6.2 KiB
Exactly! You've identified the core value proposition. Let me reverse-engineer how he gets from raw data to "GS-14 John Smith at VA Office of Rural Health during their Q2 market research phase for simplified acquisitions under $200K."The breakthrough insight is that he's essentially running an intelligence operation, not a sales process. He's gathering three types of asymmetric information:
- WHO makes the decisions (names, roles, contact info)
- WHEN they make decisions (procurement cycles, market research windows)
- HOW they prefer to buy (simplified acquisition vs. full competition, preferred vehicles)
Then he times his engagement to hit the exact window when:
- The buyer is legally allowed to talk to him
- His competitors don't know an opportunity exists yet
- He can influence requirements before they're locked in
Your LLM skills could turn this from a manual, one-client-at-a-time process into an automated intelligence pipeline that identifies dozens of these specific targeting opportunities simultaneously.
The real money isn't in writing better proposals - it's in knowing about opportunities before they become competitive.
Reverse Engineering the Intelligence Pipeline
From Raw Data to Specific Targets: The Conversion Process
Step 1: USAspending.gov → Office Identification
Raw Input: $3.7B VA spending in PSC code XYZ His Process: Click individual contract awards to see awarding office Data Points Extracted:
- VA Office of Rural Health: $45M in awards
- VA Medical Center Baltimore: $23M in awards
- VA Benefits Administration: $12M in awards
Intelligence Output: "2-3 very specific offices within the VA"
Step 2: Award History → Buying Pattern Recognition
His Analysis Method: Look at each office's individual awards over 4 years Pattern Recognition:
- Office A: Awards $2M-5M contracts through full competition
- Office B: Awards $150K-250K contracts through simplified acquisition
- Office C: Uses IDIQ vehicles, awards task orders monthly
Intelligence Output: "Some offices openly compete while others use simplified acquisitions"
Step 3: Contract Details → Decision Maker Intelligence
Data Mining Process:
- Contract award documents show Contracting Officer names
- Performance Work Statements reveal Program Manager requirements
- Past performance reviews show technical evaluators
Intelligence Output: "GS-14 John Smith" (the actual decision maker)
Step 4: Award Timing → Procurement Cycle Mapping
His Timing Analysis:
- Q1: Market research notices published
- Q2: RFIs released, industry days held
- Q3: RFPs published
- Q4: Awards made
Intelligence Output: "Q2 market research phase"
Step 5: Dollar Patterns → Acquisition Strategy
Threshold Analysis:
- 60% of awards under $250K (simplified acquisition)
- 30% of awards $250K-$10M (full competition)
- 10% of awards over $10M (major systems)
Intelligence Output: "Simplified acquisitions under $200K"
The Data Sources He's Actually Using (But Doesn't Fully Reveal)
Primary Sources
- USAspending.gov - Contract awards, dollars, offices
- SAM.gov - Current opportunities, past solicitations
- Federal Business Opportunities Archive - Historical RFPs/sources sought
Hidden Sources (Implied)
- GovWin/Deltek - Contracting officer databases, pipeline intelligence
- LinkedIn Government - Decision maker profiles, org charts
- Agency budget documents - Future spending priorities
- FOIA requests - Internal procurement forecasts
The LLM Automation Opportunity
Data Aggregation Prompts
"Extract from these 200 contract awards: contracting officer names, program manager emails, award timing patterns, dollar thresholds, and procurement vehicles used"
Pattern Recognition Prompts
"Analyze this office's 4-year award history and identify: 1) Preferred contract vehicles, 2) Seasonal award patterns, 3) Dollar threshold preferences, 4) Incumbent contractor rotation patterns"
Relationship Mapping Prompts
"Cross-reference these contracting officers with: 1) Their LinkedIn profiles, 2) Professional conference speaker lists, 3) Industry publication quotes, 4) Government directory listings to build complete contact profiles"
Timing Prediction Prompts
"Based on this office's historical procurement cycles, predict: 1) When market research will begin for FY26 requirements, 2) Optimal engagement windows, 3) Key milestone dates for relationship building"
The Million Dollar Process Map
Phase 1: Office Intelligence (Weeks 1-2)
- Mine USAspending for office-level spending patterns
- Identify 3-5 offices with consistent spending in your space
- Map each office's preferred acquisition methods
Phase 2: People Intelligence (Weeks 3-4)
- Extract contracting officer and program manager names from awards
- Build LinkedIn/contact profiles for key decision makers
- Identify their professional networks and interests
Phase 3: Timing Intelligence (Weeks 5-6)
- Map each office's historical procurement cycles
- Identify market research windows for next 12 months
- Create engagement calendar with specific target dates
Phase 4: Relationship Execution (Weeks 7-52)
- Engage during legal market research phases
- Submit targeted RFI responses
- Attend industry days and networking events
- Build relationships before RFPs drop
The Real Secret Sauce
He's not just finding opportunities - he's manufacturing competitive advantages by:
- Information Asymmetry: Knowing details about buyers that competitors don't
- Timing Asymmetry: Engaging during windows when competitors aren't active
- Relationship Asymmetry: Having existing relationships when RFPs are released
The $25K-$50K/month he mentions isn't from winning more contracts - it's from winning contracts with less competition because he's positioned himself before the crowd arrives.
Your LLM Edge
You could systematically execute this intelligence gathering across 50+ offices simultaneously, creating a continuous pipeline of "GS-14 John Smith" level targeting intelligence that would take human analysts months to develop manually.
The key insight: This isn't market research - it's competitive intelligence gathering that creates unfair advantages in timing and positioning.