AI Invoice Parsing — Construction invoice ingestion and coding assistant

Bulk upload, extract details, and allocate invoices to the right cost accounts with AI—and a clear audit trail.

Overview

AI Invoice Parsing is a web application designed to streamline the intake of construction invoices at scale. Teams can bulk upload invoices, automatically extract key fields, and receive AI-assisted suggestions to allocate invoices to the correct cost accounts / cost codes—with a clear audit trail and a human-in-the-loop workflow.

The challenge

Construction projects generate large volumes of invoices in inconsistent formats (PDFs, scans, emailed attachments). Manual processing creates recurring issues:

• Time-consuming data entry and rework caused by unreadable scans and varied layouts
• Inconsistent vendor naming and line-item descriptions that make coding error-prone
• Delays in updating cost reports and drawdown/valuation workflows because invoices lag behind
• Difficulty proving "why" an invoice was coded a certain way when audits or disputes arise

What we built

The system combines document processing with an approval workflow so automation accelerates teams without sacrificing control:

• Bulk upload of invoice documents linked to a project (and optionally packages/sub-projects)
• Field extraction for common invoice data (supplier, invoice number, dates, totals, tax, currency, line items)
• AI-assisted cost account allocation that suggests likely cost codes based on invoice content and historical patterns
• Review + approval flow so finance/project teams can accept, adjust, or reject suggestions
• Exception handling for low-confidence documents, missing fields, and ambiguous line items

Key features

• Human-in-the-loop validation: confidence-driven review queues so attention goes to the right items
• Explainable suggestions: show the signals used (vendor, keywords, prior invoices, project context) to support trust
• Audit trail: capture who approved what, when, and why—supporting governance and compliance needs
• Search + reconciliation: quickly find invoices by vendor, value, date, project, or cost code
• Export / integration-ready outputs: structured data suitable for downstream accounting/ERP systems

Technical approach (high level)

• Document ingestion: resilient handling of mixed-quality PDFs and scans
• Extraction pipeline: parse documents into structured fields and line items
• Allocation model: combine rules + AI signals to recommend cost accounts with confidence scoring
• Data integrity: idempotent processing and deduplication to prevent double-counting

Outcome

AI Invoice Parsing reduces the operational burden of invoice processing while improving consistency in cost coding. The result is faster cost visibility for projects, fewer manual errors, and stronger traceability when teams need to justify decisions.