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JK Case Study

ML-driven agentic platform: How JK Cement is building the future of dealership engagement

01
Driving loyalty with secure incentives.

About the customer

Building companionship between dealership team and
machine learning

JK Cement, one of India’s leading cement manufacturers, is gearing up to join hands with machine learning. With its strong nationwide dealership network, the company is embarking on a bold digital journey to modernise and transform its sales operations.

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Challenges

Identifying gaps that limit growth, decisions, and customer satisfaction

The cement industry operates on high-volume transactions and complex dealer networks. For JK Cement, dealer engagement is the backbone of growth. However, The sales process was hindered by the following reasons:
1. Validating dealer data via multiple processes and resources
2. Manual profiling before dealer visits, and
3. Limited visibility into financial and operational risks.

These challenges translated into lost revenue opportunities, slower decision-making, and reduced customer satisfaction.

  • 01 Dealership management team struggled to manage dealer information fragmented across multiple systems. While Salesforce manages dealership operations and transactional data, SAP handles invoicing and payouts, this information is not available in a unified, contextual format for sales executives.
  • 02 The goal is to empower dealership management team with a unified dealership intelligence platform using advanced machine learning and Explainable AI.
  • 03 This solution will deliver a 360° dealer view with actionable recommendations tailored to each region, enabling sales executives to enhance dealer engagement and boost overall sales productivity.
Team reviewing ML-driven sales operations solution.
Team reviewing ML-driven sales operations solution.

Solution

Strengthening dealership team for operational excellence

Building a Machine Learning-Driven Agentic platform leveraging ML, Explainable AI, and Agentic AI to transform JK Cement’s sales operations. This platform will shift sales from reactive to proactive, insight-led engagement, driving efficiency and growth.

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Building ML‑Explainable AI pipelines

Established a scalable intelligence layer by unifying Salesforce and SAP data in BigQuery, enabling real‑time machine learning without silos or complex pipelines. The team built three core models: Dealer Risk Prediction, Financial Risk Assessment, and Visit Recommendation. It helps sales teams identify risk, spot cross‑sell opportunities, and prioritise visits with clear, actionable guidance. With Explainable AI embedded throughout, every prediction comes with transparent reasoning and bias checks, ensuring trust, adoption, and more informed decision‑making across the dealer network.

    Industrial pipelines extending into the sea at sunset.
    Team members stacking hands over a puzzle piece.

    Conversational AI assistant

    We built a reasoning‑driven Conversational AI assistant using Vertex AI to make complex enterprise insights instantly accessible. Moving beyond basic chatbots, it acts as an analytical partner, pulling data from systems like Salesforce and SAP to deliver real‑time performance insights and proactive recommendations. Vertex AI enables this scale and intelligence by providing a unified, governed platform for building, deploying, and integrating advanced conversational models.

    Designing adaptive and engaging user interactions

    The Dealer intelligence platform was built around one principle: make AI invisible yet impactful. Intelligence is embedded directly into workflows so insights surface naturally, not as an extra task. With secure Google ID authentication, a minimal, low‑clutter interface, and contextual recommendations that appear exactly where decisions happen, the experience stays effortless and intuitive. Flexible Map and Table views further adapt to user preferences, ensuring both control and personalisation.

    Team analyzing UX data and interface designs.

    Scroll

    Building ML‑Explainable AI pipelines

    Established a scalable intelligence layer by unifying Salesforce and SAP data in BigQuery, enabling real‑time machine learning without silos or complex pipelines. The team built three core models: Dealer Risk Prediction, Financial Risk Assessment, and Visit Recommendation. It helps sales teams identify risk, spot cross‑sell opportunities, and prioritise visits with clear, actionable guidance. With Explainable AI embedded throughout, every prediction comes with transparent reasoning and bias checks, ensuring trust, adoption, and more informed decision‑making across the dealer network.

      Psych X86
      Psych X86

      Conversational AI assistant

      We built a reasoning‑driven Conversational AI assistant using Vertex AI to make complex enterprise insights instantly accessible. Moving beyond basic chatbots, it acts as an analytical partner, pulling data from systems like Salesforce and SAP to deliver real‑time performance insights and proactive recommendations. Vertex AI enables this scale and intelligence by providing a unified, governed platform for building, deploying, and integrating advanced conversational models.

      Designing adaptive and engaging user interactions

      The Dealer intelligence platform was built around one principle: make AI invisible yet impactful. Intelligence is embedded directly into workflows so insights surface naturally, not as an extra task. With secure Google ID authentication, a minimal, low‑clutter interface, and contextual recommendations that appear exactly where decisions happen, the experience stays effortless and intuitive. Flexible Map and Table views further adapt to user preferences, ensuring both control and personalisation.

      Psych X86
      Psych X86
      Diagram of data, models, and AI for dealer intelligence.
      Psych X86

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