SafeHands AI: Compliance Orchestrator

The Problem Logistics and freight insurance fraud costs enterprises billions annually. Currently, human adjusters must manually read a driver's transcript (e.g., "the cargo was completely destroyed") and cross-reference it against photographic evidence to approve or reject a claim. This process is slow, expensive, and highly prone to error. Our Solution: SafeHands AI SafeHands AI completely automates this process using a distributed multi-agent system built on the Band network. We ingeniously divided the cognitive load across three specialized, independent remote agents that collaborate over Band WebSockets to make high-stakes financial decisions: 1. The Intake Agent (Powered by Featherless Llama 3.1 8B): Listens to the driver's unstructured voice dictation, parses the messy input, and extracts structured JSON containing the claimed cargo type and claimed damage severity. 2. The Vision Agent (Powered by Featherless Qwen2.5-VL 72B): Acts as the "eyes" of the operation. It analyzes the uploaded cargo image, detects the physical cargo type, and independently estimates the actual damage percentage using multi-modal visual reasoning. 3. The Compliance Agent (Powered by AI/ML API Llama 3.3 70B): The central decision-maker. It receives the context from both the Intake and Vision agents via Band and cross-references them to catch discrepancies. If a driver claims 100% damage but the Vision agent detects only 30% damage, the Compliance Agent instantly flags the discrepancy and REJECTS the claim, logging the decision to an immutable ledger. If the evidence matches, the claim is APPROVED. Why it fits the Hackathon SafeHands AI was built specifically for Track 3: Regulated & High-Stakes Workflows. Band is not just a wrapper in our project; it is the absolute backbone coordination layer allowing our independent Python agent processes to discover each other, divide work, and seamlessly share context across different LLM provider frameworks.