Mahindra First Choice Wheels transformed its used vehicle pricing business by converting messy, inconsistent data into a strategic asset through AI and machine learning, as explained by Chandra Shekhar Prasad, Head of AI Products & Solutions at Mahindra Group. The company developed a unified pricing engine called Indian Blue Book that leverages AI-driven vehicle inspections and computer vision systems to detect micro-defects, color mismatches, and structural issues with over 95% accuracy. Initially, the pricing model achieved only 80% accuracy, but through continuous feedback loops and iterative retraining cycles, the company dramatically improved performance by collecting data from multiple sources and assigning trust levels to each source based on credibility. Rather than relying on a single monolithic algorithm, Mahindra built a sophisticated approach using multiple specialized models, each aligned to the quality and trust of its underlying data source, allowing the system to dynamically decide which dataset and model to use for generating prices. The company faced significant challenges including noisy data, escalations from banks and NBFCs, and unrealistic expectations of 100% accuracy from stakeholders, but resolved these through human expert feedback combined with model predictions to ground-truth results. A critical breakthrough came from expanding data sources and augmenting the dataset with several lakhs of data points collected from diverse sources, which initially received skepticism but ultimately became the backbone of the pricing engine. The success of this initiative is reflected in the dramatic reduction of escalations from over 400 to approximately 10, demonstrating the real-world impact of data normalization and alignment strategies. Beyond pricing, Mahindra First Choice Wheels implemented AI-based computer vision systems for real-time vehicle inspection, partnered with HRTech startup Amara to enhance employee engagement, and leveraged cloud-based scalable infrastructure to support its operations. The company also associated with CamCom for AI-powered visual inspection solutions, representing a first-of-its-kind partnership in India for the used car market. This comprehensive AI strategy demonstrates how balancing data trust strategy, multiple specialized models, dynamic retraining, and human feedback creates reliable, scalable solutions that drive both financial returns and organizational learning in the competitive used vehicle market.
Why it matters:
- Demonstrates how AI and data strategy transform business operations in the used car market, reducing pricing discrepancies and escalations significantly
- Reveals the importance of continuous feedback loops, multi-source data integration, and human expertise in building reliable AI systems rather than relying on single algorithms
Key Points
- Mahindra First Choice Wheels built Indian Blue Book, a unified AI-driven pricing engine that improved from 80% to significantly higher accuracy through iterative feedback loops
- The company uses multiple specialized models with assigned trust levels for different data sources rather than a single monolithic algorithm
- Escalations from banks and NBFCs were reduced from 400+ to approximately 10 through data normalization and alignment strategies
- AI-powered computer vision systems detect micro-defects and structural issues with 95%+ accuracy, outperforming human inspectors
- Success required balancing data trust strategy, multiple models, dynamic retraining, and human expert feedback to ground-truth results
- The company expanded data sources to collect several lakhs of data points from diverse, multi-source origins to build a truly reliable pricing engine
Source: Read original
Summary
Mahindra First Choice Wheels transformed its used vehicle pricing business by converting messy, inconsistent data into a strategic asset through AI and machine learning, as explained by Chandra Shekhar Prasad, Head of AI Products & Solutions at Mahindra Group. The company developed a unified pricing engine called Indian Blue Book that leverages AI-driven vehicle inspections and computer vision systems to detect micro-defects, color mismatches, and structural issues with over 95% accuracy. Initially, the pricing model achieved only 80% accuracy, but through continuous feedback loops and iterative retraining cycles, the company dramatically improved performance by collecting data from multiple sources and assigning trust levels to each source based on credibility. Rather than relying on a single monolithic algorithm, Mahindra built a sophisticated approach using multiple specialized models, each aligned to the quality and trust of its underlying data source, allowing the system to dynamically decide which dataset and model to use for generating prices. The company faced significant challenges including noisy data, escalations from banks and NBFCs, and unrealistic expectations of 100% accuracy from stakeholders, but resolved these through human expert feedback combined with model predictions to ground-truth results. A critical breakthrough came from expanding data sources and augmenting the dataset with several lakhs of data points collected from diverse sources, which initially received skepticism but ultimately became the backbone of the pricing engine. The success of this initiative is reflected in the dramatic reduction of escalations from over 400 to approximately 10, demonstrating the real-world impact of data normalization and alignment strategies. Beyond pricing, Mahindra First Choice Wheels implemented AI-based computer vision systems for real-time vehicle inspection, partnered with HRTech startup Amara to enhance employee engagement, and leveraged cloud-based scalable infrastructure to support its operations. The company also associated with CamCom for AI-powered visual inspection solutions, representing a first-of-its-kind partnership in India for the used car market. This comprehensive AI strategy demonstrates how balancing data trust strategy, multiple specialized models, dynamic retraining, and human feedback creates reliable, scalable solutions that drive both financial returns and organizational learning in the competitive used vehicle market.
Why It Matters
Demonstrates how AI and data strategy transform business operations in the used car market, reducing pricing discrepancies and escalations significantly
Reveals the importance of continuous feedback loops, multi-source data integration, and human expertise in building reliable AI systems rather than relying on single algorithms
Key Points
- Mahindra First Choice Wheels built Indian Blue Book, a unified AI-driven pricing engine that improved from 80% to significantly higher accuracy through iterative feedback loops
- The company uses multiple specialized models with assigned trust levels for different data sources rather than a single monolithic algorithm
- Escalations from banks and NBFCs were reduced from 400+ to approximately 10 through data normalization and alignment strategies
- AI-powered computer vision systems detect micro-defects and structural issues with 95%+ accuracy, outperforming human inspectors
- Success required balancing data trust strategy, multiple models, dynamic retraining, and human expert feedback to ground-truth results
- The company expanded data sources to collect several lakhs of data points from diverse, multi-source origins to build a truly reliable pricing engine
Source: ciso.economictimes.indiatimes.com
Original Publish Date: 29/11/2025
Entities: Mahindra First Choice Wheels, Chandra Shekhar Prasad, Mahindra Group, Indian Blue Book, CamCom, Amara, Tata Motors
