AI RFP response built on your own past wins.

Answer honestly from your own timesheets: pull the last three responses and count the senior-staff hours, section by section. One professional-services firm we work with estimates $50–70K of senior-staff time per response — most of it spent rebuilding answers that already existed somewhere in the building.

Yes, with structure. The system extracts the questions from the new RFP, matches each one against your indexed past answers and slides, and assembles a reference draft. A human approves every section before anything leaves the building — anything unmatched lands in an exception queue for a person, not a guess.

An AI RFP tool is software that reads an incoming RFP and helps draft answers, usually by generating text or retrieving from a library you load into it. The category’s catch is the empty library: the tool ships knowing nothing about your firm, so you still supply every answer. Encoding your own answer library first is what makes the draft worth reviewing.

RFP software rents you a seat with an empty library — it organizes the hunt, but every answer still comes from your team’s memory and shared drives. Encoding your answer library indexes the past RFPs, decks, and case studies you already own, with your rules on top: which answers are current, which claims need partner sign-off, and what never leaves the building.

Partners should judge and the system should fetch. In most firms the most senior people do the retrieval — hunting past answers across shared drives — because the knowledge lives in their heads. Once the answer library is encoded, partners spend their hours approving sections instead of searching for them.