Overview

Crypto users are often asked to make high-stakes decisions with very little clarity. Before buying or interacting with a token, they may need to understand ownership permissions, minting privileges, blacklist functions, proxy contracts, holder concentration, liquidity, taxes, and other technical contract behaviors.

Most blockchain scanners expose this information as raw data, but they often leave the hardest part to the user: understanding what the data actually means.

I designed and built an AI-powered smart contract risk analyzer that translates complex blockchain signals into plain-language risk guidance. The goal was not to tell users what to buy. The goal was to help them understand the risks behind a token before they make a decision.

The problem

Smart contract risk is difficult to evaluate unless you already understand blockchain development, token contracts, and on-chain behavior.

A typical token scanner might show signals like “proxy detected,” “owner privileges,” “mint function,” “blacklist enabled,” or “top holder concentration,” but those labels can be confusing to non-technical users.

The real user questions are much simpler:

• Can the developer mint more tokens?

• Can trading be paused?

• Can my wallet be blacklisted?

• What happens if whales sell?

• Is ownership actually renounced?

• Is this token different on Ethereum versus PulseChain?

• What should I be worried about before buying?

The design challenge was to turn technical blockchain data into risk explanations people could actually use.

My role

I worked across product strategy, UX design, AI interaction design, risk modeling, and technical implementation.

I defined the product concept, mapped the risk categories, designed the user-facing explanations, explored blockchain data sources, and shaped the AI layer that helps users ask plain-language questions about contract risk.

This was a self-initiated project built to explore how AI can make technical financial systems more understandable and trustworthy.

Product goal

The goal was to create a scanner that could reduce complex smart contract and holder data into a small set of understandable risk signals.

Instead of overwhelming users with raw contract flags, the product would answer:

• What was detected?

• Why does it matter?

• How risky is it?

• What could happen to the user?

• What should they check before interacting with the token?

The core product principle was simple:

Users do not need more raw blockchain data. They need clearer risk interpretation.

Users

The primary users are everyday crypto participants who want to evaluate a token before buying or interacting with it.

Many of these users understand basic crypto behavior, but they may not know how to read contract permissions, identify centralized control, interpret holder concentration, or separate meaningful risk from noise.

The product was especially shaped around PulseChain and Ethereum users because forked tokens, duplicated assets, low-liquidity markets, and chain-specific contract behavior can make risk evaluation even more confusing.

Key design challenge

The hardest part was not detecting risk. The hardest part was defining what risk means to a real user.

A developer privilege is not meaningful on its own unless the product explains the consequence. A proxy contract is not useful information unless the user understands that the contract may be upgradeable. A whale wallet only matters if the user understands how concentrated ownership can affect price movement and trust.

The product needed to move from technical labels to plain-language guidance.

For example:

Instead of:

“Proxy detected”

The analyzer should explain:

“This contract may be upgradeable, which means its behavior could potentially change after launch. That does not automatically mean it is unsafe, but it does mean users should understand who controls upgrades.”

Instead of:

“Top 10 holders own 72%”

The analyzer should explain:

“A small number of wallets control most of the supply. If one or more of these wallets sell heavily, the price could move quickly and liquidity may not absorb the sell pressure.”

Risk signals

I organized the scanner around a focused set of user-facing risk categories:

Ownership and admin privileges

• Does the contract still have an owner?

• Can the owner change important settings?

• Has ownership been renounced?

• Are there privileged wallets?

Minting risk

• Can more tokens be created?

• Is the supply fixed?

• Could the owner inflate supply after launch?

Blacklist or transfer restrictions

• Can wallets be blocked?

• Can transfers be paused?

• Can trading be limited?

Proxy and upgradeability risk

• Is the contract upgradeable?

• Can logic be changed later?

• Who controls the upgrade path?

Holder concentration

• How much supply is held by the largest wallets?

• Are top holders unusually concentrated?

• Does one wallet control a dangerous amount of supply?

Whale risk

• Could large holders create major price movement?

• How much sell pressure could exist?

• Is liquidity deep enough to absorb large exits?

Buy and sell tax risk

• Are there transfer taxes?

• Are buy and sell taxes different?

• Could taxes be changed?

Contract age and context

• Is the contract new or established?

• Does the token have enough history to evaluate?

• Is there meaningful liquidity or trading activity?

Data sources explored

To support the scanner, I explored several blockchain and market data sources, including:

• Ethereum RPC

• PulseChain RPC

• DexScreener

• GeckoTerminal

• GoPlus Security

• Moralis owner data

• Etherscan-family explorers

• PulseChain-specific liquidity and routing data

One important product consideration was chain separation. A token on Ethereum and a token on PulseChain may share a name or history, but risk needs to be evaluated by chain. The analyzer needed to avoid blending signals across networks in a way that could mislead users.

AI interaction model

The AI layer was designed as an explanation system, not a generic chatbot.

The goal was to let users ask practical questions in natural language and receive grounded, easy-to-understand answers based on the detected risk signals.

Example questions included:

• Can the developer mint more?

• Can the owner pause trading?

• Can this token blacklist wallets?

• What happens if whales sell?

• Is this contract renounced?

• Why is the risk score high?

• What should I check before buying?

• Is this token safer on Ethereum or PulseChain?

The AI experience was designed to reduce the distance between technical detection and user understanding.

Rather than showing a score and expecting users to interpret it, the product would explain what each signal means, why it matters, and what kind of decision risk it creates.

UX approach

The product experience was structured around clarity and progressive disclosure.

The first view needed to give users a fast risk summary. From there, users could explore deeper explanations if they wanted more detail.

The interface direction included:

• A clear overall risk summary

• Risk cards grouped by category

• Plain-language explanations

• Severity levels

• “What it means” descriptions

• Evidence behind each risk

• AI-generated Q&A prompts

• Chain-specific context

• Warnings without fear-mongering

• A final summary that helps users understand tradeoffs

The goal was to avoid two common extremes in crypto tools:

Too technical, where users get raw data but no guidance.

Too simplified, where users get a score but no explanation.

The analyzer needed to sit in the middle: simple enough to understand, but transparent enough to trust.

Design principles

1. Explain the consequence, not just the signal

A contract flag should always be tied to a possible user impact.

2. Avoid false certainty

The product should help users understand risk, not guarantee safety.

3. Make technical data readable

Labels, descriptions, and summaries should use everyday language wherever possible.

4. Show evidence

Users should be able to understand why the analyzer flagged something.

5. Support learning

The product should help users become better at evaluating tokens over time.

Example risk card

Minting risk

Detected: Mint function found

Severity: High

What it means: The contract may allow additional tokens to be created after launch.

Why it matters: If new tokens are minted without clear limits or governance, existing holders could be diluted and the token price could be affected.

What to check: Look for ownership status, mint permissions, and whether minting is controlled by a trusted or renounced owner.

Product outcome

The project transformed a complex technical workflow into a clearer risk interpretation experience.

The analyzer reduced a large set of raw blockchain signals into a smaller, more understandable set of user-facing categories. It also introduced an AI Q&A layer that allows users to ask the questions they actually care about instead of manually interpreting contract data.

The most important outcome was a clearer product direction:

Smart contract scanners should not only detect risk. They should explain risk.

Why this matters

Crypto products often assume users can interpret technical information on their own. But in high-stakes financial environments, unclear information can lead to poor decisions.

This project explored how AI can act as a translation layer between complex systems and human decision-making.

The broader design lesson applies beyond crypto:

When users face technical, regulated, or high-risk decisions, product design should not simply expose more data. It should help people understand what matters, why it matters, and what they can do next.

Reflection

This project helped me clarify the kind of AI products I want to design: tools that make complex systems easier to understand and trust.

The most valuable design insight was that risk is not just a technical state. Risk is a user comprehension problem. A token may have multiple contract flags, but those flags only become useful when the product explains their consequence in plain language.

If I continued developing this product, I would focus on improving data confidence, adding source transparency, refining the scoring model, and testing the explanations with both experienced and newer crypto users.

The long-term opportunity is to create a more trustworthy risk intelligence layer for blockchain users, one that helps people make better decisions without needing to become smart contract experts.