When Hexagon set out to modernize their global technical support operations, they faced a critical decision: build AI tools internally or invest in a purpose-built platform.
In this candid conversation, Hexagon COO Adam Savage joins AptEdge Co-Founder Aakrit Prasad to break down the real-world trade-offs of building vs. buying AI for complex support workflows — and why choosing AptEdge delivered faster ROI, happier teams, and long-term scalability.
Teasers

If you’re building it yourself, you’re already building a solution AptEdge has solved for. Why waste internal resources?
Problem
Support leaders are under increasing pressure to modernize operations with AI—but the path forward isn’t always clear. Many teams default to building internal tools, only to find they’re difficult to scale, expensive to maintain, and not aligned with the fast-moving needs of global support organizations. When the workflows are complex and customer experience is on the line, the wrong decision can stall innovation and damage ROI.
Solution
Hexagon, a global technology leader, faced this exact challenge. Instead of building yet another internal tool, they chose to buy a purpose-built solution designed specifically for complex support workflows. With AptEdge, they deployed AI that fit seamlessly into existing systems, gave agents smarter, faster access to information, and required no internal maintenance. The result: enterprise-grade impact without the overhead.
Results
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Increased agent efficiency by reducing time spent searching across tools, with answers surfaced in the flow of work
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Boosted support team satisfaction, empowering agents with the right context and knowledge to resolve complex cases faster
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Shortened onboarding time for new hires by eliminating the need to manually teach tool navigation and information sourcing
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Improved customer experience and CSAT, with faster first responses, higher-quality resolutions, and fewer escalations
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Unlocked knowledge reuse through AI-powered article creation from solved tickets, accelerating team learning and scale
Business Outcomes
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Accelerated time-to-value by bypassing long internal dev cycles with a plug-and-play solution tailored to support workflows
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Reduced operational overhead, eliminating the need for ongoing maintenance, upgrades, or resource pull from engineering teams
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Improved global support consistency by standardizing access to knowledge across diverse product lines and regions
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De-risked AI adoption with a proven, secure platform that scaled quickly without disrupting existing systems
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Strengthened alignment between support and business goals, freeing up internal capacity to focus on innovation, growth, and customer success
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