The problem Cross-Chain Prediction Market with AI Powered Odds solves

Problem It Solves

Traditional betting platforms are centralized and opaque, allowing companies to set unfair odds, delay or refuse payouts, and block users without transparency. Users must trust a closed system with little visibility into how odds or results are handled.

What This Project Enables:

Decentralized, Trustless Betting With Chainlink
All bets and payouts are handled by smart contracts, ensuring fairness and eliminating centralized control.

AI-Driven, Data-Backed Odds Powered by Chainlink Functions
Odds are calculated by a trained AI model using real data and updated on-chain via Chainlink Functions—ensuring accuracy and eliminating bias.

Tamper-Proof and Verified Data
Odds are set using a trained AI model combined with real-time data fetched from Polymarket by an autonomous AI agent. Chainlink Functions brings these insights on-chain, ensuring accuracy, transparency, and zero manual bias.

Fully Transparent and Auditable Payouts
All transactions are on-chain and auditable, giving users complete trust in the system.

Challenges I ran into

Challenges & How I Overcame Them

1. No Access to Testnet Tokens (Especially Avalanche Fuji)

I quickly ran into a problem when I didn’t have testnet tokens to deploy or test my contracts — particularly AVAX on Fuji and Sepolia ETH because I didnt plan to purchase LINK.

Solution:

• For Sepolia ETH, I found faucets through online communities.
• But for Fuji AVAX, it was much harder — I even ran into scammers while asking around in the Avalanche Discord group.
• Eventually, I decided to buy 1 LINK and used Chainlink's official website to get the testnet assets I needed.
• This helped me move forward with building and testing cross-chain logic.

2. Struggled with Chainlink Functions

I had never used Chainlink Functions before, and initially I couldn't even get it to pass data from off-chain to my smart contract, even the AI cannot help as it gave the wrong way, I assumed they are not trained well in this part.

Solution:

• I followed the official Chainlink Functions documentation and successfully deployed the example first.
• When my co didn’t work, I debugged the issue by:
  1. Realizing the ngrok tunnel I used for local dev caused dynamic URLs, which broke the data fetch. I fixed this by using a static domain.
  2. Looking at recent fulfillment status and logs, I noticed failures in computation or callbacks. This helped me pinpoint whether the issue was with data fetching or smart contract execution.

3. Learning & Debugging Chainlink CCIP

CCIP was the most complex part — it’s not just contract logic, it’s cross-chain messaging with tokens and data. The AI still didnt work here.

Solution:

• I started by completing the official CCIP "send tokens with data" example to learn the basics.
• Then I customized it for my project: my messages included a struct, and I needed to forward that data to another contract on the receiving chain.
• Despite following the guide, things still didn’t work, and debugging cross-chain issues was tough.
• Eventually I learned to use tools like CCIP Explorer and Tenderly to trace message delivery and execution paths.
• I hit a wall when nothing seemed wrong — but then got help from the Chainlink Discord community, who found that the gas limit was too low on the receiving chain.
• That insight gave me confidence that my logic was correct, and I was finally able to complete the cross-chain flow.

Each of these hurdles was tough, but working through them helped me build deeper understanding and confidence in Web3 development — especially with Chainlink’s powerful tools like Functions and CCIP.