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of new subnets being added it can be hard to get up to date information across all subnets, so data may be slightly out of date from time to time
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of new subnets being added it can be hard to get up to date information across all subnets, so data may be slightly out of date from time to time
Leadpoet (Subnet 71 on Bittensor) is a decentralized AI sales intelligence platform that autonomously finds and qualifies potential customers for businesses. In essence, it deploys “AI sales agents” that scour data to identify high-quality sales leads – those prospects who are fast, qualified, and ready to buy. These autonomous agents leverage Bittensor’s network of miners and validators to perform intensive data mining and AI analysis, creating a “sales engine” that hunts down prospects with high purchase intent and verifies their suitability as leads. By using Bittensor’s decentralized compute, Leadpoet’s AI agents can rapidly sift through vast data sources and signal when a target customer is likely interested in a product or service.
This approach transforms marketing spend into tangible sales pipeline by focusing on lead quality and intent rather than quantity. Unlike traditional lead generation platforms, Leadpoet is described as a full “decentralized sales intelligence engine”, not just a contact list provider. It aims to dramatically improve conversion rates by delivering only premium, high-intent prospects that are more likely to convert to customers. All of this is done autonomously: once a business provides its ideal customer profile or parameters, the AI agents can continuously source and qualify new leads without constant human oversight.
Under the hood, Bittensor’s open AI network is what powers Leadpoet’s agents. Independent miners on the subnet contribute machine learning models and compute resources to analyze data and score leads, and in return they earn Bittensor’s TAO rewards when their contributions result in useful outputs (i.e. good leads). This decentralized architecture means Leadpoet can scale its intelligence by tapping into a global pool of AI miners, rather than running on a closed, centralized model. The community has noted the subnet’s impressive scalability and real-world applicability, as it’s built to handle potentially millions of user queries and integrate with production business workflows. In summary, Leadpoet provides an AI-driven “hunter” for sales teams – continuously finding, vetting, and delivering the next likely customer, using a network of decentralized AI to do so efficiently.
Leadpoet (Subnet 71 on Bittensor) is a decentralized AI sales intelligence platform that autonomously finds and qualifies potential customers for businesses. In essence, it deploys “AI sales agents” that scour data to identify high-quality sales leads – those prospects who are fast, qualified, and ready to buy. These autonomous agents leverage Bittensor’s network of miners and validators to perform intensive data mining and AI analysis, creating a “sales engine” that hunts down prospects with high purchase intent and verifies their suitability as leads. By using Bittensor’s decentralized compute, Leadpoet’s AI agents can rapidly sift through vast data sources and signal when a target customer is likely interested in a product or service.
This approach transforms marketing spend into tangible sales pipeline by focusing on lead quality and intent rather than quantity. Unlike traditional lead generation platforms, Leadpoet is described as a full “decentralized sales intelligence engine”, not just a contact list provider. It aims to dramatically improve conversion rates by delivering only premium, high-intent prospects that are more likely to convert to customers. All of this is done autonomously: once a business provides its ideal customer profile or parameters, the AI agents can continuously source and qualify new leads without constant human oversight.
Under the hood, Bittensor’s open AI network is what powers Leadpoet’s agents. Independent miners on the subnet contribute machine learning models and compute resources to analyze data and score leads, and in return they earn Bittensor’s TAO rewards when their contributions result in useful outputs (i.e. good leads). This decentralized architecture means Leadpoet can scale its intelligence by tapping into a global pool of AI miners, rather than running on a closed, centralized model. The community has noted the subnet’s impressive scalability and real-world applicability, as it’s built to handle potentially millions of user queries and integrate with production business workflows. In summary, Leadpoet provides an AI-driven “hunter” for sales teams – continuously finding, vetting, and delivering the next likely customer, using a network of decentralized AI to do so efficiently.
The Leadpoet product is essentially a service that delivers qualified sales leads to businesses via an AI-powered platform. The team is building a user-facing application (likely a web platform or API) where a sales or marketing team can input their target customer criteria and then receive a curated list of prospects that match those criteria. Behind the scenes, the platform runs on a network of AI “sales agents” distributed across Bittensor’s subnet. These agents perform tasks like data sourcing, lead identification, intent scoring, and contact info verification in an automated pipeline. The end product for the user is a stream of “AI-sourced” sales prospects delivered in minutes – a process that would normally take human sales development reps days of research. By automating this pipeline, Leadpoet’s product helps businesses zero in on their ideal audience with precision, boosting conversion rates and saving time compared to traditional prospecting methods.
From a technical perspective, the Leadpoet build involves smart coordination of decentralized AI workers. The codebase for Subnet 71 was launched in October 2025, marking the go-live of Leadpoet’s infrastructure. The product likely includes modules for web crawling or database integration (to gather potential leads), natural language processing and analysis (to evaluate a prospect’s intent or fit), and possibly integration points to CRM systems (so that the leads can be exported or fed into the business’s sales pipeline seamlessly). Because it runs on Bittensor, the compute workload and model hosting are distributed – the miners on Subnet 71 run the models that do the heavy lifting for lead generation and get rewarded in TAO for their work.
Importantly, Leadpoet has a built-in economic model tied to its product usage. Every use of the Leadpoet service requires burning a unit of “Leadpoet Alpha”, which creates a deflationary token dynamic as adoption grows. In practice, this means when a client requests a batch of leads or runs an AI agent search, a certain amount of Leadpoet’s native credits (likely denominated in TAO or a subnet-specific token) is consumed. This not only provides immediate utility and demand for the token but also aligns the product’s success with the token’s value – as more businesses use the platform to get leads, more tokens are burned, increasing scarcity. This direct token utility is part of Leadpoet’s strategy to generate real revenue and value quickly. In fact, the project’s goal is to begin earning revenue almost immediately by selling lead-generation services, rather than just issuing tokens for speculative purposes. The product is therefore designed from day one to be commercially viable – it targets a huge existing market (business sales leads) and charges for each high-quality lead delivered, using blockchain tokens as the payment mechanism under the hood.
In summary, Leadpoet’s build is a combination of a decentralized AI backend (the Bittensor subnet with miners running the AI agents) and a practical SaaS-like frontend delivering sales leads to end users. The deliverable “product” is qualified lead data (potentially including contact names, titles, emails, company info, and an AI-generated relevance score). Because it’s powered by collective AI, the product should continually improve – for example, the intent scoring algorithms can learn from outcomes (e.g., whether delivered leads converted or not) and the system can refine future recommendations. This makes Leadpoet not just a static lead list provider, but an intelligent, learning sales assistant network that becomes more effective over time.
The Leadpoet product is essentially a service that delivers qualified sales leads to businesses via an AI-powered platform. The team is building a user-facing application (likely a web platform or API) where a sales or marketing team can input their target customer criteria and then receive a curated list of prospects that match those criteria. Behind the scenes, the platform runs on a network of AI “sales agents” distributed across Bittensor’s subnet. These agents perform tasks like data sourcing, lead identification, intent scoring, and contact info verification in an automated pipeline. The end product for the user is a stream of “AI-sourced” sales prospects delivered in minutes – a process that would normally take human sales development reps days of research. By automating this pipeline, Leadpoet’s product helps businesses zero in on their ideal audience with precision, boosting conversion rates and saving time compared to traditional prospecting methods.
From a technical perspective, the Leadpoet build involves smart coordination of decentralized AI workers. The codebase for Subnet 71 was launched in October 2025, marking the go-live of Leadpoet’s infrastructure. The product likely includes modules for web crawling or database integration (to gather potential leads), natural language processing and analysis (to evaluate a prospect’s intent or fit), and possibly integration points to CRM systems (so that the leads can be exported or fed into the business’s sales pipeline seamlessly). Because it runs on Bittensor, the compute workload and model hosting are distributed – the miners on Subnet 71 run the models that do the heavy lifting for lead generation and get rewarded in TAO for their work.
Importantly, Leadpoet has a built-in economic model tied to its product usage. Every use of the Leadpoet service requires burning a unit of “Leadpoet Alpha”, which creates a deflationary token dynamic as adoption grows. In practice, this means when a client requests a batch of leads or runs an AI agent search, a certain amount of Leadpoet’s native credits (likely denominated in TAO or a subnet-specific token) is consumed. This not only provides immediate utility and demand for the token but also aligns the product’s success with the token’s value – as more businesses use the platform to get leads, more tokens are burned, increasing scarcity. This direct token utility is part of Leadpoet’s strategy to generate real revenue and value quickly. In fact, the project’s goal is to begin earning revenue almost immediately by selling lead-generation services, rather than just issuing tokens for speculative purposes. The product is therefore designed from day one to be commercially viable – it targets a huge existing market (business sales leads) and charges for each high-quality lead delivered, using blockchain tokens as the payment mechanism under the hood.
In summary, Leadpoet’s build is a combination of a decentralized AI backend (the Bittensor subnet with miners running the AI agents) and a practical SaaS-like frontend delivering sales leads to end users. The deliverable “product” is qualified lead data (potentially including contact names, titles, emails, company info, and an AI-generated relevance score). Because it’s powered by collective AI, the product should continually improve – for example, the intent scoring algorithms can learn from outcomes (e.g., whether delivered leads converted or not) and the system can refine future recommendations. This makes Leadpoet not just a static lead list provider, but an intelligent, learning sales assistant network that becomes more effective over time.
Gavin Zaentz – CEO: Leadpoet is led by Gavin Zaentz, who has a background in both finance and tech. He was previously a Senior Product Manager at Nasdaq, bringing expertise in data-driven product development. He also has experience in the blockchain space – for example, he founded a startup leveraging the Helium network and has been a general partner in a blockchain-focused hedge fund (JGH Capital). Zaentz holds a Master’s in Business Analytics from Tulane University, equipping him with strong analytical and leadership skills as he drives Leadpoet’s vision and business strategy.
Pranav Ramesh – CTO: Pranav Ramesh is the technical lead for Leadpoet. He was formerly the Head of Quantitative Research at Nasdaq, indicating deep expertise in data science and algorithmic modeling. His background also includes work with cloud and AI infrastructure (including experience at Amazon Web Services) and he is an alumnus of Columbia University. As CTO, Ramesh architects the Leadpoet platform and oversees development of the AI agents and the Bittensor subnet integration. His quant research experience at Nasdaq suggests he is skilled in extracting insights from large data – a crucial ability for building an AI that filters millions of data points to find valuable leads.
Siam Kidd – Strategic Advisor (DSV Fund): Siam Kidd is an advisor to Leadpoet, representing the project’s key incubator, the DSV Fund. Siam Kidd is a well-known figure in the Bittensor community (CIO of DSV) and has joined Leadpoet to provide guidance on both strategy and network integration. With his experience in decentralized AI ventures, he helps the team navigate growth in the Bittensor ecosystem and align the product with market needs.
Mark Creaser – Strategic Advisor (DSV Fund): Mark Creaser, another principal of DSV Fund, is also a strategic advisor to Leadpoet. DSV Fund (a venture fund focused on Bittensor projects) not only invested in Leadpoet but actively supports it through mentorship. Mark Creaser has been instrumental in setting up the subnet’s operations and commercial strategy. Both Mark and Siam joined as advisors as part of DSV’s incubation deal, helping the team with launching and scaling one of Bittensor’s first commercially-focused subnets.
Gavin Zaentz – CEO: Leadpoet is led by Gavin Zaentz, who has a background in both finance and tech. He was previously a Senior Product Manager at Nasdaq, bringing expertise in data-driven product development. He also has experience in the blockchain space – for example, he founded a startup leveraging the Helium network and has been a general partner in a blockchain-focused hedge fund (JGH Capital). Zaentz holds a Master’s in Business Analytics from Tulane University, equipping him with strong analytical and leadership skills as he drives Leadpoet’s vision and business strategy.
Pranav Ramesh – CTO: Pranav Ramesh is the technical lead for Leadpoet. He was formerly the Head of Quantitative Research at Nasdaq, indicating deep expertise in data science and algorithmic modeling. His background also includes work with cloud and AI infrastructure (including experience at Amazon Web Services) and he is an alumnus of Columbia University. As CTO, Ramesh architects the Leadpoet platform and oversees development of the AI agents and the Bittensor subnet integration. His quant research experience at Nasdaq suggests he is skilled in extracting insights from large data – a crucial ability for building an AI that filters millions of data points to find valuable leads.
Siam Kidd – Strategic Advisor (DSV Fund): Siam Kidd is an advisor to Leadpoet, representing the project’s key incubator, the DSV Fund. Siam Kidd is a well-known figure in the Bittensor community (CIO of DSV) and has joined Leadpoet to provide guidance on both strategy and network integration. With his experience in decentralized AI ventures, he helps the team navigate growth in the Bittensor ecosystem and align the product with market needs.
Mark Creaser – Strategic Advisor (DSV Fund): Mark Creaser, another principal of DSV Fund, is also a strategic advisor to Leadpoet. DSV Fund (a venture fund focused on Bittensor projects) not only invested in Leadpoet but actively supports it through mentorship. Mark Creaser has been instrumental in setting up the subnet’s operations and commercial strategy. Both Mark and Siam joined as advisors as part of DSV’s incubation deal, helping the team with launching and scaling one of Bittensor’s first commercially-focused subnets.
Leadpoet’s roadmap is aggressive and focused on rapid execution in the first few months after launch. Key milestones include:
Month 1 – Launch and Quality Optimization: In the first month of operation, the priority is to launch the platform and optimize lead sourcing to ensure the AI agents are delivering high-quality leads. This involves fine-tuning the data sources and filters so that the output prospects truly match the target profiles and have strong buying intent. Early user feedback and results will be used to calibrate the system’s precision.
Month 2 – Refining Intent Scoring and Curation: The second month is focused on improving the AI’s decision-making. The team will refine the intent scoring algorithms and curation process. This means making the AI better at discerning how interested a prospect likely is (for example, detecting signals that indicate a prospect is “in-market” or actively searching for a solution). It also means curating the lead outputs, perhaps by ranking or categorizing leads so that end-users can prioritize outreach (e.g. “hot” leads versus warm leads). Technical improvements to the models and possibly incorporating more data points occur during this phase to boost lead relevance.
Month 3 – Paid Launch and First Revenue: By the third month, Leadpoet plans to roll out paid access to the service and secure its first paying customers. Having proven the concept and quality in the early weeks, the platform will transition from any beta or free trial phase into a revenue-generating product. This involves activating the token-burning payment mechanism for real users and likely onboarding initial clients who will pay (in TAO or fiat via the token system) for lead packs or subscriptions. Hitting “real revenue within the first three months” is a stated goal, and success by Month 3 would validate the commercial viability of Subnet 71.
Beyond the initial 3-month sprint, the roadmap will likely focus on scaling up – e.g., expanding the number of businesses using Leadpoet, broadening the data sources for leads, and continuously retraining the AI models with new feedback. While details after the third month haven’t been publicly outlined, the overarching plan is clear: quickly move from launch to revenue to growth. This means that after proving the model, Leadpoet will aim to ramp up user acquisition and possibly integrate more deeply with enterprise sales workflows (for example, integrations with CRM software or developing an API for larger customers). Ultimately, the vision is to grow Leadpoet into a significant revenue-driving subnet on Bittensor, showcasing how decentralized AI can solve real business problems and monetization can flow back into the network.
Leadpoet’s roadmap is aggressive and focused on rapid execution in the first few months after launch. Key milestones include:
Month 1 – Launch and Quality Optimization: In the first month of operation, the priority is to launch the platform and optimize lead sourcing to ensure the AI agents are delivering high-quality leads. This involves fine-tuning the data sources and filters so that the output prospects truly match the target profiles and have strong buying intent. Early user feedback and results will be used to calibrate the system’s precision.
Month 2 – Refining Intent Scoring and Curation: The second month is focused on improving the AI’s decision-making. The team will refine the intent scoring algorithms and curation process. This means making the AI better at discerning how interested a prospect likely is (for example, detecting signals that indicate a prospect is “in-market” or actively searching for a solution). It also means curating the lead outputs, perhaps by ranking or categorizing leads so that end-users can prioritize outreach (e.g. “hot” leads versus warm leads). Technical improvements to the models and possibly incorporating more data points occur during this phase to boost lead relevance.
Month 3 – Paid Launch and First Revenue: By the third month, Leadpoet plans to roll out paid access to the service and secure its first paying customers. Having proven the concept and quality in the early weeks, the platform will transition from any beta or free trial phase into a revenue-generating product. This involves activating the token-burning payment mechanism for real users and likely onboarding initial clients who will pay (in TAO or fiat via the token system) for lead packs or subscriptions. Hitting “real revenue within the first three months” is a stated goal, and success by Month 3 would validate the commercial viability of Subnet 71.
Beyond the initial 3-month sprint, the roadmap will likely focus on scaling up – e.g., expanding the number of businesses using Leadpoet, broadening the data sources for leads, and continuously retraining the AI models with new feedback. While details after the third month haven’t been publicly outlined, the overarching plan is clear: quickly move from launch to revenue to growth. This means that after proving the model, Leadpoet will aim to ramp up user acquisition and possibly integrate more deeply with enterprise sales workflows (for example, integrations with CRM software or developing an API for larger customers). Ultimately, the vision is to grow Leadpoet into a significant revenue-driving subnet on Bittensor, showcasing how decentralized AI can solve real business problems and monetization can flow back into the network.
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.
A live, on-location Revenue Search featuring LeadPoet, a Bittensor-powered subnet/product that automates outbound sales by crowdsourcing high-quality leads from miners, validating them, and selling them to clients—initially via sales agencies for scale. The model gates access by burning alpha, creating a flywheel (more demand → larger reward pool → tougher competition → better data → more demand) and plans to evolve from selling leads to booking meetings. Early pricing spans self-serve subscriptions, volume API, and enterprise deals, with strong emphasis on data quality, anti-gaming validation, and eventual conversion-based miner rewards via CRM integrations. Beta opens in December with an open-source qualification agent; early access targets January, with comparisons to Apollo highlighting fresher, re-validated data and broader coverage through permissionless miners.
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.
Recorded in June 2026, Mark Jeffrey speaks with Gavin and Prenav of Lead Poet (Subnet 71), a Bittensor-powered sales intelligence platform that uses competing miners to identify and qualify high-intent leads in real time rather than relying on static databases. The team explains how Lead Poet has evolved from simply generating large lead datasets into a live fulfillment system where each customer request becomes a competition among miners to find the most relevant prospects, verify their details and surface evidence showing why they are likely to buy. Unlike traditional providers such as Apollo or ZoomInfo, Lead Poet deliberately positions itself as a premium product, charging more per lead but aiming to eliminate much of the manual research and qualification work normally performed by sales development teams. Gavin also reveals a broader ambition to build a specialized “sales brain” — a state-of-the-art sales LLM trained from the reasoning, intent signals and adversarial research produced by miners — which could eventually be licensed to competing sales platforms such as Salesforce, HubSpot, Apollo and ZoomInfo. They discuss an early paid enterprise pilot that is already expanding internally through referrals, the use of LLM judging to score the quality and intent behind leads, and a forthcoming mining system designed to lower barriers for agentic miners by letting AI agents identify weaknesses, run research loops and improve the sales model. The conversation also explores the wider trend toward one-click, AI-driven Bittensor mining and how reducing technical friction could dramatically expand participation across the ecosystem.
Big shoutout to Gordon Frayne for his incredible work on his TAO Pill podcast! His deep dives into Bittensor Subnets break down the decentralized AI landscape like few others can. Head over to his YouTube channel to catch the full series for top-tier analysis and insights.
Recorded in May 2026. Gordon Frayne speaks with Gavin Zay, leader of Lead Poet, Bittensor Subnet 71, about the project’s evolution from building a large lead database towards a real-time sales intelligence network where miners compete to fulfil specific customer requests with the highest-quality prospects. Gavin explains that traditional platforms such as ZoomInfo and Apollo rely heavily on static databases that sales teams must manually filter and qualify, whereas Lead Poet uses Bittensor miners to dynamically source leads alongside intent signals such as fundraising, hiring activity, technology usage, PR, social media activity and other indicators that suggest a company may genuinely be in-market for a particular product or service. The new fulfillment incentive mechanism turns each customer request into a competition, with miners rewarded only when their leads are accurate, properly sourced and rank among the strongest submissions, while a separate model competition is intended to continually improve the baseline tools miners can build upon. The conversation also explores plans to require alpha on the backend to access fulfilled leads while keeping the customer experience entirely fiat-based, creating a direct link between demand for Lead Poet’s product and Subnet 71 alpha utility without exposing non-crypto customers to additional friction. Gavin discusses Lead Poet’s move upmarket towards enterprise and high-ticket sales teams, where individual contracts can be worth hundreds of thousands or millions of dollars and better intelligence on a relatively small number of prospects can therefore be extremely valuable. They also cover experiments with outcome-based pricing, where Lead Poet could share in the upside when a sourced lead converts, alongside plans for continuous intent monitoring and an outreach co-pilot that learns which messaging produces responses, meetings and sales. The broader vision is to build a full sales intelligence layer that continuously identifies the right accounts, monitors when buying signals emerge and helps sales teams engage them at the right moment, while using Bittensor’s global competition to continuously improve the quality of the underlying intelligence.
For teams evaluating agentic sales workflows, the core question is simple: can your agent source qualified leads?
Leadpoet makes that capability available to any agent.
Really enjoyed speaking with the Beyond Finance community.
@LouiseBeattie put together a great recap of the conversation and what we’re building at Leadpoet 👇
Proud to have won Best Product at the @OKX Genesis Hackathon, ranking #1 out of thousands of submissions.
We’re building best-in-class sales intelligence to help teams source and qualify the companies most likely to buy. Through our partnership with OKX, we were able to bring
The strongest agents do more than demo. They solve real workflows, create clear user value, and feel ready to scale.
Meet our winners for Best Product 🧵👇
Enjoyed sitting down with High Ticket AI Systems to talk about the state of go-to-market, how AI is transforming it, and what’s coming next for Leadpoet.
Full podcast 👇
Glad to be sponsoring @ExploitSummit.
Open discourse and the exchange of ideas are what push Bittensor forward. Events like this are where that happens.
Don’t miss it.
Most subnets talk about product-market fit. @LeadpoetAI went and got it.
Enterprises using the product within the first quarter after launch.
Shoutout to this silver sponsor of Exploit 2026.
Sept 28–29 · Montréal: https://luma.com/exploitsummit26 - ticket prices increase on August 10th
Was great speaking with @JesusMartinez.
Had a great time discussing @LeadpoetAI’s recent traction and where we’re headed.
Listen below 👇
