At a glance
- Challenge: Technicians spending up to 30 minutes manually searching 400-1,500 page manuals during machine downtime
- Solution: Multi-agent AI system built and deployed in 12 weeks through Vector Institute’s Agentic AI Bootcamp
- Result: Troubleshooting information now delivered in seconds
- Ownership: Linamar retains full IP and in-house capability to scale
Canada’s AI research leads globally but that research only creates value when companies can successfully deploy it. Vector bridges that gap, moving AI models from research papers to production lines where they solve actual operational challenges.
The Linamar story shows exactly how this works in practice.
Linamar manufactures precision-machined components, modules, and systems for automotive powertrains and industrial equipment. In manufacturing, unplanned downtime is notoriously expensive – every minute a machine sits idle means lost production. When factory robots break down, technicians face a massive bottleneck: finding the right fix in technical manuals that run anywhere from 400 to 1,500 pages. While they search, production lines wait.
Linamar saw an opportunity to modernize this diagnostic process. Working with Vector through the Agentic AI Bootcamp, the company’s technical team built a multi-agent system that delivers manufacturer-approved troubleshooting steps in seconds. More importantly, they built the internal expertise to maintain and expand the architecture themselves.
Moving beyond basic conversational interfaces, the Linamar team focused on developing robust, agentic solutions capable of scaling to high-impact operational use cases. They designed a modular system that directly integrates with their existing enterprise resource planning environment, demonstrating how applied research translates into immediate floor-level efficiency.
“After validating the use case through a low-code proof of concept, we had a team participate in Vector Institute’s Agentic AI Bootcamp to intentionally build internal capability. This allowed us to move beyond experimentation and develop more robust, agentic solutions that scale to higher-impact operational use cases.”
De-risking AI while building sovereign capability
Vector provides access to the latest techniques, hands-on engineering support, and a structured environment to build working systems in weeks instead of months. The essential differentiator here is that companies own what they build – the code, the intellectual property, and the knowledge to keep advancing it. Rather than outsourcing to external vendors, organizations develop their own internal expertise.
From June to September 2025, Linamar’s team worked alongside Vector researchers to develop a multi-agent chatbot that goes well beyond simple document search. The system needed to provide accurate technical guidance, maintain complete traceability for quality audits, and integrate with Linamar’s enterprise systems.
The solution relies on an orchestrator agent that coordinates specialized sub-agents. A search agent queries the extensive equipment manuals using hybrid search techniques, while a work order agent automatically generates maintenance records formatted for Linamar’s existing tracking system. The team evaluated multiple models before selecting Gemini 2.5 Pro for complex reasoning and Gemini 2.5 Flash for faster, cost-effective tasks. During testing, the resulting system achieved 70 per cent accuracy on real-world cases.
“Our collaboration with Vector gave us the practical engineering support we needed to move quickly,” says Mackenzie Kuntz, Data Science and AI Specialist at Linamar. ” That hands-on support made the difference between understanding the AI in theory and deploying it successfully.”
Results: From 30-minute searches to instant diagnostics
Twelve weeks from concept to working prototype achieved immediate results. Troubleshooting information that took 10 to 30 minutes of manual searching now surfaces in seconds. Technicians receive step-by-step instructions complete with direct links to the relevant manual sections.
By compressing a 30-minute diagnostic process, the AI assistant directly attacks the multi-million-dollar cost of unplanned downtime. While Linamar operates 86 facilities across 19 countries, building this capability begins with targeted domestic implementation. Developing and refining the strategy in Canada first allows the team to validate the system’s performance before evaluating broader applications. Even localized efficiency gains compound into a significant competitive advantage over time.
What comes next: Scaling a modular AI architecture
The maintenance assistant isn’t a static endpoint. It acts as a foundational architecture that Linamar is already working to expand. Because the system is modular by design, the development team can extend the same principles to different equipment categories and new operational challenges across their global footprint.
“Our focus is on building flexible architectures rather than relying on black-box vendor solutions,” notes Sharp. “Retaining the intellectual property and understanding the underlying agent mechanics allows us to apply these same engineering principles to other high-value problems across the manufacturing floor.”
Manufacturers that can deploy AI effectively gain a measurable edge. The difference between licensing AI tools and building them internally is the difference between renting capability and owning it. Through programs like the Agentic AI Bootcamp, open to Vector sponsors, Vector functions as essential infrastructure – connecting government investment to commercialization and giving Canadian companies the tools, expertise, and environment to build their own lasting competitive advantage.
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