This cluster covers automation of knowledge work in manufacturing: quoting, engineering back office, order handling. We unpack drawing-to-offer, how AI extracts parameters from a drawing and prepares a preliminary quote, where accuracy breaks and how to slot it into an existing technical sales process. Honest note: AI handles ~70% of typical cases and frees engineers for the hard 30%, not the other way around.
What decides the quality of an AI assistant in the technical office is not gigabytes but the consistency of your sources. Real sizing: chunks, volume thresholds and why the naive setup fails.
AI in technical documentation quality control catches formal inconsistencies: drawing versus spec, gaps, wrong versions. We show what it catches, what it misses, and where a person stays in the loop.
A demo looks impressive, the daily reality of a design office is different. We break down which tasks AI really takes off the engineer and which stay slideware.
Quoting from a technical drawing is a bottleneck that costs you orders. How AI cuts the cycle from days to hours, where the limits are, and what you need to make it work.
