Route-Clear: AI-Assisted Fulfillment Verification and Settlement Control
Route-Clear uses AI to extract fulfillment evidence from documents, then applies deterministic validation and controlled settlement rules. Human review, bounded payment-routing actions, and hash-chained audit logs support safer, traceable financial w
Business Challenge
Fulfillment and settlement workflows can involve unstructured documents, inconsistent evidence, and financial decisions that must be accurate and auditable. Relying on manual review alone can make validation difficult to standardise and exceptions harder to trace. Using a language model alone to make financial decisions also introduces reliability risks. Route-Clear addresses this engineering problem by separating AI-assisted evidence extraction from deterministic validation and settlement controls, with human review available for exceptions.
Solution Delivered
Route-Clear is an AI-assisted fulfillment-to-settlement controller built with FastAPI and Python on the backend and React, TypeScript, and Vite on the frontend. The workflow accepts fulfillment documents and uses Gemini multimodal AI to extract relevant evidence. Deterministic validation then checks the extracted information against application rules before a settlement decision is made. AI extraction is separated from financial decision logic so the model does not independently authorise money movement. The system includes controlled settlement decisions, a bounded Razorpay Route adapter tested in simulated mode, human review and override paths, and a hash-chained audit trail to support traceability. SQLite supports application data storage, while Decimal is used for precise financial arithmetic. Validation work includes a synthetic dataset of 150 documents, 23 passing backend tests, and a successful frontend build. Payment routing was tested in simulation, not in production. Route-Clear is presented as a technical project; no live customer deployment or independently measured business savings are claimed.
Outcomes Achieved
Route-Clear demonstrates an end-to-end prototype for AI-assisted fulfillment evidence extraction and controlled settlement processing. Its architecture separates probabilistic AI extraction from deterministic financial validation, helping make settlement decisions more controlled and explainable. Implemented capabilities include document extraction using Gemini, rule-based validation, settlement workflow logic, human review and override paths, a simulated payment-routing integration, and hash-chained audit logging. Technical validation covered a synthetic dataset of 150 documents, 23 passing backend tests, and a successful frontend build. These results establish prototype-level implementation and testing progress. They do not establish production reliability, reduced processing time, realised financial savings, or outcomes from a live customer engagement.
Measurable Business Outcome
Technical validation evidence: a synthetic dataset of 150 fulfillment documents was used during development; 23 backend tests passed; and the frontend production build completed successfully. The Razorpay Route adapter was tested in simulated mode. These are prototype validation results, not measured business improvements. No production transaction volume, hours saved, cost savings, revenue impact, or client-verified outcomes have been established.