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 with QKTech to deploy an enterprise-wide GenAI platform that abstracted technical complexity from end users.
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.
QKTech’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
AI-augmented quality controls
AI-assisted workflows validated code quality through automated reviews, documentation checks, root-cause analysis and defect resolution, reducing manual errors while maintaining engineering rigor and consistency.
Pilot deployment & validation
Pilot deployment validating accuracy, usability, and business impact
Production rollout & adoption
Production rollout with training and adoption enablement
Continuous monitoring & enhancement
Continuous improvement monitoring KPIs and enhancing features
Results Achieved
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
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