Project Overview
Automated document processing pipeline ingesting service requests, extracting structured data via AI, and routing approval checkpoints to humans.
The Operational & Engineering Challenge
Operations staff were spending over 15 hours per week manually extracting tabular data, order numbers, and specifications from vendor PDFs and emails, resulting in entry bottlenecks and avoidable transcription errors.
- High accuracy requirement: Automated extraction must be verifiable by staff before executing financial actions.
- Must handle varied PDF formats and unstructured scanned documents.
- Data privacy: Customer information must remain isolated and never used for public model training.
Engineering & Architecture Approach
We implemented an asynchronous event-driven automation layer combining deterministic optical parsing with LLM-powered semantic extraction. Extracted fields are scored for confidence and presented in a side-by-side human review drawer before syncing to target systems.
Automated webhook & email parsing with PDF text extraction.
Schema-guided LLM structured output parsing with field-level confidence flags.
Streamlined UI highlighting low-confidence fields for instant 1-click verification.
Resilient queue workers dispatching validated data to ERP and CRM systems.
Key Engineering Decisions
Enforced human verification triggers for any document confidence score below 98%.
Used Redis queue with dead-letter retry logic to ensure zero dropped documents during volume spikes.
Stored all original documents and extraction telemetry for transparent audit compliance.
Verified Outcomes
Transformed hours of manual transcription into rapid seconds of verification.
Reduced data entry mistakes to near zero through dual automated and human validation.
Enterprise data isolation maintained with zero public model training exposure.
