Invoice & Accounts Payable Automation

11 Freight invoices combine fixed fields (invoice number, dates, addresses) with variable-length data (charges). One invoice might have 3 line items, another might have 12. Defining charge_1, charge_2, charge_3 fails when the count varies. Reducto’s Extract API handles both patterns: fixed fields use schema properties, variable charges use array extraction. This cookbook shows how to extract a freight invoice into structured JSON.

Sample Document

Download the sample: freight-invoice.pdf

This Atlas National Freight invoice contains:

Invoice header (number, date, payment terms)
Bill-to and ship-to addresses
Shipment details table (items, weight, NMFC class)
Freight charges breakdown (linehaul, fuel surcharge, accessorials)
Payment instructions

Create API Key

Open Studio

Go to studio.reducto.ai and sign in. From the home page, click API Keys in the left sidebar.

Studio home page with API Keys in sidebar

View API Keys

The API Keys page shows your existing keys. Click + Create new API key in the top right corner.

Configure Key

In the modal, enter a name for your key and set an expiration policy (or select “Never” for no expiration). Click Create.

New API Key modal with name and expiration fields

Copy Your Key

Copy your new API key and store it securely. You won’t be able to see it again after closing this dialog.

Set the key as an environment variable:

export REDUCTO_API_KEY="your-api-key-here"

Studio Walkthrough

Start Extract Workflow

Go to studio.reducto.ai and click Extract on the homepage to start a new extraction workflow. Upload the freight invoice PDF.

Review Parse Results

After upload, you’ll see the Parse view showing how Reducto structures the document. The right panel displays extracted text content.

Parse view showing freight invoice with extracted text

This confirms Reducto can read the invoice header, addresses, shipment details, and freight charges.

Switch to Extract View

Click Extract in the top navigation to switch views. In the Schema Builder, add fields by entering a name, selecting a type, and providing a description. Start with invoice_number.

Extract view with Schema Builder showing invoice_number field

You can add a System Prompt in the Instructions section for context like “This is a freight invoice with shipment information.”

Build Nested Schema with Arrays

Add more fields including nested objects and arrays. For shipment information, create an array type field called shipment_info with nested fields like bill_of_lading, service_level, and equipment_type.

Schema Builder showing nested shipment_info array with fields

The Schema Builder supports nested fields - click the arrow to expand and define child fields within objects and arrays.

Run Extraction and View Results

Click Run to execute the extraction. Switch to the Results tab to see extracted values. The document highlights where each value was found.

Extraction results showing highlighted values on document

Notice how the extracted values (ANF-INV-2026-004918, February 11, 2026, Net 30) are highlighted directly on the document, showing exactly where Reducto found each piece of data.

API Implementation

Building the Schema

A good extraction schema mirrors the invoice structure. We’ll build it incrementally, explaining design decisions along the way.

Invoice Header

Every invoice has fixed header fields. Request dates in ISO format (YYYY-MM-DD) for consistent parsing and date math.

header_schema = {
    "invoice_number": {
        "type": "string",
        "description": "Invoice number from the header"
    },
    "invoice_date": {
        "type": "string",
        "description": "Invoice date in YYYY-MM-DD format"
    },
    "due_date": {
        "type": "string",
        "description": "Payment due date in YYYY-MM-DD format"
    },
    "payment_terms": {
        "type": "string",
        "description": "Payment terms like Net 30"
    }
}

Design decisions:

invoice_date and due_date: ISO format enables date calculations (days until due, overdue checks)
payment_terms: Keep as string, not parsed days. Terms vary widely (“Net 30”, “2/10 Net 30”, “Due on Receipt”)

Parties (Vendor and Bill-To)

Invoices involve two parties: the vendor (carrier) and the customer (bill-to). Nested objects keep fields organized and separate.

parties_schema = {
    "vendor": {
        "type": "object",
        "properties": {
            "name": {"type": "string"},
            "address": {"type": "string"},
            "phone": {"type": "string"},
            "email": {"type": "string"}
        }
    },
    "bill_to": {
        "type": "object",
        "properties": {
            "company": {"type": "string"},
            "department": {"type": "string"},
            "address": {"type": "string"},
            "city_state_zip": {"type": "string"},
            "account_number": {"type": "string"}
        }
    }
}

Shipment Details

Shipment metadata links the invoice to physical freight movement. Use number types for fields you’ll calculate with.

shipment_schema = {
    "shipment": {
        "type": "object",
        "properties": {
            "pro_number": {"type": "string"},
            "bol_number": {"type": "string"},
            "pickup_date": {"type": "string"},
            "delivery_date": {"type": "string"},
            "service_level": {"type": "string"},
            "total_weight_lbs": {"type": "number"}
        }
    }
}

Why these fields:

pro_number: Carrier’s tracking number, the primary identifier for freight
bol_number: Customer’s bill of lading, for matching invoices to purchase orders
total_weight_lbs: Number type enables rate-per-pound calculations

Charges Array

Here’s the key insight for invoice extraction: charges are a variable-length array. One invoice might have 3 charges (linehaul, fuel, one accessorial), another might have 12 (multiple accessorials, adjustments, fees).

charges_schema = {
    "charges": {
        "type": "array",
        "description": "All freight charges including linehaul, fuel, and accessorials",
        "items": {
            "type": "object",
            "properties": {
                "charge_type": {"type": "string"},
                "description": {"type": "string"},
                "amount": {"type": "number"}
            }
        }
    },
    "subtotal": {"type": "number"},
    "tax": {"type": "number"},
    "total_amount": {"type": "number"}
}

Why array extraction matters: Without array extraction, you’d define charge_1, charge_2, charge_3 and hope you have enough fields. If the invoice has 5 charges, you miss two. If it has 2 charges, you have empty fields. Array extraction identifies the repeating pattern (charge rows in a table) and extracts all instances automatically. Enable it with array_extract: True in settings.

Complete Extraction

Combine all schema sections and run extraction:

from reducto import Reducto

client = Reducto()

# Upload the invoice
with open("freight-invoice.pdf", "rb") as f:
    upload = client.upload(file=f)

# Complete schema combining all sections
invoice_schema = {
    "type": "object",
    "properties": {
        "invoice_number": {
            "type": "string",
            "description": "Invoice number from the header"
        },
        "invoice_date": {
            "type": "string",
            "description": "Invoice date in YYYY-MM-DD format"
        },
        "due_date": {
            "type": "string",
            "description": "Payment due date"
        },
        "payment_terms": {
            "type": "string",
            "description": "Payment terms like Net 30"
        },
        "vendor": {
            "type": "object",
            "properties": {
                "name": {"type": "string"},
                "address": {"type": "string"},
                "phone": {"type": "string"},
                "email": {"type": "string"}
            }
        },
        "bill_to": {
            "type": "object",
            "properties": {
                "company": {"type": "string"},
                "department": {"type": "string"},
                "address": {"type": "string"},
                "city_state_zip": {"type": "string"},
                "account_number": {"type": "string"}
            }
        },
        "shipment": {
            "type": "object",
            "properties": {
                "pro_number": {"type": "string"},
                "bol_number": {"type": "string"},
                "pickup_date": {"type": "string"},
                "delivery_date": {"type": "string"},
                "service_level": {"type": "string"},
                "total_weight_lbs": {"type": "number"}
            }
        },
        "charges": {
            "type": "array",
            "description": "All freight charges including linehaul, fuel, and accessorials",
            "items": {
                "type": "object",
                "properties": {
                    "charge_type": {"type": "string"},
                    "description": {"type": "string"},
                    "amount": {"type": "number"}
                }
            }
        },
        "subtotal": {"type": "number"},
        "tax": {"type": "number"},
        "total_amount": {"type": "number"}
    }
}

# Extract with array extraction enabled
result = client.extract.run(
    input=upload.file_id,
    instructions={"schema": invoice_schema},
    settings={"array_extract": True}
)

invoice_data = result.result[0]
print(f"Invoice: {invoice_data['invoice_number']}")
print(f"Total: ${invoice_data['total_amount']}")

The array_extract: True setting optimizes extraction for documents with repeating structures like invoice line items. It improves accuracy and ensures all charges are captured regardless of how many appear.

Extraction Results

When you run extraction on the sample invoice, you get:

{
  "invoice_number": "ANF-INV-2026-004918",
  "invoice_date": "2026-01-12",
  "due_date": "2026-02-11",
  "payment_terms": "Net 30",
  "vendor": {
    "name": "Atlas National Freight",
    "address": "4500 Logistics Parkway, Memphis, TN 38118",
    "phone": "(901) 555-7421",
    "email": "[email protected]"
  },
  "bill_to": {
    "company": "Acme Manufacturing, Inc.",
    "department": "Accounts Payable Dept.",
    "address": "1234 Industrial Parkway",
    "city_state_zip": "Columbus, OH 43215",
    "account_number": "ACME-44721"
  },
  "shipment": {
    "pro_number": "778345921",
    "bol_number": "FCC-2026-001234",
    "pickup_date": "2026-01-08",
    "delivery_date": "2026-01-10",
    "service_level": "LTL – Standard",
    "total_weight_lbs": 5200
  },
  "charges": [
    {
      "charge_type": "Linehaul",
      "description": "LTL Freight – OH → TX",
      "amount": 1425.00
    },
    {
      "charge_type": "Fuel Surcharge",
      "description": "Based on DOE Index (18%)",
      "amount": 256.50
    },
    {
      "charge_type": "Accessorial",
      "description": "Liftgate Pickup",
      "amount": 75.00
    },
    {
      "charge_type": "Accessorial",
      "description": "Appointment Delivery",
      "amount": 45.00
    },
    {
      "charge_type": "Accessorial",
      "description": "Limited Access – Warehouse",
      "amount": 85.00
    }
  ],
  "subtotal": 1886.50,
  "tax": 0.00,
  "total_amount": 1886.50
}

Tracing Values with Citations

In accounts payable, disputes arise: “Where did this $256.50 fuel surcharge come from?” Citations let you point to the exact location in the source document. Enable citations via the settings parameter:

result = client.extract.run(
    input=upload.file_id,
    instructions={"schema": invoice_schema},
    settings={
        "array_extract": True,
        "citations": {"enabled": True}
    }
)

# Each field includes citation info
invoice_data = result.result[0]
for field, data in invoice_data.items():
    if isinstance(data, dict) and "citation" in data:
        print(f"{field}: {data['value']} (page {data['citation']['page']})")

Each extracted value includes its source location:

{
  "invoice_number": {
    "value": "ANF-INV-2026-004918",
    "citation": {
      "page": 1,
      "bbox": [0.12, 0.08, 0.35, 0.11]
    }
  }
}

When to use citations:

Audit trails: Show auditors exactly where each charge originated
Dispute resolution: Link a questioned fee back to its source location
Quality validation: Spot-check extractions by comparing values to their highlighted source

Best Practices

Handle Multiple Invoice Formats

Different carriers use different invoice layouts. Make your schema robust with descriptive field hints:

# Use descriptions that guide extraction across formats
schema = {
    "type": "object",
    "properties": {
        "total_amount": {
            "type": "number",
            "description": "Total amount due, usually at bottom of invoice near payment instructions"
        },
        "invoice_number": {
            "type": "string",
            "description": "Invoice number or invoice ID from the header area"
        }
    }
}

# Add a system prompt for extra context
result = client.extract.run(
    input=upload.file_id,
    instructions={
        "schema": schema,
        "system_prompt": "This is a freight invoice. Extract billing and shipment details."
    }
)

Validate Extracted Totals

Invoices have internal consistency: line items should sum to the subtotal. Use this for quality checks:

invoice_data = result.result[0]

# Sum all charges
calculated_total = sum(charge["amount"] for charge in invoice_data["charges"])

# Compare to extracted subtotal
if abs(calculated_total - invoice_data["subtotal"]) > 0.01:
    print(f"Warning: Charges sum to ${calculated_total:.2f}, but subtotal is ${invoice_data['subtotal']:.2f}")
else:
    print("Validation passed: charges match subtotal")

A mismatch might indicate:

Missing charges in extraction (increase specificity in descriptions)
OCR errors on amounts (check the source document)
Hidden fees not in the main charges table

Next Steps

Array Extraction

Configure extraction for repeating items

Citations

Track where extracted values come from

Extract Best Practices

Schema design tips

Batch Processing

Process thousands of invoices

Cookbooks

Document Examples

Invoice & Accounts Payable Automation

Sample Document

Create API Key

Studio Walkthrough

API Implementation

Building the Schema

Invoice Header

Parties (Vendor and Bill-To)

Shipment Details

Charges Array

Complete Extraction

Extraction Results

Tracing Values with Citations

Best Practices

Handle Multiple Invoice Formats

Validate Extracted Totals

Next Steps

Array Extraction

Citations

Extract Best Practices

Batch Processing

Cookbooks

Document Examples

​Sample Document

​Create API Key

​Studio Walkthrough

​API Implementation

​Building the Schema

​Invoice Header

​Parties (Vendor and Bill-To)

​Shipment Details

​Charges Array

​Complete Extraction

​Extraction Results

​Tracing Values with Citations

​Best Practices

​Handle Multiple Invoice Formats

​Validate Extracted Totals

​Next Steps

Array Extraction

Citations

Extract Best Practices

Batch Processing

Sample Document

Create API Key

Studio Walkthrough

API Implementation

Building the Schema

Invoice Header

Parties (Vendor and Bill-To)

Shipment Details

Charges Array

Complete Extraction

Extraction Results

Tracing Values with Citations

Best Practices

Handle Multiple Invoice Formats

Validate Extracted Totals

Next Steps