in missed dependencies
impact analysis
An industrial manufacturer wanted to accelerate its engineering change review process without compromising the integrity demanded by CM2. Missed downstream impacts and manual reviews were slowing product updates, increasing rework, and adding risk. Despite a clear need for automation, legacy PLM tools lacked the intelligence needed to support impact analysis at scale. Protean helped the engineering team overcome these challenges—by fine-tuning AI models in-house, using their own change history, product data, and infrastructure, with full control over accuracy, traceability, and compliance.
Reviewers frequently missed downstream effects like unlinked documents, or tooling. Fixing mistakes later in the cycle meant delays, cost, and lost confidence.
Analysts had to hunt across systems to identify what might be affected. Complex changes could take days to analyze—and were still incomplete.
Off-the-shelf models didn’t recognize product structures, part naming conventions, or internal review patterns. They couldn’t predict real-world impact.
This created a model that could accurately predict how a proposed change might propagate; based on real, CM2-aligned organizational history.
Plugged directly into the existing PLM ecosystem. Developers used reusable APIs to add AI assistance into existing workflows - no orchestration, no MLOps overhead.
What once took months of effort and coordination now took days. Teams could iterate quickly, deploy confidently, and maintain control throughout the lifecycle.
This manufacturer didn’t need another AI tool, they needed a way to apply intelligence to an existing process without breaking it. They needed CM2-grade control with modern automation. Protean AI gave them exactly that: a secure, in-house platform to fine-tune, deploy, and integrate AI into change management. This accelerated engineering work, improved accuracy, and protected the product configuration baseline at every step.
Automate impact analysis, cut missed dependencies, and speed up approvals—without sacrificing CM2 rigor or data sovereignty.
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