Back-to-back customer quotes of the week from BrainChip: "Driver gives my team the productivity to win more business." — Todd Vierra, VP Customer Engagement "Driver accelerated our internal roadmap for Generative AI by providing the critical context layer needed to bridge our codebase with...
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How BrainChip's Solutions Engineering team uses Driver to build customer demos faster, ramp engineers in hours instead of days, and scale their sales pipeline.
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How BrainChip Scaled Customer Demos with Generative AI
Brainchip
•January 28, 2026
BrainChip builds Akida™, a neuromorphic AI processor that brings machine learning to edge devices at ultra-low power. It enables real-time AI in sensors, wearables, and industrial equipment—running on milliwatts.
BrainChip's Solutions Engineering team helps potential customers see what Akida can do on their hardware, creating demonstrations with models compiled for BrainChip's silicon that prove performance in real-world conditions.
The Opportunity: Scaling Expertise
The team had a solid process for building customer demonstrations. They knew how to navigate target host platform specs and reference designs—Yocto, Buildroot, ARM, i.MX—and translate that into compelling demos of Akida's performance. It worked.
But there was always more they wanted to try. GitHub repositories full of platform specifications, reference designs across dozens of hardware configurations, new combinations they hadn't explored yet. The knowledge was out there, scattered across repos and docs and people's experience. The bottleneck wasn't skill or process—it was time. There simply wasn't enough of it to pull together the right context to match their ambition.
They'd been watching Generative AI tools emerge and had a feeling these could be the accelerator they were looking for. Not more documentation—they had plenty of that. What they needed was better context: a way to surface the relevant pieces faster so they could push further into what Akida could do.
The Solution: Driver + Claude Code
BrainChip deployed Driver's MCP integration with Claude Code for their Solutions Engineering team.
Engineers now reverse engineer unfamiliar code in hours instead of days. Driver's context layer lets them map dependencies across codebases without manually tracing through documentation.
They query one codebase while working in another—critical when customer evaluations span multiple platforms. They pull implementation patterns from external reference codebases, like NXP's Yocto recipes, and understand how those patterns apply to Akida.
The key capability—quickly building demonstrations that prove Akida's performance on diverse customer hardware—became faster and more scalable. When you can query complex platform specifications in natural language, ramping up on unfamiliar customer environments takes a fraction of the time.
The Impact
Solutions Engineers now build customer demonstrations faster, which means supporting more prospects in the sales pipeline. What started with the pre-sales Solutions Engineering team has since expanded to internal research and hardware engineering—teams that saw the results and wanted the same advantage.
Driver gives my team the productivity to win more business.
— Todd Vierra, VP Solutions Engineering
The impact goes beyond productivity. For BrainChip, Driver didn't just improve workflows. It widened the sales funnel.
Driver accelerated our internal roadmap for Generative AI by providing the critical context layer needed to bridge our codebase with LLMs, turning our theoretical experiments into immediate, practical engineering workflows.
— Kurt Manninen, Senior Solution Architect
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