AiPayroll
The Future of Payroll, Powered by AI & Web3.
Created on 29th June 2025
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AiPayroll
The Future of Payroll, Powered by AI & Web3.
The problem AiPayroll solves
The problem we’re solving
We aim to solve the traditional Payroll system problems with our Decentralised payroll system backed with AI. Traditional payroll systems are often closed and require blind trust. Our Decentralised payroll system helps in managing remote teams across different countries with Cryptocurrency payment
Key issues with traditional payroll
- Zero transparency: You don’t know exactly how or when payments move. It’s all manual, prone to errors, and runs on blind trust.
- Cross-border chaos: Paying salaries internationally means high fees, conversion problems, and long delays.
- Manual drudgery: Spreadsheets, emails, and endless calculations are still the norm. Mistakes are common, and scaling is hard.
How AI Payroll fixes this
AI Payroll is a decentralized, AI-powered payroll platform built on blockchain and Web3. It automates, secures, and simplifies payroll — cutting out inefficiency and risk.
What you can use it for
- Smooth payroll management: Add employees, set salaries, track balances, and pay — all in one transparent dashboard.
- Instant payouts: Salaries are sent directly to employees’ wallets using smart contracts. No intermediaries, no delays.
- AI assistant: Our Azure OpenAI-based agent lets you ask things like:
- What’s the current month’s payroll?
- Get employee details by email
- Add or update employees
- Trigger payroll runs through MetaMask
- Scheduled payments: We use Chainlink automation to run payroll on fixed schedules — reliably and gas-efficient.
- Fully on-chain: No central data silos. Companies and employees control their own data and tokens.
Who’s it for
- Web3 startups and DAOs paying in crypto
- Remote-first companies with global teams
- HR tools looking to add AI + blockchain payroll
- Basically anyone fed up with clunky, error-prone payroll systems.
Challenges we ran into
Challenges I ran into
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Multi-agent flow with LLM + data grounding:
Getting our AI agent to actually respond with meaningful, context-aware answers was tricky. We had to figure out how to feed employee and employer data from MongoDB into the LLM so it could answer things like “What’s Rajesh’s salary this month?” without hallucinating. It took quite a bit of prompt engineering, context stitching, and making sure we weren’t leaking or mixing up data across sessions.
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Securing private data in a decentralized setup:
Mixing off-chain databases (MongoDB) with on-chain smart contracts meant thinking hard about what data must be on-chain vs what stays off-chain encrypted. Also ensuring our MetaMask interactions were safe and easy for non-technical users.
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Testing the full stack end-to-end:
From AI queries to MongoDB lookups, smart contract writes, and blockchain event listeners, getting everything to play nicely together was a challenge. Especially tough was simulating time-triggers for testing scheduled payroll.
Tracks Applied (4)
Onchain Finance
Best Use of ElizaOS
ElizaOS
AWS Credits for all Hackathon winners and runner ups
AWS
Avalanche Track
Avalanche