The problem
A large enterprise sales organization set a target of roughly $1B in new and organic revenue for the year. Hitting it meant getting more qualified proposals in front of more buyers, faster.
But the proposal team — producing about 1,000 proposals a year — was already at capacity. Every incremental deal added drafting, formatting, review, and coordination that no amount of overtime could absorb. The pipeline was ready; the proposal step was the constraint.
What we built
We started with the business problem, not the technology: the fastest path to more revenue ran straight through proposal throughput. So we built an AI proposal assistant that drafts proposals and speeds up the work around them — assembling a first draft from prior wins and approved content, then handling the formatting, tailoring, and coordination that used to eat the team's days.
People stay in the loop where judgment matters — qualifying the opportunity, shaping the pitch, and signing off before anything reaches a buyer. The assistant takes the repetitive weight off the critical path; the team's senior time goes to winning.
The outcome
Throughput rose about 30% with the same team, directly supporting the path to the $1B target. The time returned amounted to roughly $3M in annual labor savings — enough to pay back the investment in about six months, close to a 2x return in year one. And it compounds: every proposal improves the content the assistant draws on.