Smart Proposal Assistant
Massaro Corporation ("Massaro") is a Pittsburgh-based general contractor with decades of experience delivering complex construction projects across healthcare, higher education, and commercial sectors.
That history represents a valuable archive of winning proposals containing institutional knowledge, proven approaches, and refined messaging. Today, each new RFP starts from scratch—without access to this content.
Vambrace AI will build a system that lets your team interact with that history through natural language—ask questions, get answers, generate new content in Massaro's voice. The result: faster turnaround, consistent voice, and proposals informed by what has actually won work.
Pilot Engagement: $10,000 (2 months) — Deliver a working system with clear go/no-go criteria at Month 1. Option to terminate after the first month with no further commitment.
New RFP arrives. You spend 4 hours searching old proposals—"Where's that safety section from the AHN project?" Then 10+ hours drafting sections that sound like things you've written before.
Search takes seconds. First drafts generate instantly. You edit and refine instead of starting from scratch. ~7 hours saved per proposal.
Bottom line: At 40 proposals/year, that's roughly $18,000 in annual labor savings.*
*Assumes $65/hr loaded labor cost. We'll validate these estimates against your actual volume.
A private web application at massaro.vambrace.ai where your team can:
"What did we write about infection control for hospital projects?" Get instant answers with citations to the source document and page.
Describe what you need—"Write a 500-word safety narrative for a UPMC project"—and get a first draft written in Massaro's voice, informed by what's worked before.
30-minute calls to review progress, gather feedback, and adjust course. You're never waiting weeks to see results.
By the end of Month 1, your team will have a working system that answers questions about past proposals and drafts new sections.
We'll load 20-30 of your recent RFPs. Ask anything in plain English—"What was our approach to phasing on the UPMC project?"—and get an answer with citations.
Type a prompt, get a 300-500 word draft informed by how Massaro has written similar content before. Review, edit, and use.
We'll define 10 test queries at kickoff based on real questions your team asks. The system must return relevant content for at least 8 of them.
Month 2 adds the ability to work on active pursuits and expands the system based on team feedback.
Upload your active RFPs into the system. Now when you draft, the AI knows both your historical proposals AND the specific requirements of the RFP you're responding to.
Load additional proposals based on team requests. Tag content by project type, client, and outcome for faster filtering.
Tune search and draft generation based on Month 1 feedback. Better results, better voice matching, fewer edits needed.
Use the system on a real proposal from kickoff to submission. Measure actual time savings against your typical process.
Train core users. Document best practices. Ensure the team can use the system independently.
ChatGPT is excellent for one-off writing tasks. But it can't be your proposal knowledge system. Here's why:
We're not replacing ChatGPT—we're building a foundation that makes AI useful for your specific proposal work. The same AI models, but trained on your data, speaking in your voice, available to your whole team.
If the pilot proves valuable, the system can expand to deliver additional capabilities:
Longer term: The system can evolve into a centralized proposal hub—a workspace where team members collaborate on active pursuits, track status across the pipeline, and where subcontractors and partners submit their content directly. This transforms proposal development from a fragmented process into a coordinated workflow.
Month 1: $5,000 due at signing
Month 2: $5,000 due at start of Month 2 (only if continuing after Month 1 review)
Total pilot investment if both months completed: $10,000
Month 1: Ask & Draft
Week 1: Kickoff call. Define 10 test queries together. Receive document access. Begin loading RFPs.
Week 2: System live. Test against known questions. Weekly check-in call.
Week 3: Draft generation enabled. Test on sample sections. Weekly check-in call.
Week 4: Month 1 review against success criteria. Go/no-go decision. Weekly check-in call.
Month 2: Live RFP Workspace & Team Rollout
Weeks 5-6: Add live RFP workspace. Expand archive. Tune quality based on feedback.
Weeks 7-8: Full pursuit test. Team onboarding. Final review and handoff.
Weekly 30-minute check-in calls throughout. You'll always know where things stand.
This pilot is structured to validate outcomes before expanding commitment. Clear success criteria at Month 1 ensure both parties are confident in the value before continuing.
Luke Deasy
Vambrace AI