AidLedger
Emergency aid, but on-chain
The problem AidLedger solves
The Problem
Disaster response today is slow, opaque, and fragmented.
Critical decisions rely on manual damage reports, relief funds are delayed by bureaucratic approvals, and donors have little visibility into how emergency money is actually used. In high-impact situations, these delays and blind spots lead to wasted resources, mistrust, and avoidable loss of life.
How AidLedger Solves It
AidLedger is an end-to-end, autonomous disaster relief protocol powered by AI agents and secured by blockchain.
- AI-driven disaster detection analyzes satellite or drone imagery in near real time to identify affected regions
- Autonomous verification agents cross-check claims, filter misinformation, and validate severity before action
- On-chain fund disbursement ensures relief funds are released transparently and only after verified triggers
- Live dashboards convert complex AI and blockchain activity into clear, human-readable insights
AidLedger replaces fragmented coordination with a single, trusted execution layer for disaster response.
Why It’s Safer and More Reliable
- Removes slow, manual decision chains during emergencies
- Prevents fund leakage through verifiable, on-chain transactions
- Creates an immutable public audit trail for every relief action
- Balances AI speed with verification for responsible automation
Every decision is logged. Every transfer is provable. Every action is accountable.
Who Uses AidLedger
- Governments — faster, auditable emergency response infrastructure
- NGOs — transparent and efficient aid distribution
- Donors — cryptographic proof of impact and fund usage
- Disaster response teams — real-time situational intelligence
In One Line
AidLedger transforms disaster relief into a fast, verifiable, and trust-minimized system powered by AI agents and secured by blockchain.
Challenges I ran into
Challenges I Ran Into
Building AidLedger required tightly integrating AI, multi-agent orchestration, blockchain execution, and real-time visualization—each introducing its own set of challenges. The most critical hurdles and how they were resolved are outlined below.
Blockchain Transaction Failures
The Issue:
Even after successful disaster detection and verification, on-chain transactions occasionally failed due to underpriced replacement errors or silent funding failures.
Root Cause:
Upstream agents did not always populate transaction parameters correctly, leading to invalid or zero-value transfers.
How I Solved It:
I traced the full execution pipeline end-to-end, introduced defensive validation and fallback logic, and resolved Web3 library version mismatches affecting raw transaction handling.
AI Confidence vs Real-World Accuracy
The Issue:
Early computer vision models produced inconsistent confidence scores, making it unclear when an event should trigger fund release.
Root Cause:
Different disaster types require different detection strategies and sensitivity thresholds.
How I Solved It:
I separated detection pipelines by disaster category (fire, flood, structural damage, casualties) and tuned confidence thresholds independently to balance responsiveness with false positives.
Agent Coordination & State Flow
The Issue:
Multiple agents occasionally executed out of order, causing race conditions and premature actions.
Root Cause:
Execution boundaries between detection, verification, and execution stages were not explicitly enforced.
How I Solved It:
I introduced a message-queue-driven workflow with strict state transitions, ensuring each agent acted only after the previous stage completed successfully.
Image Processing Pipeline Errors
The Issue:
Mismatched image formats (raw bytes, PIL images, OpenCV arrays) caused crashes and inconsistent analysis results.
Root Cause:
Different libraries expected different formats, with insufficient validation during conversions.
How I Solved It:
I standardized the image processing pipeline, added validation at each stage, and implemented graceful fallbacks to prevent system-wide failures.
Rapid Frontend Prototyping vs System Complexity
The Issue:
Exposing real-time AI decisions and blockchain activity without overwhelming users.
Root Cause:
Highly complex backend processes needed to be translated into clear, human-understandable signals.
How I Solved It:
I adopted a clarity-first UI approach—surfacing only essential states, using real-time status indicators, and progressively revealing technical details when needed.
Tracks Applied (3)
Ethereum Track
ETHIndia
AI TRACK
CodeCrafters
Open Innovation
Lovable
Technologies used
Cheer Project
Cheering for a project means supporting a project you like with as little as 0.0025 ETH. Right now, you can Cheer using ETH on Arbitrum, Optimism and Base.