MASSARO CORPORATION

Smart Proposal Assistant

Put your proposal history to work on every new pursuit

January 2026 | Confidential

Prepared by:

Vambrace AI

Executive Summary

Problem

Proposal knowledge is trapped in individual files and memories. Each RFP response starts from scratch.

Solution

AI chat interface to search, ask questions, and generate RFP sections from 30-40 historical proposals—with source citations on every response.

Target Impact

8-13 hours saved per proposal. Same team responds to more RFPs without adding headcount.

Long-Term Outlook

Proposal hub for generation, collaboration, compliance, competitive intelligence, and analytics.

Pilot Deliverables

AI chat interface for proposal Q&A

RFP section generation

Source citations on every response

Live pursuit test end-to-end

Success Criteria

80%+ query satisfaction • 3+ team members using weekly • Stakeholder sign-off on value

Investment

$12,000

2-month pilot program

Vambrace AI

Purpose-built AI systems. We partner with teams to build practical AI tools that drive real results—serving as a trusted advisor throughout AI adoption.

What We Heard

Pain points and opportunities identified in our conversations with the Massaro team.

"Every time we do this, it's like a one-off. I upload it, I ask these questions, it gives me good feedback. But then it says you're out of space..."

On ChatGPT Limitations

"I think a good starting point would be if we could have some kind of uniform ability to take RFPs we've written in the last two or three years and have the ability for some AI system to read those and use those."

On the Opportunity

"I don't think we're expecting it to produce our RFPs necessarily. But I do think we'd like to use the language of other RFPs to help inform newer ones."

On Expectations

Additional Pain Points

Each RFP starts from scratch, no repeatability
Consistent formatting for multiple parties
Consistent voice across proposals
AI-generated content "wasn't project specific, needed more color"

The Opportunity

The Massaro team already uses AI for individual queries. The next step: shared, persistent knowledge that compounds over time.

Years

of proposal history
ready to activate

30-40

hours per proposal
in research and drafting*

The current state: Each AI session starts from scratch. Knowledge from previous queries is lost when the session ends. Team members maintain separate ChatGPT histories with no shared organizational memory.

The opportunity: Build persistent institutional knowledge that compounds over time—rooting modern AI capabilities in Massaro's rich traditions and decades of proven expertise.

WHAT CHANGES WITH THE SMART PROPOSAL ASSISTANT (SPA)

Full and immediate access to your proposal archive

Shared across all team members

Every response cites source documents

Preserves Massaro's voice and legacy

Filter by client, sector, or outcome

*Based on industry research for complex technical proposals

The Solution

Less time searching, more time winning. AI-powered search, chat, and generation—all grounded in your proposal archive.

Search

Find relevant content across 30-40 proposals instantly with source citations.

Chat

Ask questions in plain English, get conversational answers with verification.

Generation

Draft RFP sections in Massaro's voice from your historical content.

Relationship Mapping

Connect projects, teams, clients, and outcomes across your history.

smart-proposal.massaro.com

Draft a safety approach section for a healthcare project with occupied spaces

SPA3 sources

Safety Approach for Occupied Healthcare Facilities

Massaro implements comprehensive infection control protocols including daily barrier inspections, HEPA filtration systems, and dedicated coordination with hospital staff. Our phased approach maintains 24/7 operations with negative pressure containment...

AHN Forbes pg 12-14UPMC Shadyside pg 8-9St. Clair pg 15

Ask a question or request content...

Most teams remain in experimentation mode with AI—individual accounts, no shared memory, no persistent system. SPA addresses the gap between where you are and where you want to be.

Organizational Impact

Unlock capacity to pursue more proposals while maintaining knowledge integrity across your organization.

Time Savings Per Proposal

Research and retrieval

5-8 hours saved

First draft generation

3-5 hours saved

Target: 8-13 hours saved per proposal

At 50 proposals/year = potential 400-650 hours annually

The value proposition: Not replacing expertise, but amplifying it. Your team focuses on strategy and differentiation while SPA handles research and retrieval with full source traceability.

Capacity Unlocked

15-20%

Target: more proposals
pursued annually

Additional
headcount required

Uniform ability to leverage past RFPs. Same team, same cost structure. More competitive pursuits with verifiable content.

What Success Looks Like

Increased team capacity. Same team pursues more opportunities without additional headcount.

Reduced time to first draft. Less time gathering content, more time on strategy and refinement.

Institutional knowledge preserved. Decades of expertise accessible for every new pursuit.

Consistent quality across proposals. Every response reflects Massaro's voice and standards, regardless of author.

2-Month Pilot Program

Structured engagement with clear deliverables and success criteria.

$6,000/month

2-month pilot = $12,000 total

Clear exit point after Month 1 if success criteria not met

What You Get

AI chat interface for proposal questions and research

RFP section generation from 30-40 historical proposals

Source citations on every response

Draft content in Massaro's voice, not generic AI

System tells you when information is missing

Full live pursuit test end-to-end

Month 1: Search + Chat

Ingest 30-40 historical proposals with relationship mapping
Deploy AI chat interface for proposal Q&A
Source citations on every response
Initial team onboarding

Month 2: Generation

RFP section generation from historical content
Draft responses in Massaro's voice
System learns from team feedback
Full live pursuit test end-to-end

Success Criteria

80%+ query satisfaction rate, 3+ team members using weekly, stakeholder confirmation of demonstrated value.

Long-Term Vision

As the system receives more data, more sophisticated capabilities become possible.

Future Platform Capabilities

Multi-Hop Reasoning: "What worked on projects we won vs lost?"

Automated Proposal Prep Packages with Full Traceability

Win/Loss Pattern Analysis Across Client Relationships

Cost Data Integration with Historical Benchmarks

Automated Compliance Audits Across Submissions

Managed Proposal Suite with Full Workflow Support

Partnership Structure

You control all accounts and own the codebase

No per-user licensing fees

Unlimited team access

Full documentation handoff

Clear exit terms at any phase

About Vambrace AI: Purpose-built AI systems. We partner with teams to build practical AI tools that drive real results—serving as a trusted advisor throughout AI adoption.

Next Steps

Ready to start when you are.

Confirm Scope

Review this proposal and discuss any questions

Submit Questions

Address any concerns or clarifications needed

Sign Engagement Letter

Finalize agreement and terms

Kickoff

Target start: February 2026

Document Access

Share proposal archive (30-40 proposals) via secure transfer

Luke Deasy

Founder, Vambrace AI

Background in technology, investment banking, and venture capital. BS in Statistics from Carnegie Mellon University.

luke@vambrace.ai(412) 807-1999

Appendix: Technical Details

Supplementary information for technical stakeholders. Subject to change based on implementation requirements.

Technology Stack

Cloud Infrastructure	Digital Ocean (SOC 2 Type II certified)
Knowledge Graph	Neo4j AuraDB with native vector search
LLM Provider	Anthropic Claude
Embeddings	Voyage AI voyage-3
Application	FastAPI + Next.js

Model Tiers (Cost Optimization)

Entity extraction	Claude Haiku (fast, low cost)
Query generation	Claude Sonnet (balanced)
Response generation	Claude Sonnet/Opus (quality focus)

Data Security

Data Residency	US only
LLM Data Policy	No training on your data

Timeline Dependencies

Document provision	Within 1 week of kickoff
Feedback turnaround	3 business days
Training session	2 hours, full team
Collaboration	Weekly syncs and progress reviews

Timeline assumes dependencies met. Delays extend timeline proportionally.

Estimated Monthly Operating Costs

Infrastructure	$150-300/month
Neo4j AuraDB	$65-200/month
API Costs (LLM + Embeddings)	$100-400/month
Optional Managed Support	$500/month

Costs vary based on usage volume. Tiered model approach optimizes API spend.

Appendix: ChatGPT vs SPA - Different Tools for Different Jobs

ChatGPT excels at what you already use it for. SPA addresses the core gap: "Every time we do this, it's a one-off."

Capability	ChatGPT (Current Approach)	Smart Proposal Assistant
Best For	✓One-off queries, general language generation, brainstorming	✓Persistent organizational knowledge, repeatable research
Organizational Memory	⚠Session-based. Resets each conversation.	✓Persistent knowledge base across all users and sessions.
Source Traceability	⚠Cannot cite your internal documents.	✓Every response traced to source with page numbers.
Team Access	⚠Individual accounts with separate histories.	✓Team-wide shared knowledge, unified access.
Relationship Mapping	⚠Treats uploaded documents as isolated text.	✓Connects projects, teams, clients, and outcomes.
When Info is Missing	⚠May generate plausible-sounding content.	✓Tells you explicitly when information is missing.
Data Location	⚠OpenAI cloud infrastructure.	✓Your infrastructure, your data, full control.

The bottom line: ChatGPT is excellent for one-off queries and general language tasks - keep using it for that. SPA solves a different problem: building persistent, shared organizational knowledge that compounds over time and does not reset with each conversation.