Mahindra & Mahindra — From a 90-Spec Benchmarking Agent to a 207-Spec Vehicle Development Platform
Mahindra & Mahindra partnered with Wohlig Transformations to build an AI-powered Vehicle Development, Benchmarking, and Product Planning Platform on Google Agent Development Kit (ADK) + Gemini 3 Flash, evolving from a 90-spec MVP into a production platform analysing 207 specs per car across three integrated functions.
Project Overview
Mahindra & Mahindra‘s R&D, product-planning, and competitive-intelligence teams partnered with Wohlig Transformations to put AI at the centre of how vehicle decisions get made. The engagement began in February 2026 with a focused 3-week SOW: an ADK-powered automotive benchmarking agent covering 90 car specs, car-only competitor analysis, and integration with Mahindra’s existing ChatAI on Google Cloud. The MVP proved the approach quickly — and on the strength of that impact, Mahindra expanded the scope substantially. The production platform now covers 207 specs per car across three integrated functions — Vehicle Development, Benchmarking, and Product Planning — with a custom React.js UI/UX, ADK exposed as the REST API backend, user RBAC (admin / analyst / viewer), custom views per spec or spec group, chat history across sessions, and multi-file upload for document intelligence. A multi-source data layer — Google Custom Search API, web scraping, YouTube Data API v3, and PDF document intelligence — feeds Gemini 3 Flash agents, all deployed on Google Cloud.
The Challenge
Single-Function Bottleneck: The original ChatAI integration only supported competitor benchmarking — but Mahindra’s R&D teams needed an AI workstation spanning vehicle development, benchmarking, and product planning, not a point tool.
Limited Spec Coverage: 90 car specs was the right scope to validate an MVP, but production decisions across SUVs, pickups, commercial vehicles, and tractors demand far richer analytical depth.
Multi-Source Data Sprawl: Vehicle insights live across official automotive sites, expert review portals, YouTube channels, and OEM PDF brochures — no single source covers it all, and stitching them together manually doesn’t scale.
RBAC + Custom Views: Admins, analysts, and viewers each need different surfaces — and analysts often need bespoke spec groupings that a default schema can’t anticipate.
Conversational Continuity: One-off queries weren’t enough; teams needed chat history that preserved investigation context across multi-day workflows.
Key Objectives
Expand Spec Coverage: Grow from 90 to 207 specs per car to support deep R&D decisions.
Three Integrated Functions: Unify Vehicle Development + Benchmarking + Product Planning in one platform.
Custom React UI/UX: Replace the existing-ChatAI integration with a purpose-built frontend driven by ADK as a REST API.
RBAC + Custom Views: Admin / analyst / viewer tiers plus per-user custom views of individual specs or spec groups.
Multi-Source Data Integration: Combine web scraping, Google Custom Search API, YouTube Data API v3, and PDF document intelligence with RAG.
Production Security: Cloud IAM, Secret Manager, API Gateway, end-to-end encryption, and VPC isolation.
The Solution: AI-Powered Vehicle Development + Benchmarking + Product Planning Platform
V1 — The 3-Week MVP (Feb–Mar 2026): An ADK-powered benchmarking agent covering 90 car specs and car-only competitor analysis, integrated with Mahindra’s existing ChatAI. A Python backend on Google Cloud, with RBAC, interactive dashboards, and exportable reports (PDF / Excel / PPT) — delivered in three weeks.
V2 — The Expanded Production Platform: 207 specs per car across three integrated functions — Vehicle Development, Benchmarking, and Product Planning. A custom React.js UI/UX with ADK as the REST API backend, RBAC plus custom views per spec or spec group, chat history across sessions, and multi-file upload with document intelligence (OCR + RAG).
Multi-Source Data Layer: Google Custom Search API (domain-whitelisted to curated automotive sources) + web-scraping pipelines + YouTube Data API v3 (video reviews, expert opinions, sentiment) + PDF brochure ingestion → OCR → vector embeddings → RAG corpus.
AI Core: Vertex AI Gemini 3 Flash agents orchestrated by ADK in a multi-agent pipeline — competitor data extraction → spec normalisation → RAG → comparative insights → report drafting.
Technology Stack: Vertex AI, Gemini 3 Flash, Agent Development Kit (ADK), React.js, Python, FastAPI, Google Custom Search API, YouTube Data API v3, Cloud Run, Cloud Storage, BigQuery, Cloud IAM, Secret Manager, API Gateway, and Cloud Operations Suite.
Key Benefits & Results
Previous: 90 specs, car-only benchmarking. Our Solution: 207-spec, 3-function platform (Vehicle Development + Benchmarking + Product Planning). Result: 2.3× analytical depth and coverage of the full R&D decision lifecycle.
Previous: Integration with existing ChatAI (limited UX flexibility). Our Solution: Custom React.js UI/UX with ADK as REST API. Result: Frontend and backend evolve independently — a future-proofed architecture.
Previous: RBAC tiers only. Our Solution: RBAC + per-user custom views per spec or spec group. Result: Analysts and product planners get bespoke surfaces without admin intervention.
Previous: Stateless queries. Our Solution: Chat history across sessions. Result: Investigation context preserved across multi-day workflows.
Previous: Single PDF upload. Our Solution: Multi-file upload. Result: Document intelligence across multiple brochures and reports in one session.
Previous: Single-source competitor data. Our Solution: Multi-source integration (Custom Search + Web Scraping + YouTube + Document RAG). Result: Richer, cross-validated insights for product decisions.
Previous: Manual report drafting. Our Solution: AI-generated comparative reports. Result: Exportable PDF / Excel / PPT outputs ready for stakeholder review.
Technical Innovation
ADK as REST API + React Frontend: A modular architecture where Google’s Agent Development Kit is exposed as a backend microservice and the frontend is a custom React.js application. Frontend and backend evolve independently — Mahindra can change UX patterns without touching agent logic, and Wohlig can swap models or pipelines without touching the UI.
207-Spec Schema with Custom Views: A full automotive specification schema (engine, safety, comfort, infotainment, dimensions, performance, and more) at 207 fields per car, with user-defined custom views that let analysts pivot on any combination of specs or spec groups.
Multi-Source AI Pipeline: Google Custom Search API (domain-whitelisted to curated automotive sources) + web scraping + YouTube Data API v3 + PDF document intelligence with OCR + RAG — four orthogonal data streams unified by ADK orchestration and Gemini 3 Flash insight generation.
RBAC + Chat History + Multi-File Upload: Production-grade experience features — admin / analyst / viewer tiers, persistent chat history, and multi-document upload with RAG — layered on top of the agent, and uncommon in MVP-stage automotive AI tools.
Continuous Scope Expansion: A 3-week SOW MVP that grew into a sustained production engagement. The V1 → V2 jump — 90 → 207 specs, single → three functions, integration → custom UI — demonstrates Wohlig’s ability to evolve a product alongside client adoption.
Wohlig’s Approach
Architecture & setup — GCP project setup, access finalization, and platform integration scoping.
AI & data pipeline development — Web scraping + Google Custom Search API + YouTube Data API v3 integrations, Gemini 3 Flash workflows, and ADK orchestration.
Visualization & reporting — Interactive dashboards, report templates, and multi-format export (PDF / Excel / PPT).
V1 deployment & UAT — Cloud Run deployment, Cloud IAM + RBAC, monitoring, and Mahindra UAT.
V2 scope expansion — 207-spec schema; three functions (Vehicle Development + Benchmarking + Product Planning); React.js UI/UX rebuild; ADK as REST API; custom views; chat history; multi-file upload.
Continuous iteration — Documentation, training, knowledge transfer, and ongoing optimization with Mahindra’s R&D and product-planning teams.
About Mahindra & Mahindra
Mahindra & Mahindra Ltd. is one of India’s largest multinational automotive corporations, headquartered in Mumbai. Part of the globally diversified Mahindra Group, its portfolio spans SUVs, pickup trucks, commercial vehicles, tractors, and emerging mobility solutions across India, South Africa, Australia, and Latin America. Mahindra’s R&D and product-planning functions are leading AI adoption to accelerate vehicle development and competitive intelligence.
About Wohlig Transformations Pvt. Ltd.
Founded in 2015, Wohlig Transformations specialises in GenAI and DevOps, with 160+ professionals across India and the UK.
Detailed Case Study Presentation: https://canva.link/qk4f0afxt31jby7