AI comparison and reconciliation

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The AI comparison and reconciliation pattern uses TEXT_UNDERSTANDING nodes as comparison agents to perform field-by-field validation between AI-extracted data and expected values from a system of record. The output is a structured exception report that drives downstream routing: auto-approve, human review, or reject.

When to use

Use this pattern when you need to:

Validate AI-extracted document data against a trusted source (TMS, ERP, core banking system, database)
Produce auditable, structured comparison results with per-field match status
Route exceptions based on severity and overall match rate
Replace manual spot-checking of document extraction results

This pattern typically follows the fan-out extraction pattern in a document processing pipeline.

Architecture

The workflow accepts two data sets as input, runs them through a TEXT_UNDERSTANDING comparison agent, and routes the result based on the exception report.

Extracted Data + System Data
        |
        v
 TEXT_UNDERSTANDING (compare)
        |
        v
   Exception Report
        |
        v
   Condition (severity routing)
      /       |       \
     v        v        v
Auto-approve  Human   Reject
              review

Node breakdown:

Node	Type	Purpose
Compare	TEXT_UNDERSTANDING	Field-by-field comparison of extracted vs. expected values
Route	Condition	Branch based on overall match rate and exception severity
Auto-approve	End / next step	High-match documents proceed without intervention
Human review	End / next step	Medium-match documents queued for manual review
Reject	End / next step	Low-match documents flagged for rejection

Implementation

Input structure

The comparison node receives two objects: the AI-extracted values and the system-of-record values.

{
  "extractedData": {
    "shipperName": "Acme Logistics Ltd.",
    "consigneeName": "Beta Corp",
    "totalWeight": "14500",
    "weightUnit": "KG",
    "containerCount": 3,
    "bolNumber": "MSKU-2024-78432"
  },
  "systemData": {
    "shipperName": "Acme Logistics",
    "consigneeName": "Beta Corporation",
    "totalWeight": "14500",
    "weightUnit": "KG",
    "containerCount": 3,
    "bolNumber": "MSKU-2024-78432"
  }
}

Comparison prompt

Configure the TEXT_UNDERSTANDING node with a prompt that instructs the LLM to perform structured comparison.

You are a document reconciliation agent. Compare the AI-extracted values against
the system-of-record values and produce a structured exception report.

Instructions:
1. Compare each field individually. Use fuzzy matching for names and addresses
   (e.g., "Acme Logistics Ltd." vs "Acme Logistics" is a MATCH).
2. For numeric fields, compare exact values. Flag unit mismatches separately.
3. For derived values (e.g., total weight = sum of line items), verify the
   calculation independently.
4. Compute an overall match rate as a percentage (0-100).
5. Assign a confidence score (0-100) reflecting how certain you are in the
   comparison results.
6. Flag each exception with a severity: CRITICAL, WARNING, or INFO.

Extracted data:
{{extractedData}}

System-of-record data:
{{systemData}}

Output schema

Define the output schema on the TEXT_UNDERSTANDING node to enforce structured results.

{
  "type": "object",
  "properties": {
    "matchRate": {
      "type": "number",
      "description": "Overall match rate from 0 to 100"
    },
    "confidenceScore": {
      "type": "number",
      "description": "Confidence in comparison accuracy from 0 to 100"
    },
    "fieldResults": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "fieldName": { "type": "string" },
          "extractedValue": { "type": "string" },
          "expectedValue": { "type": "string" },
          "status": {
            "type": "string",
            "enum": ["MATCH", "MISMATCH", "MISSING"]
          },
          "severity": {
            "type": "string",
            "enum": ["CRITICAL", "WARNING", "INFO"]
          },
          "note": { "type": "string" }
        },
        "required": ["fieldName", "status"]
      }
    },
    "exceptions": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "fieldName": { "type": "string" },
          "description": { "type": "string" },
          "severity": {
            "type": "string",
            "enum": ["CRITICAL", "WARNING", "INFO"]
          }
        },
        "required": ["fieldName", "description", "severity"]
      }
    }
  },
  "required": ["matchRate", "confidenceScore", "fieldResults", "exceptions"]
}

Severity routing

Configure a Condition node after the comparison to route based on the exception report.

Route	Condition	Action
Auto-approve	`matchRate >= 95` and no CRITICAL exceptions	Proceed to next processing step
Human review	`matchRate >= 70` and `matchRate < 95`, or any WARNING exceptions	Queue for manual review
Reject	`matchRate < 70` or any CRITICAL exception	Flag for rejection and notify

Adjust the match rate thresholds based on your business requirements. Start conservative (higher thresholds for auto-approve) and relax them as you gain confidence in extraction quality.

Configuration reference

Setting	Value	Description
Node type	TEXT_UNDERSTANDING	Comparison agent
Output format	JSON schema	Structured exception report

Exception severity levels

Severity	Description	Examples
CRITICAL	Data integrity issue that blocks processing	Missing required field, numeric value off by more than 10%, wrong document matched to shipment
WARNING	Discrepancy that requires human judgment	Name variation beyond fuzzy match threshold, date format ambiguity, unit conversion needed
INFO	Minor difference, typically acceptable	Abbreviation vs. full name, trailing whitespace, formatting differences

Variations

Threshold-based routing

Instead of fixed severity categories, use configurable thresholds stored in a FlowX Database data source. This allows business users to adjust auto-approve and reject boundaries without modifying the workflow.

Multi-document cross-reference

Extend the pattern to compare fields across multiple documents in the same shipment. For example, verify that the total weight on the bill of lading matches the sum of weights on the packing list and that both align with the commercial invoice.

Audit trail generation

Add a downstream node that persists the full exception report (including all MATCH results) to a database or document store. This provides a complete audit trail for compliance and quality monitoring over time.

AI patterns overview

All available AI patterns and how to combine them

Fan-out extraction

Classify and extract from multiple document types

Hybrid AI + business rules

Combine AI with deterministic logic for auditable decisions

AI node types

Reference for all AI node types including TEXT_UNDERSTANDING

Cookbooks

Tutorials

AI patterns

AI comparison and reconciliation

When to use

Architecture

Implementation

Input structure

Comparison prompt

Output schema

Severity routing

Configuration reference

Exception severity levels

Variations

Threshold-based routing

Multi-document cross-reference

Audit trail generation

AI patterns overview

Fan-out extraction

Hybrid AI + business rules

AI node types

​When to use

​Architecture

​Implementation

​Input structure

​Comparison prompt

​Output schema

​Severity routing

​Configuration reference

​Exception severity levels

​Variations

​Threshold-based routing

​Multi-document cross-reference

​Audit trail generation

​Related resources

AI patterns overview

Fan-out extraction

Hybrid AI + business rules

AI node types

When to use

Architecture

Implementation

Input structure

Comparison prompt

Output schema

Severity routing

Configuration reference

Exception severity levels

Variations

Threshold-based routing

Multi-document cross-reference

Audit trail generation

Related resources