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Case Studies

Case Studies

A global manufacturing conglomerate drives GenAI adoption for 150K+ workforce

Industry & Segment

Automotive & Manufacturing

Objective

To democratize generative AI across the organization’s global workforce by providing secure access to enterprise knowledge, streamlining workflows through pre-trained models, and advancing innovation without requiring deep technical expertise.

Client Overview

A multibillion-dollar multinational corporation with a diversified portfolio spanning automotive, manufacturing, and multiple other industries partnered

Business Challenges

Model distribution and management at enterprise scale

The organization needed flexibility to adopt multiple genAI foundation models for different use cases without vendor lock-in, while maintaining centralized governance over model access, usage, and lifecycle management.

Data security and access control for sensitive enterprise information

Enabling AI-powered access to enterprise knowledge raised critical security requirements, such as, protecting sensitive operational data, customer information, and proprietary business intelligence while allowing associates to query documents, databases, and call transcripts. The platform needed workspace-level data isolation, role-based access control, audit logging, and compliance with enterprise data governance policies to prevent unauthorized access or data leakage.

User adoption across diverse technical backgrounds

The workforce spanned manufacturing floor operators, sales teams, customer service agents, engineers, and business analysts with vastly different skill levels. The solution needed AI capabilities accessible through a conversational interface that felt natural to all users.

Want to leverage genAI solutions to achieve measurable business value?

QualityKiosk's Approach

We deployed a comprehensive enterprise genAI platform built on Google Cloud services, following a phased implementation approach from discovery through production rollout.

Model Marketplace and Foundation:

• Pre-trained foundation models accessible through centralized marketplace
• Bring Your Own Model (BYOM) capability without vendor lock-in
• Vertex AI for model hosting, inference, and lifecycle management
• Google Kubernetes Engine for scalability and workload isolation
• Cloud Storage for call recordings, transcripts, and embeddings

Retrieval-Augmented Generation (RAG) Workspaces:

• Personal RAG for individual document queries with data isolation
• Team Workspaces for collaborative knowledge bases with controlled access
• Page-level source referencing for traceability
• Semantic search across structured and unstructured data

Intelligence Capabilities:

• NLP to SQL translation for natural language database queries
• Unified search across databases, documents, transcripts, and PDFs
• Intent and insight extraction from conversations
• Automated call summarization
• Text generation and classification

Security and Governance Framework:

• Role-based access control (RBAC)
• Workspace-level data segregation
• IAM policies for model access and data governance
• Audit logging and encryption

Implementation Phases

Discovery & baseline assessment

Discovery and baseline definition assessing workflows, data sources, and success metrics

Solution architecture & design

Solution design defining architecture, workspace models, and integration points

Secure data ingestion & integration

Data ingestion connecting structured and unstructured sources with secure pipelines

AI model configuration & enablement

Model configuration enabling RAG, NLP-to-SQL, search, and summarization capabilities

Pilot deployment & validation

Pilot deployment validating accuracy, usability, and business impact

Performance optimization & refinement

Refinement based on user feedback and performance metrics

Production rollout & adoption

Production rollout with training and adoption enablement

Continuous monitoring & enhancement

Continuous improvement monitoring KPIs and enhancing features

Technical Results

150,000+

Associates enabled with genAI capabilities

Platform features

Model Marketplace, Personal and Team RAG workspaces, NLP-to-SQL, unified enterprise search, call summarization, intent extraction

Secure by design

RBAC, workspace isolation, audit logging, encryption

Minimal training

Conversational interface requiring no technical expertise

Business Outcomes

Pre-trained models eliminated build-from-scratch timelines, allowing teams to focus on higher-value tasks

Enterprise knowledge became accessible through natural language queries, transforming information retrieval from an IT dependency to self-service

Organization-wide AI access shifted innovation from top-down mandates to bottom-up experimentation across departments

BYOM flexibility supports continuous platform evolution without vendor lock-in

TESTIMONIALS

Testimonials

This partnership has been a productivity powerhouse for M&M. The pre-trained models in genAI are like having a team of AI experts on hand. They’ve streamlined workflows across departments, allowing employees to focus on higher-level tasks and significantly boosting overall productivity. I’m confident this is just the beginning, and we will continue to unlock new levels of efficiency within.