Keyword Intelligence Subnet (name subject to change) is a decentralized SEO intelligence network on Bittensor that uses competitively scored miners to estimate search volume, CPM, keyword difficulty, and target keywords for websites through consensus-driven statistical modeling.
Miners are tasked with analyzing websites and web pages to extract keyword data, including volume, cost-per-mille (CPM), keyword difficulty, and relevance scores. Miners can support different types of data without a penalty, since SEO data is broad, and not everybody can have access to everything. The goal is to let miners do what they are best at.
Validators score each data type with a different approach, and to be sure that an average metric is returned for a specific keyword.
If you dealt with blogging or local SEO before, you are likely to use a keyword analysis tool to analyze your target keyword and monitor your competitors.
Tools like SEMrush, Ahrefs, and Moz charge high monthly fees because they operate massive centralized scraping and clickstream infrastructures. Small startups, indie founders, and agencies are priced out. Most individuals are exploiting the freemium plan of these tools, which single-handedly shows the fact that we lack a public and affordable tool.
That’s why KIS can build a new platform using miners with different skills. One miner can analyze keywords while another can calculate a TA score.
Our collaboration against their centralized clickstream data.
KIS operates as a competitive estimation market rather than a shared scraping pool. Miners are not forced to implement the same methodology. Instead, they compete on the quality, stability, and speed of their keyword estimations.
Each task type is treated independently at the validator level. A miner may specialize in one or several metrics. Performance is tracked separately per metric so that a strong CPM estimator is not penalized for not supporting keyword volume estimation.
However, if a miner is specialized at one task and returns an inaccurate estimate, it will get penalized.
When a validator receives a keyword query, it sends the same task to multiple miners. Responses are aggregated using statistical filtering. Extreme outliers are removed. A consensus value is computed using a median or trimmed mean. Each miner is then scored based on how close its estimate is to that consensus value. Over time, validators maintain historical performance records to detect instability, randomness, or manipulation patterns.
Speed is also factored into scoring, but it is weighted carefully to avoid incentivizing low-quality fast responses. Stability across repeated queries matters more than one-off precision. A miner that fluctuates heavily for identical inputs will gradually lose weight.
Primary purpose of a KIS miner is returning keywords from a web page, and a TA score as an output. The rest of the tasks are extra points for a miner because, without finding the primary keyword of a web page or a website, there is no point in analyzing it.
The workflow of how a simple miner works:
Workflow of a miner
An example request miner receives would be a JSON data like this: