text data, significantly improving AI accuracy through metadata and synthetic data generation for vector databases. These databases give you control over your data while leveraging top-tier models like OpenAI, Anthropic, and Meta.

ReadyAI offers more precise metadata tagging at a lower cost than competitors like Scale AI, which rely on expensive, less reliable human labor. Their use of Bittensor’s decentralized validation ensures higher quality control over metadata, and they implement a privacy-preserving system for processing both public and proprietary data. As they continue to evolve, ReadyAI is committed to protecting data privacy, fostering collaboration in AI research, and advancing their dataset to meet the growing demands of conversational AI, helping create seamless, human-like interactions for emotional support, personalized learning, and beyond.

Afterparty, the team behind ReadyAI, aims to enable creators to fully own and monetize their AI personas and creations, highlighting the need for creators to have true ownership of their AI platforms. Afterparty recognizes a lack of quality in open-source foundational models for conversational natural language fluency.

 

PURPOSE

What exactly is the 'product/build'?

The ReadyAI subnet is designed to offer a low-cost, resource-efficient data structuring and semantic tagging pipeline for both individuals and businesses. A prime example of their data processing power is the creation of an annotated, persona-labeled dialogue dataset for conversational AI development—The ReadyAI. This dataset provides crucial resources for the open-source community to make significant strides in developing natural, engaging conversational AI through further training, finetuning, and system integration.

ReadyAI leverages Bittensor’s decentralized infrastructure to incentivize a global network of miners and validators to contribute and verify high-quality conversational data. Their innovative “fractal data mining” approach enhances data integrity by cross-referencing miner submissions with ground truth, rewarding accurate and valuable contributions.

 

Key Features

  • Indexing and tagging billions of conversations from diverse sources such as YouTube and podcasts
  • Utilizing fractal data mining and conversation windows to ensure efficient and privacy-preserving processing
  • Generating synthetic participant profiles using conversation metadata
  • Implementing an algorithm to evaluate conversation quality based on relevance, engagement, novelty, coherence, and fluency
  • Providing an open-source dataset for training and refining conversational AI models
  • Establishing an incentivized mining and validation system to ensure data contribution and maintain integrity

ReadyAI utilizes Bittensor’s infrastructure to annotate conversational data.

 

Benefits

  • Addresses the current lack of personalization in conversational AI models
  • Facilitates natural and engaging conversations tailored to individual contexts and preferences
  • Provides a comprehensive and annotated dataset for advancing conversational AI
  • Promotes contributions and innovations within the open-source community
  • Ensures data integrity through validation and scoring mechanisms

 

System Design

  • Data stores: Serve as the primary source of truth for conversation windows, participant profiles, and vector databases
  • Validator roles: Involve pulling data, generating overview metadata for grounded conversations, creating windows, and scoring submissions.  Validators have the authority to decide what data to allow or block, ensuring personal information remains confidential. Maintaining control over data protection is fundamental to the design.
  • Miner roles: Focus on processing conversation windows, contributing metadata, and applying tags.  Specific metadata tags are extracted to assess their similarity to those in the overall conversation, focusing on uniqueness to provide a deeper understanding of the conversation’s context. The conversational window safeguards against miners creating unauthorized databases with personal data. Miners have the opportunity to enhance their performance by utilizing system prompting, high-quality chaining, and fine-tuning models to improve metadata extraction. The flexibility for miners to fine-tune their models based on their sources allows for endless opportunities in the mining process.
  • Data flow: Includes establishing ground truth, creating windows, submitting miner contributions, scoring, and validation

 

Rewards and Incentives

  • Miners are rewarded for contributing accurate and valuable metadata.  The “conversation Windows” concept in Subnet divides conversations into overlapping segments, allowing miners to analyze each segment and extract detailed metadata. Miners receive a portion of the conversation to analyze, providing a more granular level of metadata tagging compared to the overall conversation tags.
  • Rewards are distributed in a balanced manner to encourage high-quality submissions. Miners’ scoring is based on matching tags with the overall conversation and generating unique tags close to the semantic neighborhood of the conversation. Validators compare miners’ tags within the same conversation window to ensure relevance and uniqueness in the metadata tagging process.
  • Cross-referencing and vector embedding analysis are used to ensure data integrity.
  • An algorithm is in place for assessing conversation quality, though it is not yet used for miner rewards.

 

The ReadyAI subnet is designed to offer a low-cost, resource-efficient data structuring and semantic tagging pipeline for both individuals and businesses. A prime example of their data processing power is the creation of an annotated, persona-labeled dialogue dataset for conversational AI development—The ReadyAI. This dataset provides crucial resources for the open-source community to make significant strides in developing natural, engaging conversational AI through further training, finetuning, and system integration.

ReadyAI leverages Bittensor’s decentralized infrastructure to incentivize a global network of miners and validators to contribute and verify high-quality conversational data. Their innovative “fractal data mining” approach enhances data integrity by cross-referencing miner submissions with ground truth, rewarding accurate and valuable contributions.

 

Key Features

  • Indexing and tagging billions of conversations from diverse sources such as YouTube and podcasts
  • Utilizing fractal data mining and conversation windows to ensure efficient and privacy-preserving processing
  • Generating synthetic participant profiles using conversation metadata
  • Implementing an algorithm to evaluate conversation quality based on relevance, engagement, novelty, coherence, and fluency
  • Providing an open-source dataset for training and refining conversational AI models
  • Establishing an incentivized mining and validation system to ensure data contribution and maintain integrity

ReadyAI utilizes Bittensor’s infrastructure to annotate conversational data.

 

Benefits

  • Addresses the current lack of personalization in conversational AI models
  • Facilitates natural and engaging conversations tailored to individual contexts and preferences
  • Provides a comprehensive and annotated dataset for advancing conversational AI
  • Promotes contributions and innovations within the open-source community
  • Ensures data integrity through validation and scoring mechanisms

 

System Design

  • Data stores: Serve as the primary source of truth for conversation windows, participant profiles, and vector databases
  • Validator roles: Involve pulling data, generating overview metadata for grounded conversations, creating windows, and scoring submissions.  Validators have the authority to decide what data to allow or block, ensuring personal information remains confidential. Maintaining control over data protection is fundamental to the design.
  • Miner roles: Focus on processing conversation windows, contributing metadata, and applying tags.  Specific metadata tags are extracted to assess their similarity to those in the overall conversation, focusing on uniqueness to provide a deeper understanding of the conversation’s context. The conversational window safeguards against miners creating unauthorized databases with personal data. Miners have the opportunity to enhance their performance by utilizing system prompting, high-quality chaining, and fine-tuning models to improve metadata extraction. The flexibility for miners to fine-tune their models based on their sources allows for endless opportunities in the mining process.
  • Data flow: Includes establishing ground truth, creating windows, submitting miner contributions, scoring, and validation

 

Rewards and Incentives

  • Miners are rewarded for contributing accurate and valuable metadata.  The “conversation Windows” concept in Subnet divides conversations into overlapping segments, allowing miners to analyze each segment and extract detailed metadata. Miners receive a portion of the conversation to analyze, providing a more granular level of metadata tagging compared to the overall conversation tags.
  • Rewards are distributed in a balanced manner to encourage high-quality submissions. Miners’ scoring is based on matching tags with the overall conversation and generating unique tags close to the semantic neighborhood of the conversation. Validators compare miners’ tags within the same conversation window to ensure relevance and uniqueness in the metadata tagging process.
  • Cross-referencing and vector embedding analysis are used to ensure data integrity.
  • An algorithm is in place for assessing conversation quality, though it is not yet used for miner rewards.

 

WHO

Team Info

The Afterparty team consists of individuals with backgrounds in AI, technology, and creator marketing, showcasing a diverse range of expertise to disrupt the creator economy.

David Fields – Co-Founder

Eytan Elbaz – Co-Founder

Robert Graham – Co-Founder and Chief Talent Officer

Dan Rahmel – Co-Founder

John Van Liere – Product Lead

Matthew Dusette – Product Operations Manager

The Afterparty team consists of individuals with backgrounds in AI, technology, and creator marketing, showcasing a diverse range of expertise to disrupt the creator economy.

David Fields – Co-Founder

Eytan Elbaz – Co-Founder

Robert Graham – Co-Founder and Chief Talent Officer

Dan Rahmel – Co-Founder

John Van Liere – Product Lead

Matthew Dusette – Product Operations Manager

FUTURE

Roadmap

ReadyAI aims to revolutionize personalized conversational AI by providing a comprehensive, annotated dataset of global conversations. Leveraging a robust system design, fractal data mining, and a carefully crafted rewards model, the CGP enables the open-source community to develop more engaging and context-aware AI models while maintaining data integrity and resilience.

The database’s real intention is to categorize and index all conversations globally, allowing model makers to access structured data for more robust AI development. The goal involves continually structuring data to provide a wide range of applications beyond character experiences, shaping the future of conversational AI.

ReadyAI aims to revolutionize personalized conversational AI by providing a comprehensive, annotated dataset of global conversations. Leveraging a robust system design, fractal data mining, and a carefully crafted rewards model, the CGP enables the open-source community to develop more engaging and context-aware AI models while maintaining data integrity and resilience.

The database’s real intention is to categorize and index all conversations globally, allowing model makers to access structured data for more robust AI development. The goal involves continually structuring data to provide a wide range of applications beyond character experiences, shaping the future of conversational AI.

MEDIA

A special thanks to Mark Jeffrey for his amazing Hash Rate series! In this series, he provides valuable insights into Bittensor Subnets and the world of decentralized AI. Be sure to check out the full series on his YouTube channel for more expert analysis and deep dives.

This session from late 2024 features an in-depth discussion with David Fields of Ready AI, where he explores the intersection of AI and blockchain technology, specifically within the Bittensor ecosystem. The conversation delves into how Ready AI, operating within Bittensor’s subnet 33, aims to disrupt the data annotation market by leveraging large language models (LLMs) to provide more cost-effective and accurate data structuring, comparing its impact with traditional AI companies like Scale AI. Fields also touches on the dynamic shifts occurring in decentralized AI, the significance of verification mechanisms in Bittensor’s structure, and the future of AI-powered subnets. The session highlights the potential for Bittensor to address the critical challenge of making AI work not just computationally, but usefully, pushing the boundaries of what decentralized systems can offer to AI development.

Novelty Search is great, but for most investors trying to understand Bittensor, the technical depth is a wall, not a bridge. If we’re going to attract investment into this ecosystem then we need more people to understand it! That’s why Siam Kidd and Mark Creaser from DSV Fund have launched Revenue Search, where they ask the simple questions that investors want to know the answers to.

In this June 2025 session of Revenue Search, David Fields from SN33 (Ready AI) discusses the intersection of AI, decentralization, and data annotation. Ready AI is working to decentralize the data curation process traditionally done by human annotators, aiming to make it faster, cheaper, and more accurate through a decentralized network of miners using AI models. David explains how they are processing various types of data, including conversational, social media, and podcast data, for AI model training. He also highlights how this model is already being applied in enterprise solutions, including Brand Radar, a product that provides real-time data insights for businesses. As the conversation unfolds, David touches on the challenges of scaling data annotation, the impact of decentralization, and the growing demand for more efficient and secure AI model training. He also reflects on the competition in the field, particularly following the acquisition of Scale AI by Meta, and Ready AI’s approach to fulfilling the growing enterprise demand for structured, decentralized data solutions.

 

A big thank you to Tao Stats for producing these insightful videos in the Novelty Search series. We appreciate the opportunity to dive deep into the groundbreaking work being done by Subnets within Bittensor! Check out some of their other videos HERE.

In this session, David and Dan from Ready AI present Subnet 33, which structures unstructured data—like Common Crawl, social media, and conversational text—into richly tagged, metadata-enhanced formats optimized for AI agents and LLMs. They describe the evolution from raw web data to “n-dimensional” structured data that enables precise, real-time AI inference, particularly in the emerging MCP (Modular Cognitive Processing) ecosystem. Subnet 33 leverages Bittensor’s incentive system to crowdsource annotation tasks via LLM miners and validators, now including GAN-style architectures with time constraints. They’ve launched enterprise APIs, secured six major pilot partners, begun monetization, and started processing Common Crawl to build what they describe as the world’s most useful structured dataset for AI.

NEWS

Announcements

ReadyAI Follow 190 3,241

Structuring the world's data, making it universally accessible to AI Data labelling at scale — 51 % more accurate than GPT-4o Bittensor Subnet 3️⃣3️⃣ 🌐

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