End-to-End Guide: Building an AI-Augmented Accounts Receivable Triage Pipeline

The Problem with Traditional B2B AR Dashboards

In mid-market manufacturing and distribution across India, cash flow friction rarely stems from an inability to view late payments. Your BI dashboard already highlights that your Days Sales Outstanding (DSO) sits at 68 days and that ₹4.2 crore is locked in the 60+ days bucket. The problem is that static charts do not move money into your bank account. They show you outcomes, not actionable root causes.

When an enterprise buyer delays paying a ₹12 lakh invoice, the root cause is almost never simple recalcitrance. It is typically a subtle operational disconnect: an uncredited debit note, a discrepancy between the physical purchase order and the SAP billing line items, a missing e-way bill, or an unacknowledged GST mismatch in GSTR-2B. Standard ETL pipelines pump raw ledger dumps from Tally Prime or SAP S/4HANA straight into staging tables, leaving financial analysts to spend hours manually emailing buyers, pulling delivery receipts, and cross-checking WhatsApp threads.

In this guide, we will build an end-to-end AR operational engine. We will move beyond passive reporting by engineering a pipeline that ingests transactional ledgers, applies automated dbt modeling to calculate aging metrics, and hooks into an asynchronous LLM agent to classify disputed payments and execute context-aware recovery workflows.

Phase 1: Ingestion and Change Data Capture (CDC) from Enterprise Ledgers

Most Indian enterprises operate on a hybrid backend: core accounting running on Tally Prime in regional warehouses, with SAP or Oracle handling central finance. To build a unified AR view, we need a reliable ingestion layer that captures ledger entries and payment updates without locking production tables.

For PostgreSQL-backed finance stores, we leverage continuous logical replication. If you are operating directly against Postgres databases, configuration begins by enabling Change Data Capture via Postgres WAL logs as outlined in the official PostgreSQL Logical Replication documentation. For legacy Tally installations, we deploy an light, local daemon that polls the Tally XML/ODBC interface every 15 minutes, writing state changes into an append-only staging warehouse table (stg_tally_ledger_entries).

Staging Table Schema Architecture

Your staging schema must separate raw transactional events from downstream analytical models. Here is the minimal schema required for incoming invoice and payment events:

CREATE TABLE stg_raw_ar_events (
  event_id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  erp_source VARCHAR(32) NOT NULL, -- 'SAP', 'TALLY', 'ZOHO'
  invoice_number VARCHAR(64) NOT NULL,
  customer_id VARCHAR(64) NOT NULL,
  invoice_date DATE NOT NULL,
  due_date DATE NOT NULL,
  total_amount NUMERIC(15, 2) NOT NULL,
  balance_outstanding NUMERIC(15, 2) NOT NULL,
  currency VARCHAR(3) DEFAULT 'INR',
  last_modified_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()
);

Phase 2: Modeling Aging Buckets and Discrepancies with dbt

Once raw transactions land in your warehouse, dbt transforms raw rows into standardized aging buckets. Rather than relying on rigid Power BI DAX measures that calculate aging dynamically at query time (slaughtering visual performance on large datasets), we materialise daily snapshot views of customer balances.

We define standard aging buckets: Current, 1-30 Days, 31-60 Days, 61-90 Days, and 90+ Days. However, the critical addition is the Discrepancy Flag. A dbt model joins invoice lines with credit notes and pending support tickets to isolate invoices where unpaid balances stem from line-item rejections rather than standard payment terms.

Sample dbt Transformation snippet:

WITH base_invoices AS (
  SELECT
    invoice_number,
    customer_id,
    due_date,
    balance_outstanding,
    CURRENT_DATE - due_date AS days_overdue
  FROM {{ ref('stg_raw_ar_events') }}
  WHERE balance_outstanding > 0
)
SELECT
  invoice_number,
  customer_id,
  balance_outstanding,
  days_overdue,
  CASE
    WHEN days_overdue <= 0 THEN 'Current'
    WHEN days_overdue BETWEEN 1 AND 30 THEN '1-30 Days'
    WHEN days_overdue BETWEEN 31 AND 60 THEN '31-60 Days'
    WHEN days_overdue BETWEEN 61 AND 90 THEN '61-90 Days'
    ELSE '90+ Days Overdue'
  END AS aging_tier
FROM base_invoices;

Phase 3: Injecting the AI Triage Agent

This is where standard BI ends and automated operational analytics begins. When an invoice crosses 15 days overdue, or when a customer emails saying "payment held due to rate variance", a traditional system relies on a finance clerk to read the email, log into the ERP, and inspect the PO. If you want us to evaluate and automate your current ledger workflow, our Systems Audit & Blueprint engagement maps out these exact operational bottleneck points and designs your custom agentic pipeline.

We place an asynchronous worker—powered by Python and an LLM agent framework—on a queue. When an invoice is flagged as overdue or disputed, the worker executes three concrete tasks:

• Context Retrieval: It pulls the original PDF purchase order, the generated ERP invoice, and the latest email/WhatsApp thread with the customer account team using vector embeddings or direct key lookup.
• Dispute Classification: It analyzes the communication history to categorize the blocker into structured tags: MISSING_EWAY_BILL, QUANTITY_DISCREPANCY, UNAPPLIED_CREDIT_NOTE, or LIQUIDITY_DELAY.
• Draft Action Execution: If tagged as MISSING_EWAY_BILL, it queries the GST portal staging table, extracts the public PDF link, and drafts an outbound email for the AR team lead to approve with a single click.

Python Worker Snippet for Exception Triage

import os
from json import loads
from openai import OpenAI

client = OpenAI(api_key=os.environ.get(

Static dashboards show you that an invoice is 45 days overdue; an agentic AR pipeline reads the email trail, detects that the buyer rejected line-item three due to a missing e-way bill, and drafts the correction before your finance lead even opens their morning report.