Proposal cycle time
Past wins and option lists exist. Assembling the next package still takes days.
On-site AI for OEMs with technical products, application engineers who write proposals, and a sales force that sells direct or through a manufacturers’ rep network. Pricing, configs, and customer applications stay on your network.
Past wins and option lists exist. Assembling the next package still takes days.
Every sizing question becomes a phone tag with an application engineer who is already underwater.
Sales, application engineering, and ownership all feel it. The decision often sits with a small board.
Pull prior proposals, specs, and options into a new package faster — with citations back to the source files.
“Will this model work for X duty?” answered from manuals and past applications — walled by product line.
Independent manufacturers’ reps get the same technical answers the factory would give — without exposing the whole book.
Objections, configs, and handoff notes from the OEM network — searchable, logged, still inside the perimeter.
Product line / region / role. A rep or seller scoped to one family does not retrieve another family’s book — or another territory’s confidential pricing.
Mid-size manufacturers with engineered products — not commodity catalog-only sellers. We sell to the people who feel proposal and channel friction, knowing a board often has to say yes.
Cycle time, win rate, and rep productivity without putting the price book in a public chat tool.
Fewer repeat sizing calls; proposals that start from real prior art instead of a blank page.
Knowledge stays a company asset — especially when senior app eng or rainmakers eventually leave.
Keystrokes, walls, ~30 tok/s stream, stack overlay — on How it works. Then talk to the people who live in OEM sales.