# Invoice Processing Automation — AI Workflow export
# https://invitationbuddy.com/workflows/invoice-processing-automation
schema_version: "1.0"
exported_at: "2026-08-16T09:10:50+00:00"
source: "https://invitationbuddy.com/workflows/invoice-processing-automation"
workflow:
  name: Invoice Processing Automation
  slug: "invoice-processing-automation"
  version: "1.0.0"
  tagline: "Read supplier invoices, validate them and post for approval"
  summary: "Watches the AP inbox, extracts every field, validates against the purchase order and supplier record, and posts clean data for approval with low-confidence fields flagged."
  description: |-
    Accounts payable is manual keying plus exception handling. This pipeline does the keying — supplier, dates, totals, line items, tax — validates it against the PO and supplier master, and posts the result.
    
    The economics come from confidence routing: high-confidence fields post automatically, uncertain ones queue for a human. That is what makes it work at volume without becoming a rubber stamp.
  category: Finance
  type: API Pipeline
  difficulty: intermediate
  automation_level: mostly_automated
  pricing: paid
  language: en
  trigger:
    type: email_event
    detail: Invoice arrives in AP inbox
  input_types:
    - pdf
    - email
    - document
  output_types:
    - dataset
    - spreadsheet
    - report
  author: null
  company: null
  last_updated: "2026-07-13"
overview:
  purpose: |-
    Accounts payable is manual keying plus exception handling. This pipeline does the keying — supplier, dates, totals, line items, tax — validates it against the PO and supplier master, and posts the result.
    
    The economics come from confidence routing: high-confidence fields post automatically, uncertain ones queue for a human. That is what makes it work at volume without becoming a rubber stamp.
  business_value: "Removes manual keying from AP and shortens the approval cycle, while keeping a human on the exceptions that matter."
  expected_outcome: "Structured, validated invoice records posted for approval, with only uncertain fields requiring human attention."
  problems_solved:
    - "Manual keying is slow and error-prone at volume"
    - Every supplier uses a different invoice layout
    - "Mismatches against the PO are found late, after approval"
    - No audit trail linking a posted figure to the document it came from
  ideal_users:
    - Finance and AP teams processing meaningful invoice volume
    - Shared service centres
    - Operations teams handling supplier documents
  benefits:
    - Handles layout variation that template matching cannot
    - Validates against the PO before anything reaches approval
    - "Per-field confidence targets human effort where it matters"
    - Every figure traceable back to its position in the document
  limitations:
    - Unusual layouts still need examples to reach good accuracy
    - "Multi-page line-item tables that span breaks are the common failure"
    - Handwritten annotations are unreliable
    - Regulated environments need an audit trail of every correction
  success_metrics:
    - "Straight-through processing rate"
    - "Field-level accuracy on a sampled audit"
    - "Average time from receipt to approval-ready"
    - Cost per invoice processed
metrics:
  estimated_minutes: 6
  estimated_cost: 0.22
  cost_currency: USD
  estimated_tokens: 5000
  success_rate: 93
  automation_percent: 85
  human_review_percent: 15
industries:
  - Finance
  - Manufacturing
  - Retail
  - "E-commerce"
tags:
  - Enterprise Ready
  - High ROI
  - Human Review
  - Batch Processing
ai_skills:
  - OCR
  - Document Processing
  - Data Analysis
tools:
  -
    name: ChatGPT
    role: primary
    required: true
    url: "https://invitationbuddy.com/ai-directory/chatgpt"
models: []
integrations:
  -
    name: OpenAI
    kind: ai_provider
    auth_type: api_key
    required: true
  -
    name: Google Sheets
    kind: productivity
    auth_type: oauth
    required: true
  -
    name: Airtable
    kind: storage
    auth_type: api_key
    required: false
  -
    name: Webhook
    kind: protocol
    auth_type: webhook
    required: false
  -
    name: REST API
    kind: protocol
    auth_type: api_key
    required: false
prerequisites:
  -
    kind: account
    label: Document AI provider
    detail: "Nanonets, Mindee, Textract or an equivalent IDP service."
    required: true
  -
    kind: api_key
    label: LLM API key
    detail: For validation reasoning and exception summaries.
    required: true
  -
    kind: account
    label: Accounting system with API
    detail: To post the extracted records.
    required: true
  -
    kind: permission
    label: Data residency review
    detail: Invoices contain commercial and sometimes personal data.
    required: true
steps:
  -
    number: 1
    title: Watch the AP inbox
    description: "Trigger on new mail with a PDF or image attachment; deduplicate against already-processed documents."
    input: Inbound email
    output: Document job
    estimated_minutes: 1
    difficulty: intermediate
    automation: auto
    manual_review: false
  -
    number: 2
    title: Classify the document
    description: "Confirm this is an invoice rather than a statement, credit note or remittance — each needs different handling."
    model: "GPT-4o"
    input: Document
    output: Document type
    estimated_minutes: 1
    difficulty: intermediate
    automation: auto
    manual_review: false
    warnings:
      - Statements misclassified as invoices cause duplicate payments — this step is not optional
  -
    number: 3
    title: Extract the fields
    description: "Pull supplier, invoice number, dates, currency, subtotal, tax, total and every line item, each with a confidence score."
    tool: Nanonets
    input: Invoice document
    output: Structured fields + confidences
    expected_result: A structured record with a confidence per field.
    estimated_minutes: 1
    difficulty: intermediate
    automation: auto
    manual_review: false
  -
    number: 4
    title: Validate against PO and supplier
    description: "Check arithmetic, match the PO, verify supplier and bank details, and flag duplicates."
    model: "GPT-4o"
    prompt: |-
      Validate this extracted invoice against the purchase order and supplier record.
      
      Invoice: {{invoice_json}}
      Purchase order: {{po_json}}
      Supplier record: {{supplier_json}}
      
      Check and report on:
      1. Does line total + tax equal the stated total?
      2. Do line items match the PO in description, quantity and price?
      3. Do the supplier name and bank details match the master record exactly?
      4. Is this invoice number already recorded for this supplier?
      
      Return JSON with a pass/fail per check and a short reason for each failure. Flag ANY bank detail mismatch as critical.
    input: Extracted fields + PO + supplier master
    output: Validation report
    estimated_minutes: 1
    difficulty: intermediate
    automation: auto
    manual_review: false
    warnings:
      - "A changed bank detail is the classic invoice-fraud signal — always route it to a human, never auto-approve"
  -
    number: 5
    title: Route by confidence
    description: "Fully validated, high-confidence invoices post straight through; anything uncertain or failing a check goes to the exception queue."
    input: Validated record
    output: Posted or queued
    estimated_minutes: 1
    difficulty: intermediate
    automation: auto
    manual_review: false
  -
    number: 6
    title: Human exception review
    description: "A finance user reviews only the flagged fields, side by side with the source document."
    input: Exception queue
    output: Corrected record
    expected_result: Corrections logged against the document for audit.
    estimated_minutes: 1
    difficulty: intermediate
    automation: manual
    manual_review: true
prompts:
  -
    title: Validate against PO and supplier
    kind: user
    body: |-
      Validate this extracted invoice against the purchase order and supplier record.
      
      Invoice: {{invoice_json}}
      Purchase order: {{po_json}}
      Supplier record: {{supplier_json}}
      
      Check and report on:
      1. Does line total + tax equal the stated total?
      2. Do line items match the PO in description, quantity and price?
      3. Do the supplier name and bank details match the master record exactly?
      4. Is this invoice number already recorded for this supplier?
      
      Return JSON with a pass/fail per check and a short reason for each failure. Flag ANY bank detail mismatch as critical.
    variables:
      - invoice_json
      - po_json
      - supplier_json
    model_hint: "GPT-4o"
use_cases:
  -
    title: Accounts payable at volume
    description: Process supplier invoices without a keying team.
    industry: Manufacturing
    outcome: Shorter approval cycle
  -
    title: "Multi-entity consolidation"
    description: Normalise invoices from many entities into one chart of accounts.
    industry: Finance
    outcome: Consistent coding
  -
    title: Expense document capture
    description: Structure receipts and expense claims alongside invoices.
    industry: Retail
    outcome: Faster reimbursement
faq:
  -
    question: "What straight-through rate is realistic?"
    answer: "With a stable supplier base and clean scans, a high proportion posts without touch. New suppliers and unusual layouts drop it until they have been seen a few times."
  -
    question: "How does this prevent invoice fraud?"
    answer: "Step 4 explicitly checks bank details against the supplier master and flags any change as critical. That single check catches the most common payment-diversion attack — but only if the flag routes to a human."
  -
    question: "Do we need a dedicated document AI service?"
    answer: "For volume, yes. General vision models read invoices well but do not give reliable per-field confidence scores, and confidence routing is what makes the economics work."
