Building a secure LLM-based parsing pipeline to extract supplier PDF billing data and synchronize records.
The client is a distribution business processing hundreds of vendor supply invoices weekly. The invoices are delivered as PDF email attachments from dozens of different suppliers, each using unique document layouts.
Accounting staff manually read each supplier PDF, located totals, verified product billing codes, and typed the fields into their main administrative ledger.
Manual typing was slow and caused inventory tracking errors due to typos. During transaction peak seasons, billing backlogs delayed supplier payments, causing supply chain friction.
Sigmatic Soft built an event-driven AI Invoice Extractor Engine. When a supplier email arrives, the PDF attachment is scraped, parsed using an LLM model, validated against predefined schemas, and written directly into the central database.
We designed the pipeline using Python, utilizing OpenAI APIs. We set up Pydantic validation structures to guarantee structured JSON outputs: if the model fails to extract the exact total or return the correct date format, the document is flagged for human review.
The automation reduced document processing time from minutes to under 10 seconds. Over 90% of invoices are parsed and synced with zero human intervention, completely eliminating manual entry backlog.
AI models carry hallucination risks. Designing strict programmatic validation rules (e.g. validating that line-item totals mathematically sum to the grand total) is crucial before writing AI outputs to production databases.
Let's discuss how to integrate LLM workflows into your office operations.
Discuss AI Workflows