In manufacturing, speed matters—especially when it comes to quoting. If you can’t turn around quotes quickly and accurately, your customers will move on to someone who can. But should you leverage AI, like ChatGPT, to make the quoting process easier?
In this episode of MakingChips, we’re tackling a hot topic: the risks and limitations of using large language models for estimating and quoting.
Jason Ray, the co-founder and CEO of Paperless Parts, joins us to break down why these AI tools might not be the magic solution some shops hope for. From data security concerns to unpredictable costs and AI hallucinations that could lead to costly mistakes, we’ll explain why trusting a general-purpose AI with your quoting process might be more of a liability than a benefit.
Instead, we’ll explore a smarter approach—one that enhances human expertise rather than replacing it. If you care about accuracy, efficiency, and protecting your customers' data, you don’t want to miss this conversation.
Because if you’re not quoting quickly and accurately, you won’t be winning business. And if you’re not winning business, you’re not MakingChips, and you’re not making money. BAM.
Segments
- [0:27] Use Paperless Parts for your estimating and quoting needs
- [2:21] The bottlenecks we all face with quoting
- [10:24] Paperless Parts is razor-focused on estimating and quoting
- [12:48] Meet us at The MFG Meeting in San Antonio
- [14:53] Should you use an AI engine for quoting?
- [19:08] Subjective vs objection AI
- [31:02] Why this conversation is important
Resources mentioned on this episode
- Paperless Parts
- ProShop ERP
- Meet as at The MFG Meeting in San Antonio!
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