While the industry talks about EVs, SDVs, and software-defined platforms, most programs still run on execution models that haven’t caught up.

Engineering teams remain locked in traditional engineering workflows, spending weeks on repetitive design tasks that should be automated. Critical rules are missed, leading to downstream revisions that cost time, money, and trust.
 

Manufacturing partner decisions happen in isolation without deep context on technology involved, material grade, process fit, or cost drivers. Raw data is scattered. Supplier selection often depends on familiarity, not precision. In a world where consumer goods are sourced with AI-backed marketplaces, automotive sourcing still feels manual and reactive.
 

Talent is another constraint. Hiring full-time engineers for fast-changing project needs leads to mismatched skills and long onboarding cycles. There’s a vast, underutilized gig engineering workforce yet most companies don’t have the systems to activate it meaningfully.

And when it comes to manufacturing, the gap widens further. Pilot runs and subassembly builds still depend on rigid, CapEx-heavy setups. Micro factories, which should offer agility and scalability, often sit outside the traditional ER&D ecosystem fragmented, under-leveraged, and disconnected from engineering.
 

These are not operational hiccups. They’re execution gaps, real, structural barriers to speed, efficiency, and scale.

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If you’ve experienced any of this, you’re not alone and you’re in the right place.

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