Multi-agent competitive simulation and proposal generation for any competitive opportunity.
The first AI tool that doesn't just draft proposals — it simulates the entire competitive landscape before you write a single word. Works across government contracting, academic grants, foundation funding, community development, commercial procurement, and international tenders.
Every AI proposal tool on the market optimizes the production phase — drafting, compliance checking, formatting. But research shows that winning organizations spend the majority of their effort on capture strategy, not production. The strategy layer — competitive intelligence, win theme development, evaluator modeling — is where bids are won or lost, and no tool addresses it.
RFP Battlecard runs a multi-agent competitive simulation before generating proposals:
- Profiles 8-12 competitors with specific program-level intelligence
- Simulates competitor bids — each competitor agent produces 3 bid variants
- Scores all bids through an adversarial evaluator agent using actual Section M criteria
- Iterates 7+ rounds — each round hardens your strategy against competitor moves
- Generates complete proposal drafts — all volumes, formatted to Section L specs, output as .docx
- Benchmarks the output — scores against 8 quality dimensions (BQB framework)
No other tool — commercial, open source, or consulting firm — does steps 1-4.
Tested against a real competitive solicitation:
| Dimension | Score (0-10) |
|---|---|
| Compliance Coverage | 7 |
| Evaluation Alignment | 4* |
| Competitive Differentiation | 8 |
| Past Performance Credibility | 3* |
| Risk Identification | 9 |
| Competitive Intelligence Depth | 8 |
| Strategic Coherence | 8 |
| Actionability | 7 |
| Total | 54/80 |
*Low scores reflect the test company's actual qualification gaps — the tool correctly identified these as showstoppers rather than masking them. For a well-qualified bidder, projected score is 65-72.
| Approach | Typical Score |
|---|---|
| Manual (no AI) | 25-40 |
| Generic AI (ChatGPT single-pass) | 30-45 |
| Commercial RFP tools (Responsive, Loopio) | 35-50 |
| AI-native tools (pWin.ai, AutogenAI) | 40-55 |
| Human consultant (Shipley-trained) | 50-65 |
| RFP Battlecard | 54-75 |
| Human + RFP Battlecard | 70-80 |
This is a Claude Code plugin.
# Clone to your Claude plugins directory
git clone https://github.com/YOUR_USERNAME/rfp-battlecard.git ~/.claude/plugins/rfp-battlecardThen restart Claude Code. The skill auto-registers.
Dump all your files into a single folder — RFP docs, org profile, resumes, past performance, certifications, whatever you have. Flat or organized into subfolders — doesn't matter.
my-rfp-folder/
├── RFP-2026-0042.pdf # The actual RFP (required)
├── Amendment-001.pdf # Amendments, Q&A
├── company-overview.docx # Your org profile
├── john-smith-resume.pdf # Key personnel
├── case-study-HUD-2024.pdf # Past performance
├── sam-registration.pdf # Certifications
└── teaming-agreement.docx # Partners/subs
Full Bid Pipeline:
/rfp:bid /path/to/my-rfp-folder
Auto-classifies all files → competitive intelligence → multi-agent simulation (7 rounds) → full proposal drafts → .docx export → quality benchmark. After your first bid, the skill offers to save your org profile so future bids only need the RFP document.
Time: 2-4 hours | Output: 25+ documents in organized directory structure
Quick Evaluation (Go/No-Go):
/rfp:evaluate /path/to/my-rfp-folder
Fast assessment: auto-classify files, identify gate requirements, profile top 5 competitors, estimate win probability, recommend Go/No-Go.
Time: 15-30 minutes | Output: Single evaluation summary
Benchmark Existing Bid:
/rfp:benchmark /path/to/bid/folder
Score any bid package (yours or a competitor's) against the BQB framework.
Time: 15-20 minutes | Output: Validation report with scores and recommendations
After your first bid, the skill saves your org profile to ~/.rfp-battlecard/org-profile/. On future bids, just drop the RFP PDF in a folder and run — your org data loads automatically.
bid/
├── 00-competitive-intelligence/ # Competitor profiles, market brief, SWOT
├── 01-simulation/ # 7+ rounds of competitive simulation
├── 02-proposal/ # Complete proposal volumes (I-IV)
├── 03-supporting/ # Win themes, risk register, tech concepts
├── 04-validation/ # BQB benchmark report
└── docx/ # All documents as .docx (mirrored structure)
| Agent | Role |
|---|---|
| Evaluator | Government SSEB evaluator — scores against Section M with line-by-line compliance checks |
| Competitors (3-5) | Top competitors, each producing 3 bid variants based on real capabilities |
| Client | Your company — generates and refines 3 strategies per round |
| Round | Focus |
|---|---|
| 1 | Baseline bids — all agents produce initial strategies |
| 2 | Gate compliance — creative past performance framing |
| 3 | Discriminator sharpening — what competitors can't match |
| 4 | Task order positioning — which work you can realistically win |
| 5 | Small business strategy — socioeconomic optimization |
| 6 | Risk mitigation — address every evaluator-identified weakness |
| 7 | Final optimization — consolidated strategy with Go/No-Go |
| 8-10 | Stress testing — adversarial rounds targeting your weaknesses |
| Feature | Commercial Tools | RFP Battlecard |
|---|---|---|
| Competitor bid modeling | No | Yes — 3-5 competitors × 3 variants |
| Adversarial evaluator scoring | No | Yes — Section M line-by-line |
| Iterative strategy refinement | No (single-pass) | Yes — 7+ rounds |
| Ghost theme generation | No | Yes — per competitor |
| Go/No-Go decision framework | Basic checklist | Quantitative multi-gate scoring |
| Creative past performance framing | Template-based | Strategy-driven narrative |
| Dual-track recommendations | No | Yes — prime + sub backup |
| Win probability estimate | No | Yes — with confidence interval |
| Sector | What It Does |
|---|---|
| US Federal Government | Full FAR compliance, Section L/M parsing, OCI analysis, SB plan, clearance assessment |
| State/Local Government | Simplified compliance, DBE/MBE/WBE goals, local preference analysis |
| Defense/Intelligence | Security gates, ITAR, classified requirements, DD254 |
| Academic Grants (NSF/NIH/DOE) | Intellectual merit, broader impacts, PI qualifications, budget justification |
| Foundation Grants | Theory of change, impact metrics, funder alignment, sustainability plan |
| Community Development (CDBG/HUD) | Needs assessment, equity analysis, stakeholder engagement, match funding |
| Commercial/Corporate | ROI analysis, SLA commitments, pricing strategy, vendor references |
| International (USAID/World Bank/EU) | Country context, local partners, donor priorities, MEL frameworks |
| SBIR/STTR | Innovation narrative, commercialization plan, technical objectives |
| Quick Eval (any sector) | Go/No-Go recommendation in 15 minutes |
- Claude Code CLI
- Python 3 with
python-docx(pip3 install python-docx) popplerfor PDF extraction (brew install poppler)
- 68% of proposal teams now use generative AI (doubled from 34% in 2023)
- Average RFP win rate: 45% (2025) — top firms reach 70%+
- Price-to-Win studies cost $20K-$300K and take weeks
- This tool compresses that to hours
- Currently a Claude Code plugin — standalone (platform-agnostic) version planned
MIT
PRs welcome. Key areas for improvement:
- FPDS/USASpending.gov data integration for automated competitor research
- Price-to-Win modeling with historical rate analysis
- Agency-specific evaluation pattern libraries
- Integration with SAM.gov opportunity tracking