Accounts Receivable Analytics with Agentic ELT

Overview

Dataplatr’s Agentic ELT (AELT) framework enables accounts receivable automation by using AI-driven ELT pipelines on the Databricks Lakehouse. The framework is designed to build, validate, and deploy Accounts Receivable data pipelines using LLM-powered data agents combined with human-in-the-loop (HITL) validation. It supports core AR data domains including invoices, payments, credit memos, customer master data, and AR aging, while ensuring transparency, auditability, and governance throughout the data lifecycle.

At the analytics layer, Dataplatr’s Accounts Receivable AI Agent operates on curated Gold tables to monitor key AR indicators such as aging buckets, delinquency signals, and promise-to-pay status. Based on predefined rules and approvals, the agent can trigger downstream actions like reminders, collector tasks, or escalation workflows. This approach enables continuous monitoring and operationalization of AR data while reducing manual effort and keeping finance teams in control of compliance and decision-making.

Business Challenge & Solution

Organizations face two major challenges in AR analytics:

1. Technical complexity- Building reliable AR aging tables requires careful reconciliation of invoices, payments, adjustments, credit memos, write-offs, and aging logic. Manual SQL and spreadsheets introduce inconsistency AR views especially across platforms like Oracle Accounts Receivable or legacy ERP systems.

2. Functional fragmentation - Different definitions of buckets, delinquency, and DSO across departments lead to inaccurate reporting and unreliable cash-flow forecasts.

Architecture Overview – Medallion + Agentic Orchestration

1. Bronze (L0): Raw data ingestion (Customer Master,Invoice, Payments).
2. Silver (L1–L2): AI agents automatically generate standardized SQL, enforce incremental load logic, and validate primary keys using Delta Live Tables (DLT).
3. Gold (L3): Conversational agent assists users in defining business tables such as AR Transaction Summary and Aging through natural language, validating and deploying DLT SQL automatically.

Pipeline Flow

1. Metadata Enricher Agent (Bronze): Ingests raw AR invoice, vendor, and payment files from sources and automatically documents schemas, column meanings, invoice attributes, and vendor metadata using AI-generated descriptions, with optional HITL validation for accuracy.
2. Silver SQL Generator Agent: Creates standardized L1 views for invoices, vendors, and payments, and generates L2 incremental DLT SQL pipelines with built-in invoice key detection, update/delete handling, exception tagging, and quality rules for dates, amounts, and vendor IDs.
3. Conversational Gold Agent: Allows AR teams to build advanced Invoice Analytics tables simply by describing them (“Create an Invoice Aging bucket summary” or “Build a vendor-wise cycle time table”). The agent validates logic, fixes SQL errors automatically, and ensures consistency across AR metrics.
4. DLT Execution: A single Databricks Delta Live Tables pipeline constructs the complete Accounts Receivable Automation lifecycle, seamlessly linking L1, L2, and L3 layers while providing full data lineage, change tracking, and auditability.

AR Functional Insights (Gold Layer)

Once the agentic pipeline is deployed, the accelerator delivers ready-to-use insights:

1. AR Aging Intelligence - Provides consolidated visibility into open receivables across invoices, credit memos, and payments, with aging views by customer, region, segment, and account owner. Includes automated delinquency scoring for prioritised collections.
2. DSO (Days Sales Outstanding) - Tracks DSO trends with month-over-month and year-over-year comparisons. Supports customer-level and product-level DSO analysis, along with monitoring monthly aging drift and payment delays.

3. Collections & Payment Behavior - Surfaces early vs. late payment patterns, identifies broken promises-to-pay, and flags high-risk or escalating accounts for proactive collection strategies.ale

Dashboard Widgets

AR Aging Overview

1. Aging Trend Over Time – Shows month-over-month movement in AR aging buckets, highlighting shifts in overdue balances and emerging risk segments.
2. Top Delinquent Customers – Identifies customers with the highest overdue amounts to help prioritise collection efforts and optimise recovery strategies.

Accounts Receivable Analytics (AR) Transaction Summary

1. Total Receivables Overview – Displays total outstanding AR, payments received, and pending invoices.
2. Customer-Wise Receivables – Highlights customers with the highest outstanding balances or delayed payments.
3. Invoice Aging Summary – Breaks down receivables into aging buckets (0–30, 31–60, 61–90, 90+ days).
4. Payment Status Analysis – Tracks invoices that are paid, partially paid, or overdue.
5. Collections Performance – Monitors high-risk accounts, broken promises-to-pay, and collection effectiveness.
6. AR Trend Analysis – Visualizes monthly or quarterly changes in receivables and DSO (Days Sales Outstanding).

Key Capabilities

1. LLM-Driven Metadata Enrichment for all AR entities, automatically generating business-friendly definitions for invoices, vendors, credit memos, and payments.
2. Automated Incremental SQL Generation across Invoice, Vendor, and Payment models with built-in AR logic such as due dates, net terms, and outstanding balance rules.
3. End-to-End DLT Orchestration, including auto-mapped dependencies, schema propagation, and DAG creation for the complete Accounts Receivable Automation lifecycle.
4. Conversational Analytics Creation, enabling finance teams to build AR Gold Tables simply by describing insights (“Create an AR Accelerator aging bucket summary”).
5. Self-Healing SQL Engine that auto-corrects AR transformations, fixes schema changes, and validates business rules without manual intervention.
6. CDC-Enabled Processing to support near-real-time updates of invoice statuses, payments, adjustments, and aging calculations.

Marketplace Deliverables

1. Production-Ready Notebooks: End-to-end AELT pipeline notebooks for Bronze (Ingestion), Silver (Transformation), and Gold (Analytics) with agent-assisted automation.
2. Config Templates: JSON mappings for AR objects with AI-assisted metadata generation.
3. DLT SQL Files: Automatically generated and validated SQL for incremental tables and AR Aging views.
4. CDC & Data Quality Frameworks: Out-of-the-box components for real-time ingestion and data validation.
5. Documentation & Implementation Guide: Step-by-step Databricks setup, object mapping, and AELT deployment manual.

Why Choose Dataplatr’s AR Agentic ELT Accelerator

1. Agentic Automation: Removes manual mapping and SQL writing
2. Rapid Time-to-Value: From raw invoices to dashboards in hours, not weeks
3. Deep AR Logic: Prebuilt cycle time, aging, matching, vendor scoring models
4. Governance: Unity Catalog lineage, audit, and metadata
5. Built for Databricks: Lakehouse-native, scalable, secure

Transform AR Analytics with Agentic ELT
Deliver accurate aging, reliable DSO, and automated collections intelligence all powered by AI + Databricks.
For a demo or hands-on notebook, feel free to reach out.

Contact us at: [email protected]
For consultations or custom inquiries: https://dataplatr.com/contact-us