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AI FOR CUSTOM MANUFACTURING QUOTE MANAGEMENT
Custom manufacturing quotations are difficult to scale because every request can combine different drawings, materials, outside services, quantities and labour assumptions. Our agentic AI framework organizes that work into a reviewable case so experienced people can spend more time making decisions and less time assembling information.
01
A promising RFQ is not a single form to fill out. Someone must interpret drawings, identify missing specifications, source stock and subcontracted steps, estimate labour, track replies and reconcile costs before a quote can be approved. As request volume rises, the work piles up around a small number of estimators and project managers.
02
The AI assistant structures each request by line item, extracts requirements for review, prepares customer clarification questions and supplier RFQ drafts, and assembles an evidence-backed preliminary quote package. A project manager edits and approves external messages and the final commercial decision.
03
The approach was developed around one custom manufacturer’s operating requirements. Its modular case workflow, controlled knowledge tables, approval checkpoints and quote pipeline can be adapted to other shops, product mixes and approval rules after discovery.
