Why most AI in quoting still needs a human in the loop

Every quoting-automation pitch eventually says some version of "the AI handles it." It rarely says what happens when the AI is wrong, because that's the part that's hard to demo. On an engineered spec, wrong isn't a typo. It's a grade substituted for another grade, a tolerance class read as looser than it is, a mil-spec callout resolved with the wrong material.

The industries that actually need this software, fasteners, bearings, hydraulics, valves, are the ones where a confident wrong answer costs more than a slow right one. That's a specific kind of software problem, and it's not the one most AI vendors are optimizing for.

The models are genuinely good at reading unstructured text and proposing a match. They're not good at knowing what they don't know, unless the system around them is built to surface that explicitly. That's the part that matters: not whether the AI can resolve a spec, but whether it tells you when it isn't sure.

Camber scores its own confidence on every resolved line and routes anything below threshold to a person, with the ambiguity named rather than buried. Two SAE grades match a callout. A customer's order history shows one of them 41 times out of 43. That's a fact a rep can act on in five seconds, not a guess dressed up as an answer.

The AI reads the spec, proposes the match, and prices it inside your rules. Your rep decides what ships. That split isn't a limitation we're working around, it's the actual design.