top of page

Agentic-AI Powered Dealer Engagement & Loyalty Transformation for Leading Tile Manufacturer 

Discover how DGTL and Emvo.ai implemented Agentic AI for a leading tile manufacturer to automate dealer collections, product enablement, and loyalty programs—driving higher recovery, engagement, and ROI. Read the full case study.

Gemini_Generated_Image_o7fonto7fonto7fo.png

Company Overview

A leading tile manufacturing company in India with a vast and diverse dealer network, the organization operates at scale across multiple regions, managing high-volume transactions and complex financial workflows. With strong market presence and established distribution channels, dealer collections play a critical role in maintaining cash flow efficiency—making it essential to streamline recovery processes and ensure consistent, timely engagement across the network.

Business Challenge  

Despite strong systems in place, the collections process faced multiple operational bottlenecks:

1. Manual Data Dependencies

  • Reliance on CSV exports and spreadsheets

  • Delayed workflows due to manual data processing

  • Increased risk of human errors in tracking outstanding amounts

2. Inconsistent Follow-Ups

  • Human agents struggled to track payment commitments

  • Missed follow-ups led to delayed recoveries

  • No system-driven accountability for dealer promises

3. Limited Scalability

  • Teams were unable to reach all dealers consistently

  • High-value accounts were not always prioritized effectively

  • Revenue leakage due to uncontacted overdue accounts

4. Disconnected Ecosystem

  • Lack of coordination between finance teams and field sales

  • Manual communication for cheque pickups and updates

  • No unified system to close the loop between data and action

Objective 

To build an intelligent, scalable, and autonomous collections ecosystem that can:

  • Eliminate manual dependencies in recovery workflows

  • Ensure 100% dealer coverage for overdue follow-ups

  • Track commitments and trigger timely actions

  • Improve collection efficiency while reducing operational effort

  • Create a connected system between finance, dealers, and field teams

The Solution  - DGTL's Agentic AI Approach

DGTL in association with Emvo.ai implemented a multi-agent AI architecture, where each agent performed a specialized function while working in sync to create a seamless collection workflow.

Multi-Agent Collection System

​

1. Data Analyst Agent – Intelligence Layer

  • Integrated directly with SAP via APIs

  • Pulled real-time ledger data and outstanding amounts

  • Prioritized dealers based on overdue value and risk

  • Eliminated dependency on manual data extraction

​

2. Collection Calling Agent – Execution Layer

  • Initiated AI-driven outbound voice calls at scale

  • Engaged dealers in natural, human-like conversations

  • Negotiated payment timelines and handled objections

  • Operated in Hinglish, adapting to regional communication styles

  • Ensured every dealer interaction was tracked and recorded

​

3. Internal Communication Agent – Coordination Layer

  • Sent automated email nudges to field sales teams

  • Coordinated cheque pickups and follow-up actions

  • Monitored communication loops and updated statuses

  • Closed the gap between finance teams and on-ground execution

Key Features 

  • Real-Time SAP Integration for accurate financial grounding

  • High-Volume Calling Infrastructure for scalability

  • WhatsApp Automation for post-call summaries and confirmations

  • Conversation Memory to track past commitments and trigger follow-ups

  • Real-Time Interruption Handling for natural conversations

  • Zero Hallucination Framework ensuring only verified financial data is used

Performance & Result

30%

Increase in Collections

100%

Dealer Coverage 

6+

Hours  Saved Daily

Zero

Context Loss

The implementation transformed collections from a manual, reactive process into a proactive, intelligent, and autonomous system. What was once dependent on human effort is now driven by data, intelligence, and continuous engagement, ensuring faster recoveries and stronger financial control.

Conclusion 

This engagement wasn’t just about automation—it was about redefining how dealers experience the brand. Through Agentic AI, DGTL helped the organization move from follow-ups to foresight, from reminders to relationships, and from fragmented workflows to a unified, intelligent dealer ecosystem.

Agentic AI didn’t replace teams—it amplified outcomes.

bottom of page