CheckerChain Whitepaper v2.0

CheckerChain: Next-Gen AI-powered Crypto Review Platform

ABSTRACT. Existing online review platforms are not able to solve the problem of manipulated and fake reviews. Traditional centralized review platforms do not even incentivize users for their contributions. We believe these problems are due to the lack of a consensus mechanism in review architecture. Utilizing blockchain technology will partially solve the need of transparent and tamper-proof reviews.

In this paper, we propose an AI-powered trustless review consensus mechanism (tRCM) on top of blockchain technology. This tRCM architecture enables reviewers to reach a consensus on an opinion without the need of knowing each other's opinions and completely removes the chance of fake or manipulated reviews to receive any incentives.

1. PREFACE

This protocol is experimental; can suffer from parameter changes and logical upgrades. Improved methodologies can be integrated upon rigorous revisions from both the core team and the community. This paper outlines the most up-to-date version of this protocol which is intended to be completely open-source to develop the desired review aggregator platform.

2. INTRODUCTION

There are hundreds of thousands of crypto projects, products, companies, books, movies, and events happening every day. It is a real hassle to find the best product when we have to trust the feedback and reviews from unknown or known parties without any consensus mechanism. The existing review system is completely broken and unfair as there is no trustless check system to avoid paid, promoted, or faked reviews.

Company name	Category	Per year revenue	Monthly user	Market Share
TripAdvisor	Traditional	1.81b USD	132m	3%
TrustPilot	Traditional	211m USD	75.4m	1.5%
ProductHunt	Traditional	7.5m USD	3.3m	-
Better Business Bureau	Traditional	5.5b USD	13.2m	-
Yelp	Traditional	1.33b USD	136.1m	6%
Google	Traditional	282b USD	84.5b	73%
Facebook	Traditional	134.9b USD	12.13b	3%
Revain	Web3, Crypto
Presearch	Web3, Crypto
Fakespot	Artificial Intelligence

We introduce a decentralized and trustless review protocol built on blockchain where opinion-checkings are incentivized on reaching a consensus. Millions of these users can get revenue shared with CheckerChain. Posters, Reviewers, and Influencers are rewarded based on the quality of their work.

CheckerChain is a next-gen AI-powered crypto review platform built on blockchain using trustless review consensus mechanism (tRCM). Anyone can become a "Poster" to publicly list any crypto products and anyone can nominate themselves in tRCM protocol to participate in reviewing process. Reviewers are arbitrarily selected from the nominee pool by the protocol in zero-knowledge conditions. While the tRCM protocol is utilized for reviewing only crypto related products in CheckerChain platform, there exists a "tRCM-as-a-Service (tAAS)" extension to implement tRCM across multiple categories such as movies, books, electronics, cities, restaurants, hotels, and more.

3. INTERACTION ON CHECKERCHAIN

CheckerChain uses tRCM protocol, an evolutionary upgrade of the existing review industry with a decentralized philosophy. Hence, our protocol is built on blockchain, which is the most decentralized, the most secure and the most valueable technology network.

CheckerChain operates in 3 layers with 7 user types:

Review Data Labeling (3 user types)

(a) Posters: users who list crypto products on CheckerChain (b) Reviewers: users who are nominated by tRCM protocol to write reviews (ground truths) (c) Influencers: users who interact, boost, and engage on CheckerChain

Review AI Modeling (2 user types)

(a) Miners: neurons that run/optimize AI model to predict the ground truth of reviews for a product (b) Validators: neurons that check the work of miners and give weights for reward.

Review Data Accessibility (2 user types)

(a) Viewers: users who check reviews and ratings to make decision (b) Makers: Brands, Businesses, Partners who own the product

4. tRCM ARCHITECTURE

tRCM is an acronym for trustless Review Consensus Mechanism. It is the core protocol utilized on CheckerChain to make reviews trustless.

tRCM is based on 2 assumptions for a review to hold any authentic value,

• reviews are performed in zero-knowledge proofs without any control of either the poster or the reviewer.

• honest reviewers in the protocol always establish a majority

In tRCM protocol, anyone can participate but the protocol selects the reviewers arbitrarily to review a product. Selected reviewers can only get reward for their work when their review score falls in consensus range. Closer the consensus, more the reward.

Reviewers have a higher probability to make their review closer to consensus only when they are honest. Any dishonest review by any reviewer falls outside of consensus. This generates no or least reward making dishonest reviews highly expensive to perform. This will eventually discourage such attackers from participating in the tRCM protocol.

These scores are vital parameters to derive incentives for each contribution.

• Trust Score: This is an atomic data of a product calculated from reviewer's task. It represents rating of a product in the range of 0 to 100.

• Normalized Trust Score: This is a derived data of a product calculated from Trust score to determine the impact on reward. Posters receive reward based on Normalized Trust score.

• Consensus Score: This is an atomic data of reviewer's task. It represents the quality of trust score in the range of 0 to 100.

• Profile Score: This is both an atomic and aggregated data of reviewer's performance. It represents the quality of consensus in the range of 0 to 100. Reviewers receive reward based on Profile score.

• Rating Score: This is a derived data of a product calculated as Trust score out of 5 and processed with Bayesian Average.

• Feedback Score: This is an aggregated data of reviewer's task combined with influencer's task. It represents sentiments of a product in the range of 0 to 5.

• Normalized Feedback Score: This is a derived data calculated from Feedback Score; processed with Bayesian Average.

• Sentiment Score: This is a categorical data of a product from reviewer's task. It is non-numerical in category and numerical inside a category in the range of 0 to 100.

• Ranking Score: This is a derived data of a product calculated from Rating score and Consensus score. It represents the rank of a product starting from 1 over 24h, 7 days, 30 days and All-Time ranges.

4.1 Trustless Review Process

When a product gets listed on CheckerChain, tRCM protocol enacts on 30+ parameters of 10 categories to generate 3 vital atomic scores: Trust Score of Product, Profile Score of Reviewer and Consensus Score of Review Cycle.

10 Categorical Metrics of CheckerChain for Crypto Reviews

Project (Innovation/Technology)

Userbase/Adoption

Price/Revenue/Tokenomics

Utility Value

Security

Social Presence

Partnership (collab, VCs, exchanges)

Team

Roadmap

Clarity & confidence

Let $Ri​=(ri1​,ri2​,...,ri30​)$ represent the assessment of the $𝑖$-th randomly assigned reviewer across the 30 parameters. Each $𝑟𝑖𝑗$ denotes the assessment of the $𝑗$-th parameter by the $𝑖$-th reviewer.

At the end of the assessment time limit, a consensus $C=(c1​,c2​,...,c30​)$ is derived based on the submitted values of all reviewers. Each $𝑐𝑗$ represents the consensus value for the $𝑗$-th parameter.

To quantify how closely each reviewer's assessment aligns with the consensus, a similarity measure $sim(𝑅𝑖,𝐶)$ is introduced. This measure quantifies the degree of agreement between the assessment of the $𝑖$-th reviewer and the consensus opinion. Similarity metrics such as cosine similarity and Euclidean distance have been employed for this purpose. A higher similarity indicates a stronger alignment with the consensus and thus a higher Trust Score.

A function $𝑓(sim(𝑅𝑖,𝐶))$ is utilized to map the similarity for a Trust Score within the range of 0 to 100, and involves scaling, normalization, and mathematical transformations.

$TSi​=f(sim(Ri​,C)),∀i=1,2,...,N$

Where:

• $𝑇𝑆𝑖$ is the Trust Score assigned to the $𝑖i$-th reviewer's assessment.
• $𝑁$ is the total number of randomly assigned reviewers.
• $𝑅𝑖$ represents the assessment of the $𝑖$-th reviewer.
• $𝐶$ represents the consensus derived from all reviewers' assessments.
• $sim(𝑅𝑖,𝐶)$ is the similarity measure between the $𝑖$-th reviewer's assessment and the consensus.

If any reviewer disagrees with the Trust Score assigned in the genesis round, indicating a lack of consensus among reviewers, another round of assessment is initiated. In this subsequent round, a new set of randomly assigned reviewers evaluates the product, and the process repeats until a satisfactory level of consensus is reached.

Given a reviewer's assessment vector $𝑅𝑖$ and the consensus vector $𝐶$, the Profile Score $𝑃𝑆𝑖$ for the $𝑖$-th reviewer can be calculated as:

$PSi​=1/[1+euclidean(Ri​,C)]×100$

This scales the inverse of the Euclidean distance to a range of 0 to 100, where higher values indicate closer alignment with the consensus.

We define $penaltymissi​​(t)$ as the penalty for missing tasks and $penaltyaligni​​(t)$ as the penalty for not aligning with the consensus for the $𝑖$-th reviewer at time $𝑡$ within an epoch. These two factors impact the profile scores within an epoch and are reset on a new epoch.

4.2 Blockchain Infrastructure

CheckerChain uses multiple blockchain infrastructures to store all review outputs in a decentralized, immutable and tamper-proof system. Built on blockchain, CheckerChain provides its users an absolute transparency and complete ownership.

• Inscription of Review Data (IRD) on Bitcoin

When a consensus is generated from tRCM for any product, it incorporates the review data for that cycle into the OP_RETURN field of SegWit-enabled transactions. With SegWit, the witness data, including the digital signature, is stored separately from the transaction data, resulting in a more compact representation of the review data within the OP_RETURN field. Furthermore, Taproot enhances privacy by obfuscating the spending conditions associated with the transaction, thereby safeguarding the confidentiality of review-related activities.

The process begins with encoding the review data into a format suitable for storage in the OP_RETURN field. This involves converting the structured data into a hexadecimal representation to ensure compatibility with the Bitcoin protocol.

import hashlib
import bitcoin
from bitcoin.wallet import CBitcoinAddress, CBitcoinSecret
from bitcoin.core import CTransaction, COutPoint, CTxOut, CMutableTxOut, CMutableTransaction, CScript, OP_RETURN

def serialize_review_data(data):
    # Serialize review data into a structured format
    serialized_data = serialize_to_json(data)
    return serialized_data

def construct_transaction(serialized_data, address):
    # Construct Bitcoin transaction
    txout = CMutableTxOut(0, CScript([OP_RETURN, serialized_data]))  # Embed serialized data in OP_RETURN
    tx = CMutableTransaction([txout])
    tx_hex = tx.serialize().hex()
    return tx_hex

def broadcast_transaction(tx_hex):
    # Broadcast transaction to Bitcoin network
    try:
        bitcoin.pushtx(tx_hex)
        print("Transaction broadcasted successfully.")
    except Exception as e:
        print(f"Error broadcasting transaction: {e}")

# Example usage
review_data = {
    "product_id": "Bitcoin",
    "trust_score": 99,
    "consensus": 93,
    "ratings": 4.8
}

serialized_data = serialize_review_data(review_data)
tx_hex = construct_transaction(serialized_data, "1BitcoinAddress")
broadcast_transaction(tx_hex)

Next, a new Bitcoin transaction is constructed with a custom scriptPubKey containing the OP_RETURN opcode followed by the encoded review data. This scriptPubKey specifies the conditions under which the funds can be spent, in this case, marking the output as unspendable and designating it for data storage.

Once the transaction is constructed, it is broadcast to the Bitcoin network and included in a block by miners. The review data becomes permanently recorded on the blockchain, providing an immutable and publicly accessible record.

To retrieve the review data of any product, users can query the Bitcoin blockchain using a Bitcoin node or blockchain explorer. The encoded data is then decoded from its hexadecimal representation, allowing for the review information to be accessed and analyzed.

Tokenized Asset: $CRCN

CheckerChain uses $CRCN tickered digital assets for rewarding all contributors in review-to-earn model. $CRCN is mined through subnet SN87 of Bittensor while it can be etched and managed using protocols like Runes on Bitcoin.

Runes protocol utilizes Bitcoin’s UTXO (Unspent Transaction Output) model. Every Bitcoin transaction involves consuming existing UTXOs as inputs and then creating new UTXOs as outputs. Runes protocol aims to make the creation and management of fungible tokens on Bitcoin much more streamlined. Using Runes, CheckerChain embeds $CRCN within a UTXO, marking it as a Rune and assigning specific properties to it.

Each $CRCN is identified by a Rune ID that references the block and transaction where it was created, allowing easy tracking and proof of origin.

• Inscription of Review Data (IRD) on Ethereum, EVM-compatible Chains

The Ethereum blockchain, along with other EVM-compatible chains such as Binance Smart Chain, Polygon, and Avalanche C-Chain, provides a flexible framework for implementing CheckerChain’s Inscription of Review Data (IRD). The process begins with the serialization of review data—including product identifiers, trust scores, consensus metrics, and user ratings—into a structured JSON format. To ensure compatibility with the transaction structures of these networks, this serialized data is subsequently encoded into a hexadecimal representation.

pythonCopy codeimport json

def serialize_review_data(data):
    """
    Serializes review data into a JSON string.
    """
    return json.dumps(data)

review_data = {
    "product_id": "CheckerChain",
    "trust_score": 95,
    "consensus": 92,
    "ratings": 4.7
}
serialized_data = serialize_review_data(review_data)
print(f"Serialized Data: {serialized_data}")

CheckerChain employs a purpose-built smart contract to facilitate the storage, retrieval, and management of review data. This contract not only enables the immutable storage of data but also automates the distribution of $CRCN tokens under the platform's review-to-earn incentive model. When a transaction is initiated, the encoded review data is embedded in the transaction’s payload and transmitted to the smart contract. Upon successful execution, the smart contract inscribes the review data onto the blockchain, thereby establishing a decentralized and publicly verifiable record.

solidityCopy code// Solidity: Sample Smart Contract for CheckerChain
pragma solidity ^0.8.0;

contract CheckerChain {
    mapping(bytes32 => string) public reviewData;

    function storeReviewData(bytes32 reviewId, string calldata data) external {
        reviewData[reviewId] = data;
    }

    function getReviewData(bytes32 reviewId) external view returns (string memory) {
        return reviewData[reviewId];
    }
}

Contributors to the review process are rewarded with $CRCN tokens of SN87 Bittensor subnet, which can be implemented as ERC-20 standard assets to ensure compatibility and fungibility. The design of these tokens integrates principles similar to the Runes protocol on Bitcoin, enabling efficient tracking and streamlined token management. For data retrieval, users can query the smart contract through blockchain explorers, such as Etherscan, or via dedicated APIs. The retrieved data is then decoded from its hexadecimal format into its original structure for verification and analysis.

pythonCopy codefrom web3 import Web3

def retrieve_review_data(contract_address, review_id):
    """
    Retrieves review data from the smart contract.
    """
    contract = web3.eth.contract(address=contract_address, abi=contract_abi)
    review_data = contract.functions.getReviewData(review_id).call()
    return review_data

By leveraging the programmability and widespread tooling support of Ethereum and EVM-compatible networks, CheckerChain ensures a robust, transparent, and efficient implementation of its IRD framework.

• Inscription of Review Data (IRD) on Non-EVM Compatible Chains

Non-EVM-compatible blockchains, such as Solana, Cardano, and Polkadot, offer distinct architectural paradigms that CheckerChain adeptly integrates into its IRD framework. On the Solana blockchain, review data is first serialized into a JSON format and then encoded into a compact hexadecimal representation. Solana’s account-based model facilitates efficient data storage by associating a dedicated blockchain account with each review cycle or product. A custom Solana program, analogous to a smart contract, manages the validation and inscription of review data into these accounts.

rustCopy code// Solana Program: Store Review Data
use solana_program::{
    account_info::{next_account_info, AccountInfo},
    entrypoint,
    pubkey::Pubkey,
    program_error::ProgramError,
    msg,
};

entrypoint!(process_instruction);
fn process_instruction(
    program_id: &Pubkey,
    accounts: &[AccountInfo],
    input: &[u8],
) -> ProgramResult {
    let account = next_account_info(accounts)?;
    account.data.borrow_mut().copy_from_slice(input);
    msg!("Review data stored successfully.");
    Ok(())
}

On Cardano, the review data is inscribed as transaction metadata within its extended UTXO (eUTXO) model. The serialized metadata, embedded within the transaction structure, is stored on-chain, ensuring low-cost and high-throughput operations. Data validation and reward distribution are orchestrated through Plutus scripts, Cardano’s smart contract framework.

haskellCopy code-- Cardano: Plutus Script Example
{-# LANGUAGE DataKinds #-}
{-# LANGUAGE NoImplicitPrelude #-}
{-# LANGUAGE TemplateHaskell #-}
{-# LANGUAGE ScopedTypeVariables #-}

module CheckerChain where

import PlutusTx
import PlutusTx.Prelude
import Ledger
import Ledger.Typed.Scripts
import Prelude (String)

{-# INLINABLE storeReviewData #-}
storeReviewData :: String -> BuiltinData -> Bool
storeReviewData review _ = traceIfFalse "Review data validation failed." (length review > 0)

In the Polkadot ecosystem, CheckerChain utilizes parachains such as Moonbeam and Astar, each tailored to specific operational needs. On Moonbeam, which supports EVM compatibility, smart contracts are deployed to store and manage review data. Conversely, on WASM-based parachains such as Astar, the data is inscribed using native chain records.

By tailoring its multichain framework to the specific features of each blockchain, CheckerChain ensures the secure and immutable inscription of review data while maintaining universal accessibility. The integration of $CRCN tokens across these platforms serves as a unifying reward mechanism, enhancing interoperability and trust within the ecosystem.

4.3 Artificial Intelligence (AI)

In tRCM protcol of CheckerChain, BERT and LSTM are implemented to fine-tune all 10 categories and their 30 parameters over the training models. Each review $𝑅𝑖$ is represented as a sequence of review data, denoted by $𝑅𝑖=(𝑤𝑖1,𝑤𝑖2,...,𝑤𝑖𝑛)$, where $𝑛$ is the number of reviews in the cycle. BERT extracts contextualized embeddings for each value in the review, resulting in embeddings $𝐸𝑖𝑗$ for the $𝑗$-th token in the $𝑖$-th review. Mathematically, this can be represented as:

$𝐸𝑖𝑗=BERT(𝑤𝑖𝑗)$

These embeddings are then fed into an LSTM network for sequence processing, producing hidden states $𝐻𝑖$ for each review. The LSTM process can be represented as:

$𝐻𝑖=LSTM(𝐸𝑖1,𝐸𝑖2,...,𝐸𝑖𝑛)$

Once the hidden states $𝐻𝑖$ are obtained, they are analyzed to derive sentiment scores $𝑆𝑖𝑘$ and assessment scores $𝐴𝑖𝑚$ for each category $𝐶𝑘$ and parameter $𝑃𝑚$ respectively. Mathematically:

$𝑆𝑖𝑘=SentimentAnalysis(𝐻𝑖,𝐶𝑘)$

$𝐴𝑖𝑚=AssessmentAnalysis(𝐻𝑖,𝑃𝑚)$

In the context of trustless reviews, each reviewer performs zero-knowledge proof, ensuring the confidentiality of reviewers' identities and assessments. Mathematically, this process can be denoted as $ZeroKnowledgeProof(𝑅𝑖)$, where the review $𝑅𝑖$ is transformed into a zero-knowledge proof representation.

By integrating these mathematical models into the trustless review system, CheckerChain ensures both the privacy and accuracy of tRCM outputs while leveraging advanced AI techniques for fine-tuning the review process.

4.4 Subnet AI Infrastructure With Miner & Validator

CheckerChain’s ecosystem seamlessly integrates human feedback with AI-driven predictions, creating a trustworthy and decentralized review system. At its core, the CheckerChain platform gathers product ratings directly from human reviewers through tRCM protocol, forming the Ground Truth—a consensus-based benchmark that represents the most reliable assessment of a product. This human-verified data serves as the foundation for training and improving AI models within the CheckerChain subnet.

The CheckerChain subnet operates as a decentralized AI-powered prediction layer, continuously refining review scores through machine learning. It is structured into two key components: validators and miners. Validators play a crucial role in distributing product review tasks to miners and aggregating the Ground Truth ratings collected from the main platform. They evaluate miner-generated predictions, benchmarking them against the Ground Truth to ensure accuracy. By maintaining a competitive environment, validators score miners to optimize their models for better precision and efficiency.

Miners, on the other hand, are responsible for running AI models that predict review scores for listed products. These models evolve over time by learning from past predictions and adjusting their algorithms based on discrepancies with the Ground Truth. Through Reinforcement Learning from Human Feedback (RLHF), miners incorporate additional insights from validators and human reviewers, ensuring their models align more closely with real-world assessments. This continuous feedback loop allows the subnet to improve autonomously, reducing biases and increasing reliability in AI-driven ratings.

The subnet follows a decentralized learning and incentive structure, where AI models start with predefined datasets and historical review scores. Over time, miners fine-tune their models by comparing predictions with Ground Truth data, optimizing accuracy through RLHF. Validators play a key role in integrating tRCM-based human feedback into the training process, refining AI predictions iteratively. As a result, miners that consistently produce high-accuracy predictions receive higher benchmarks and greater rewards, creating a self-reinforcing cycle of improvement.

By combining tRCM human-decentralized ratings with AI-driven predictions, CheckerChain’s subnet evolves into a self-learning, decentralized, and transparent review system. The open participation model allows anyone to join as a miner or validator, contributing to an AI-enhanced ecosystem that continuously adapts to real-world opinions. This fusion of human intelligence and AI automation ensures a fair, scalable, and corruption-resistant review platform, setting a new standard for decentralized trust in product evaluations.

4.5 Decentralized Storage of Review Data & AI Agent Benchmarking

CheckerChain utilizes 0G’s decentralized storage infrastructure to manage its review data, ensuring cost efficiency, data integrity, and accessibility within a decentralized framework. The adoption of 0G’s solution provides significant cost advantages, reducing storage expenses by up to 80% compared to traditional cloud services like Amazon S3. Beyond cost-effectiveness, 0G’s decentralized network ensures data provenance by maintaining the authenticity and integrity of stored reviews, while its censorship-resistant architecture safeguards the data against tampering or unauthorized alterations. This combination of attributes underscores CheckerChain’s commitment to secure, reliable, and transparent data management practices.

The integration of 0G’s data availability layer enhances CheckerChain’s ability to support real-time querying and data-driven applications. The highly accessible review data enables AI agents and users to retrieve and analyze structured information with efficiency, facilitating timely decision-making processes. Moreover, this infrastructure serves as a robust foundation for training and refining AI models, enabling them to improve in predicting review scores and conducting sentiment analysis. By ensuring consistent and reliable data access, CheckerChain positions itself as an advanced platform for real-time analytics and informed evaluations.

CheckerChain with 0G extends beyond storage and accessibility to strategic integration within 0G’s AI agent ecosystem and marketplace. This lays the groundwork for innovative applications such as “agent breeding,” where high-performing AI agents are combined to create new, optimized iterations, further advancing the ecosystem’s technological capabilities.

5. PROTOCOL INCENTIVES

CheckerChain protocol incentivizes all contributions made by anyone within the ecosystem. When the majority of participants are honest, the trustless review consensus mechanism (tRCM) becomes secure and realistic. This protocol is designed to distribute incentives based on the consensus level of tRCM achieved.

Initially, incentives are maintained by distributing the new tokens. When $CRCN tokens get completely distributed, the inflation rate drops to 0%, and incentives are maintained by distributing revenues earned by the platform.

Protocol incentives are distributed on a monthly basis and counted as Epoch. The contributions of posters, reviewers and influencers are collectively accounted per epoch into performance scores. Based on these monthly scores, rewards are emitted by the Distributor Smart Contract. All scores get reset every epoch.

Any contributions on CheckerChain platform are incentivized through CP and CHECKR point system which are backed by Reward Vault (SN87 digital assets called $CRCN).

6. UTILITIES & ECOSYSTEM

CheckerChain is a utility-driven decentralized application (dApp). It fixes the problem of fake and manipulated review systems and is also designed with a gamified review-to-earn ecosystem.

6.1 Trustless Review Platform

CheckerChain is a next-gen AI-powered trustless review platform. This is one of the most innovative evolutions in the review industry. While CheckerChain is particularly focused on the crypto and blockchain segment, it is a much-needed upgrade that can be experimented with in all other categories where reviews are important.

6.2 Review-to-Earn Platform

Traditional review platforms have no mechanism to incentivize contributors without impacting review metrics. CheckerChain integrates tRCM bringing fairness to review metrics while also incentivizing all contributors.

6.3 tRCM as a Service (TaaS) for third-parties

tRCM is a revolutionary protocol. It does not need to be limited within a platform. Hence, tRCM as a Service (TaaS) model is available for any third parties to utilize tRCM. It can extend from a crypto review platform to multiple segments such as movies, books, electronics, cities, restaurants, hotels, and more.

6.4 Revenue Sharing

Posters, reviewers, and influencers are automatically rewarded. As the platform grows to generate revenues, participants in this ecosystem can act as beneficiaries.

6.5 Asset Utility (Payment for Services, Staking, Liquidity-Providing)

$CRCN tokens can be utilized as payment for various services where tRCM is implemented. Holders can earn rewards for staking or providing liquidity.

6.6 Gamified UX

CheckerChain is an interactive web3 platform where contributions are incentivized and boosted through a gamified model. UI and UX are designed to support various achievements, badges, digital artifacts, and leaderboards.

7. TOKENOMICS

$CRCN token is the only token of CheckerChain that is integrated through SN87 Subnet of Bittensor. The maximum supply of $CRCN token is 21,000,000 and follows the halving schedule as defined in subnet architecture.

Distribution of $CRCN (100% fair launch, no Presale, no VC, no Premined)

Distribution	Amount ($CRCN)	Allocation %
Miners	8,610,000	41%
Validators	8,610,000	41%
Founders	3,780,000	18%
TOTAL	21,000,000	100%

Loyalty Points

There are 2 types of loyalty point systems pegged with $CRCN. CP (checker points) is for leaderboard engagement (influencer), and CHECKR is for data labeler (poster, reviewer)

Pegging System (one-way): 10,000 CHECKR = 1 $CRCN (max 2.1b CHECKR) Reward Vault is maintained by the team with a floor of 210,000 $CRCN and a ceiling of 2.1m $CRCN.

Conversion Rate (one-way): 1 CP = 1 CHECKR (adjusted off-chain based on growth of user activities) There is no max limit on CP but controlled by 420,000,000 CHECKR.

Note: $CRCN is bought back by CheckerChain platform revenue to refill the Reward Vault every quarter.

Distribution of CHECKR (100% backed by $CRCN from Reward Vault)

Distribution	Amount (CHECKR)	Allocation %
Posters Incentives	420,000,000	20%
Reviewers Incentives	1,260,000,000	60%
Influencer Incentives	420,000,000	20%
TOTAL	2,100,000,000	100%

7.1 CHECKR Distribution schedule for Reviewers Incentives, Posters Incentives, and Influencers Incentives.

Time Period	Year Distribution %	Yearly Distribution
Posters	1.9%	8,000,000
Reviewers	1.9%	24,000,000
Influencers	1.9%	8,000,000

7.2 Revenue allocations:

Allocations	Supply Distribution Era	End of Supply Era
Posters	0%	10%
Reviewer	0%	30%
Influencers	0%	10%
Foundation	100%	50%

7.3 Reward Distribution:

Method	Calculation Basis	Period	Distribution
Poster	Trust Score	1st of Month	10% Instant 90% Vested (0.5% per day unlock)
Reviewer	Profile Score	1st of Month	10% Instant 90% Vested (0.5% per day unlock)
Influencer	CP points	1st of Month	10% Instant 90% Vested (0.5% per day unlock)
Miner	Weights, Trust	Per Block	100% Instant
Validator	vTrust	Per Block	100% Instant

7.4 Reward Ineligibility Criteria

For Poster	For Reviewer	For Influencer
45% < Trust Score < 55%	Profile Score < 40%	CP < 1000

At the end of every epoch, all conditions are checked. If ineligible, rewards become 0 for that category.

8. Business Model

CheckerChain controls its inflationary review-to-earn tokenomics with a powerful business model. Without generating sustainable revenue and infusing that revenue into its (SN87 digital assets) $CRCN tokens, it suffers a death spiral. Hence, to access CheckerChain platform and its premium features, there are two pricing models:

1. Subscription model (pay per month or year):

Features ⬇️	Free Forever Plan	Premium Plan	Business Plan
	$0/month	$9.99/month	$99.99/month
Product Submission	1 basic listing/monthly	10 basic listing/month	30 basic listing/month
Review Submission	Unlimited	Unlimited	Unlimited
Feedback Submission	Unlimited	Unlimited	Unlimited
Gassless	Yes	Yes	Yes
Earning Fee Tax	30% Charged	10% Charged	0% Charged
Penalty for Missing Tasks	Yes	No	No
Profile Verification	No	Yes	Yes
Analytical Page	Very limited	Limited	Yes (Full)
Comparison Tool	No	No	Yes
Reply Reviews	No	No	Yes
Review Cycle Request	No	10 request/month	30 request/month
Customized Review Questions	No	No	Yes
Ads-free Browsing	No	Yes	Yes

1. Pay per feature

Actions	Fees (in $USDT)	Notes
Basic Product Listing	$0	up to 10k impressions (50 reviews)
Standard Product Listing	$500	up to 40% traffic impressions (250 reviews)
Premium Product Listing	$1000	up to 70% traffic impressions (250+ reviews)
Review Cycle Fee	$4.99	Basic Product Listing mode
Full Analytical Page Access	$4.99	Monthly
Comparison Tool	$4.99	Monthly
Advertisement fee	custom	One time
Claim Product Ownership	custom	One time
Product Verification	$99.99	Monthly
Profile Verification	$4.99	Monthly
Product Discount/Offer	10% of Offer + $1.99	One time
Early Claim (During vesting)	custom	20% of claimed amt

Business models may get updated based on ecosystem growth and community demands.

9. CONCLUSION

We have outlined a decentralized review platform that can self-sustain in a trustless fashion. Using tRCM architecture, users can join or leave the protocol at their will without compromising the validity of reviews as long as the majority of reviewers are honest. This protocol incentivizes honest participants and penalizes attackers. Hence, there is no benefit of attacking the protocol with dishonest reviews. In this paper, we discussed our architecture, nature of participants, economic incentives, and some limitations. We are confident to baseline this whitepaper to launch the initial version of the decentralized review platform.

8.1. Acknowledgement

We would like to thank all of the advisors and proofreaders who improved this whitepaper with new ideas and error fixings.