AI Document Intelligence & Auto-Population Engine
A fintech and pet-tech platform that helps pet owners plan, save, and manage the lifetime cost of pet care is evolving from a static cost calculator into an adaptive, data-driven financial planning platform. Pet owners already hold rich information in veterinary bills, receipts, vaccination records, health records, and estimates, but it is unstructured, manually entered, and rarely reused — creating onboarding friction, incomplete data, and forecasts that degrade over time. Designed and prototyped an AI-powered document intelligence and auto-population engine.
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Design and prototype an AI-driven document intelligence system that ingests pet-related documents, extracts structured health and financial data, and automatically populates discrete records within a pet profile — a foundational data layer for the platform's future adaptive financial forecasting and planning.
Four phases: discovery and requirements (confirming document types, target data fields, and a sandboxed, oversight-controlled environment); data model and architecture design for OCR, document classification, structured extraction, confidence scoring, and human-review routing, kept modular and auditable; prototype development; and testing, refinement, and handover with known limitations documented.
A prototype that ingests invoices, receipts, health records, vaccination records, and estimates; performs OCR, document classification, and structured data extraction; normalizes the results into standardized medical and financial record types; and produces integration-ready structured outputs for pet profile auto-population. It generates confidence scores and surfaces records that need human review, and runs in a sandboxed environment without autonomously triggering financial or operational actions. Delivered with documentation of the extraction logic, output structure, confidence scoring approach, limitations, and integration considerations — a foundation for future forecasting, cost variance analysis, and savings recommendations.