OpenLeaf
Intelligent Autonomous Refund Policy Engine
Created on 15th February 2026
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OpenLeaf
Intelligent Autonomous Refund Policy Engine
The problem OpenLeaf solves
This solution transforms the complexity of returns into a high-integrity revenue recovery tool. By shifting from a reactive, manual process to a proactive, AI-driven workflow, it creates a safer environment for brands and a frictionless experience for honest buyers.
Our solution transforms the return center from a massive revenue leak into a high-integrity retention engine by replacing manual, reactive workflows with an AI-driven, security-first architecture. By combining deepfake image forensics to eliminate fraud with context-aware sequential logic that prioritizes exchanges over refunds, we automate the "why" and "how" of every claim. Once integrated with our predictive risk scoring and policy-retrieval systems, brands will no longer just process returns they will intelligently protect their bottom line, offering instant trust to loyal shoppers while programmatically neutralizing bad actors.
Challenges we ran into
Our primary technical hurdle lay in the RAG (Retrieval-Augmented Generation) pipeline, specifically during Document Encoding and API synchronization. We initially faced high latency and context drift when mapping natural language user complaints to dense, unstructured PDF policy drafts. To resolve this, we transitioned from standard page-level encoding to a recursive character splitting strategy, creating overlapping vector chunks that preserved legal context. We further optimized the API middleware by implementing asynchronous request handling and a caching layer for frequent policy queries, reducing response times from seconds to milliseconds and ensuring the sequential chat engine remained fluid and accurate.
Tracks Applied (1)
Ethereum Track
ETHIndia
Technologies used