The accounts payable automation landscape just got a major upgrade. Aito's latest releases introduce game-changing capabilities that solve one of the most persistent challenges in invoice processing: making accurate predictions when you have limited historical data.

Whether you're dealing with a new vendor, an unfamiliar expense category, or a recently hired employee, traditional ML approaches often struggle. Today, we're excited to show you how Aito's enhanced prediction capabilities automatically leverage entity properties and enterprise-scale performance optimization change the game entirely.

The Challenge: Making Smart Predictions with Limited Context

Let's start with a real scenario every accounting team faces:

It's Monday morning, and your automated invoice processing system encounters an invoice from a new cloud services vendor. The system has never seen this vendor before, but it needs to assign a GL code, predict the appropriate approver, and route it to the right department. Traditional rule-based systems would fail here, and most ML models would make random guesses.

This is where Aito's automatic property-based predictions become invaluable.

How Aito Makes Intelligent Predictions for Unknown Entities

Instead of requiring explicit prior configuration, Aito automatically uses the properties of entities to make intelligent predictions. Think of it like an experienced accountant who uses what they know about a vendor's industry, size, and service type to make smart categorization decisions.

Here's how it works in practice:

The Problem: Unknown Vendor Scenario

Your system encounters a new vendor "NewCloudTech Solutions" with no historical data:

{
  "from": "invoices",
  "where": {
    "VendorName": "NewCloudTech Solutions",
    "Amount": 2400,
    "Description": "Monthly cloud infrastructure"
  },
  "predict": "GLCode"
}

Traditional ML: Would make a random guess because this vendor has never been seen before.

The Aito Solution: Automatic Property-Based Intelligence

Instead of requiring manual configuration, Aito automatically leverages vendor properties to make intelligent predictions. If your vendor table includes properties like this:

// Vendor table entry for the new vendor
{
  "VendorName": "NewCloudTech Solutions",
  "Industry": "Technology",
  "ServiceType": "Cloud Infrastructure",
  "CompanySize": "Medium"
}

Aito automatically uses these properties to find similar vendors and their GL code patterns. The prediction query remains simple:

{
  "from": "invoices", 
  "where": {
    "VendorName": "NewCloudTech Solutions",
    "Amount": 2400,
    "Description": "Monthly cloud infrastructure",
    "VendorIndustry": "Technology",
    "VendorServiceType": "Cloud Infrastructure"
  },
  "predict": "GLCode"
}

Result: Aito automatically uses all fields from the where clause (VendorName, Amount, Description, VendorIndustry, VendorServiceType) to find similar patterns in historical data, achieving 95%+ accuracy even for completely new vendors.

Real-World Impact: From Posti's 7,000 Monthly Invoices

Let's examine how these improvements transform high-volume invoice processing, using insights from our customer Posti's automation success:

Scenario 1: New Employee Onboarding

Challenge: Bob, a new IT manager, joins the company. He has zero historical invoice approvals.

Traditional approach: All invoices default to a senior manager, creating bottlenecks.

Aito's automatic approach: Uses Bob's employee properties to make intelligent assignments. If your employee table has:

// Employee record for Bob
{
  "Email": "bob@company.com",
  "Role": "Manager", 
  "Department": "IT",
  "ApprovalLimit": 10000,
  "Seniority": "Mid-level"
}

The prediction query automatically leverages these properties:

{
  "from": "invoices",
  "where": {
    "Department": "IT",
    "Amount": 3500,
    "InvoiceType": "Software License"
  },
  "predict": "Approver"
}

Result: Aito automatically uses all fields from the where clause (Department, Amount, InvoiceType) to find similar patterns in historical data. The system learns from how other IT managers with similar roles and approval limits have been assigned invoices, ensuring Bob gets appropriate assignments despite having no historical data.

Scenario 2: Seasonal Expense Categories

Challenge: Holiday season brings unique expenses (catering, gifts, events) that appear infrequently.

Aito's approach: Automatically uses descriptive keywords and timing to find similar expense patterns:

{
  "from": "invoices",
  "where": {
    "Description": "Holiday catering services",
    "Month": "December", 
    "Amount": 1200,
    "VendorCategory": "Food Service"
  },
  "predict": "GLCode"
}

Outcome: Aito automatically uses all the fields from the where clause (Description, Month, Amount, VendorCategory) to find similar patterns in historical data, matching this to similar event-related expenses from previous years and other catering services, achieving 99.2% accuracy on seasonal expenses compared to 60% with rule-based systems.

Enterprise Scale Performance: Processing Millions of Invoices

The June 2025 performance optimization specifically targets large-scale financial operations:

Performance Benchmarks

• Query Response Time: 120-150ms per prediction
• Concurrent Processing: 1,000+ simultaneous invoice predictions
• Dataset Scale: Optimized for 10+ million invoice records
• Memory Efficiency: 40% reduction in resource usage for linked table operations

What This Means for Your Accounting Team

Before optimization: Processing 3,000 invoices took 15-20 minutes After optimization: Same workload completes in under 5 minutes

// Batch processing multiple predictions simultaneously
{
  "queries": [
    {
      "from": "invoices",
      "predict": "GLCode",
      "where": {"InvoiceID": "INV-001"}
    },
    {
      "from": "invoices", 
      "predict": "Approver",
      "where": {"InvoiceID": "INV-001"}
    },
    {
      "from": "invoices",
      "predict": "PaymentTerms",
      "where": {"InvoiceID": "INV-001"}
    }
  ]
}

Advanced Statistical Analysis for Financial Intelligence

The new $aggregate endpoint enables sophisticated financial analytics directly within your prediction workflows:

Monthly Spending Pattern Analysis

{
  "from": "invoices",
  "select": {
    "Department": "Department",
    "AvgAmount": {"$mean": "Amount"},
    "TotalSpend": {"$sum": "Amount"},
    "InvoiceCount": {"$count": "*"}
  },
  "where": {
    "Date": {"$gte": "2025-01-01"}
  },
  "groupBy": ["Department"]
}

Prediction Confidence Scoring

{
  "from": "invoices",
  "select": {
    "PredictedGLCode": "GLCode",
    "Confidence": {"$probability": "GLCode"}
  },
  "where": {
    "VendorName": "TechCorp Solutions"
  }
}

Implementation Guide: Getting Started in 15 Minutes

Here's how to implement contextual GL code prediction for your invoice processing:

Step 1: Prepare Your Data Structure

{
  "invoices": [
    {
      "InvoiceID": "INV-2025-001",
      "VendorName": "Office Supplies Inc",
      "Amount": 450.00,
      "Description": "Printer paper and toner",
      "Department": "Operations",
      "GLCode": "6400-Office-Supplies",
      "ApprovalRequired": true
    }
  ]
}

Step 2: Make Intelligent Predictions for New Vendors

{
  "from": "invoices",
  "where": {
    "VendorName": "New Office Vendor",
    "Description": "Desk supplies and equipment",
    "VendorCategory": "Office Supplies",
    "Amount": 450
  },
  "predict": "GLCode"
}

Step 3: Handle Edge Cases with Confidence Thresholds

# Python SDK example
result = aito.predict(
    from_table="invoices",
    where={"VendorName": "Unknown Vendor", "Description": "Miscellaneous expense", "Amount": 150},
    predict="GLCode"
)

if result.confidence > 0.85:
    # Auto-assign GL code
    process_automatically(result.prediction)
else:
    # Route for manual review
    queue_for_review(result.prediction, result.confidence)

Beyond GL Codes: Multi-Field Invoice Intelligence

The real power emerges when predicting multiple fields simultaneously. Aito automatically uses vendor and invoice properties to predict all relevant fields:

{
  "from": "invoices", 
  "where": {
    "VendorName": "CloudTech Solutions",
    "Amount": 5000,
    "Description": "Monthly SaaS subscription",
    "VendorType": "Software",
    "Department": "Engineering"
  },
  "predict": ["GLCode", "Approver", "PaymentTerms", "TaxCategory"]
}

Result: Aito automatically uses all the where clause fields (VendorName, Amount, Description, VendorType, Department) to find patterns across the historical data, achieving 98%+ accuracy across all predicted fields without manual configuration.

ROI Impact: Quantifying the Business Value

Based on implementations across our accounting-focused customers:

Time Savings

• Manual GL coding: 2-3 minutes per invoice
• With contextual predictions: 10-15 seconds per invoice
• Monthly savings: 40-60 hours for teams processing 1,000+ invoices

Accuracy Improvements

• Rule-based systems: 70-80% accuracy
• Traditional ML: 85-90% accuracy
• Aito with automatic property-based predictions: 95-99% accuracy

Cost Reduction

• Reduced manual review: 70% fewer invoices requiring intervention
• Faster processing: 3x improvement in throughput
• Error prevention: 85% reduction in mis-coded expenses

What's Next: The Future of Accounting Intelligence

These prediction enhancements represent just the beginning. Here's what we're seeing in the accounting automation space:

1. Real-time anomaly detection using confidence scoring
2. Predictive cash flow analysis with automatic seasonal pattern recognition
3. Intelligent vendor risk assessment based on payment patterns
4. Automated compliance checking with regulatory context

Getting Started Today

Ready to transform your invoice processing with contextual predictions? Here's how to begin:

1. Try the enhanced predictions with your existing invoice data
2. Download our Invoice Processing Starter Kit with ready-to-use examples
3. Schedule a demo to see these features in action with your specific use cases

The future of accounting automation isn't just about speed—it's about intelligence that adapts, learns, and makes smart decisions even when facing the unknown.

Have questions about implementing contextual predictions for your accounting workflows? Reach out to our team - we're here to help you build the intelligent accounting platform your team deserves.