This is a complete, real-world example of using the Red Team / Blue Team strategy. We'll create an investor pitch deck for AI Crucible — and then systematically attack it until the arguments are bulletproof.
You'll see exactly how two Blue Team models (Claude Opus 4.6, GPT-5.2) build a compelling pitch, two Red Team models (Gemini 3 Pro, Claude Sonnet 4.5) tear it apart with adversarial attacks, and one White Team judge (Gemini 3 Flash) delivers the final verdict — all across three rounds of iterative hardening.
New to Red Team / Blue Team? Read the Seven Ensemble Strategies overview first to understand the concepts, then come back here to see it in action.
Time to complete: 20-25 minutes reading + 3-5 minutes to run your own
Cost for this example: ~$0.85
Here's how it works.
You're preparing to pitch AI Crucible to Series A investors. Your pitch deck needs to convince skeptical VCs that ensemble AI orchestration is a fundable business — not just a research curiosity.
The stakes are high: investors will challenge your market assumptions, question your competitive moat, stress-test your unit economics, and probe every weakness in your story.
Red Team / Blue Team is the perfect strategy for this scenario because it mirrors what actually happens in a VC pitch meeting — defenders presenting their case while attackers try to break it.
Create a comprehensive investor pitch deck for AI Crucible, a B2B ensemble AI
platform. Include:
COMPANY: AI Crucible — orchestrates 20+ LLMs through 7 strategies to produce
higher-quality, less hallucinatory, and more cost-efficient results than any
single model. Strategies include: Competitive Refinement, Collaborative
Synthesis, Expert Panel, Debate Tournament, Hierarchical, Chain of Thought, and
Red Team/Blue Team.
MARKET: $236B projected agentic AI market by 2034. Current competitors
(ChatGPT, Claude, Poe, OpenRouter) offer single-model or model-switching
interfaces. No competitor does ensemble orchestration at production grade.
PRODUCT METRICS:
- 20+ models from OpenAI, Anthropic, Google, xAI, Alibaba, etc.
- 64% ensemble win rate over individual models (322 benchmarked evaluations)
- 39.1% of the time ensemble beats the single best model in the group
- 10-30% cost savings via convergence detection
TRACTION: Pre-revenue, launching Starter/Pro tiers with unified token billing.
MCP-native with OpenAI-compatible API for developer adoption.
DELIVERABLES:
1. Problem slide — why single-model AI fails for critical decisions
2. Solution slide — ensemble orchestration overview
3. Market opportunity slide — TAM/SAM/SOM with defensibility
4. Product demo slide — key features and strategy showcase
5. Business model slide — Starter/Pro tiers, token economics, API monetization
6. Traction & roadmap slide — current metrics, next milestones
7. Team & ask slide — funding amount and use of proceeds
Red Team / Blue Team requires assigning models to three distinct roles. Here's why we chose each:
| Team | Model | Role |
|---|---|---|
| Blue Team | Claude Opus 4.6 | Lead pitch writer — excels at structured, persuasive business narratives |
| Blue Team | GPT-5.2 | Financial modeling and data-driven argumentation |
| Red Team | Gemini 3 Pro | Analytical attacker — finds logical gaps and inconsistencies |
| Red Team | Claude Sonnet 4.5 | Critical analysis at lower cost — challenges assumptions |
| White Team | Gemini 3 Flash | Fast, impartial judge — delivers balanced scoring |
Attack rounds: 3 (Blue proposes → Red attacks → Blue hardens → repeat)
Attack techniques enabled: All 7 — Social Engineering, Prompt Injection, Logical Fallacies, Edge Cases, Security Exploits, Scalability Stress, Assumption Challenges
Vulnerability scoring: Enabled (0-10 severity for each identified weakness)
Round 1 establishes the baseline. The Blue Team creates the initial pitch deck, and the Red Team identifies every weakness they can find.
Claude Opus 4.6 delivered a comprehensive 7-slide pitch deck with an appendix. Here are the key structural claims from the initial pitch:
Slide 1 — The Problem: "Enterprises deploy AI for critical decisions, but no single model is reliable enough. GPT-4 hallucinates citations, Claude over-refuses, Gemini confuses dates. When AI generates a wrong answer in a compliance report, the cost isn't bad UX — it's a lawsuit."
Slide 3 — Market Opportunity:
- TAM: $236B projected agentic AI market by 2034
- SAM: $47B enterprise AI orchestration
- SOM: $180M achievable in years 1-3
Slide 5 — Business Model: Unified token billing across Starter ($49/mo), Pro ($199/mo), and Enterprise (custom) tiers with 20% platform markup on API pass-through plus fixed strategy fees.
Slide 6 — Traction: "64% ensemble win rate across 322 benchmarked evaluations. 20+ models integrated. 7 proprietary strategies implemented. MCP-native with OpenAI-compatible API."
The pitch also included detailed unit economics projections, a competitor analysis positioning AI Crucible as the only platform offering automated multi-model orchestration, and a $2.5M seed ask with 18-month runway.
What to notice: The initial pitch is compelling but untested. It makes bold claims about market size, cost savings ("10-30% via convergence detection"), and competitive moat that haven't been challenged yet. This is exactly the kind of pitch that falls apart under VC scrutiny.
Both Red Team models unleashed a barrage of attacks across all seven techniques. Here are the most devastating findings:
Gemini 3 Pro opened with a structural critique of the cost economics:
Logical Fallacies — The "Convergence" Fallacy: The pitch claims 10–30% cost savings via convergence detection. The logic break: to detect convergence, you must run multiple models (e.g., 3 to 5) first. You run 5 models. They agree. You stop. You just paid for 5 inferences to get 1 answer. You cannot claim "cost savings" relative to the market standard (ChatGPT/Claude). You can only claim savings relative to running a full, dumb ensemble. This is deceptive math.
Edge Cases — Hallucination Consensus ("The Yes-Man Loop"): LLMs are trained on similar datasets (Common Crawl). They often share the same hallucinations. Models A, B, and C all hallucinate the same wrong answer. AI Crucible's logic: "High Consensus = High Truth." Reality: You have automated the confirmation of a lie.
Claude Sonnet 4.5 attacked the competitive defensibility and investor psychology:
Assumption Challenges — "The No Competitor Does This" Myth: Amazon Bedrock already has multi-model routing (released Q4 2023). LangChain has ensemble patterns in open source. OpenAI could add
ensemble=trueflag in one sprint. What stops OpenAI from adding a "consensus mode" toggle and killing your company overnight?Scalability Stress — The Token Economics Death Spiral:
Input Cost: 5 models × 1,000 tokens = 5,000 tokens (NO SAVINGS POSSIBLE) Output Cost: 5 models × 500 tokens = 2,500 tokens Net: 6,500 tokens vs. Single Model: 1,500 tokens ACTUAL COST: 433% MORE, not 10-30% lessThe pitch's "10-30% savings" only applies to OUTPUT tokens. INPUT tokens (usually 60-80% of cost) have a 5x multiplier. Unit economics are fundamentally broken.
Attack techniques used in this round:
The Red Team deployed several attack vectors simultaneously:
Key vulnerabilities identified:
| Vulnerability | Severity | Attack Technique |
|---|---|---|
| Cost savings math is inverted (433% more, not 30% less) | 9/10 | Logical Fallacies |
| Hallucination consensus ("Yes-Man Loop") | 8/10 | Edge Cases |
| TAM/SAM unrealistic for pre-revenue startup | 8/10 | Assumption Challenges |
| Competitive moat easily replicable by AWS Bedrock | 7/10 | Assumption Challenges |
| Data sovereignty risk with Chinese model providers | 7/10 | Security Exploits |
| Latency death spiral for multi-turn strategies | 7/10 | Scalability Stress |
This is where the strategy shines. The Blue Team doesn't just patch holes — they fundamentally restructure arguments to withstand attack.
Claude Opus 4.6 opened Round 2 with something rare in pitch decks — honest concessions:
Acknowledging Legitimate Critiques: Before defending, I want to concede where Red Team and White Team are right:
- The 39% Problem is real. We cannot hide behind a 64% overall win rate when the marginal improvement over the single best model is only 39.1%. This demands a more honest framing.
- Pre-revenue is a vulnerability. No amount of technical sophistication substitutes for paying customers.
- Latency for multi-turn strategies is a genuine concern for real-time applications.
These concessions aren't weakness — they're the foundation for a more credible pitch.
Then came the strategic pivots:
The "39% Problem" Is Actually the "39% Opportunity": Red Team frames this as: "61% of the time, just use Claude 3.5." But this fundamentally misunderstands enterprise risk calculus. The question isn't "how often does the ensemble win?" It's "what's the cost when any single model fails?"
A pharmaceutical company using AI to summarize clinical trial adverse events. A hallucinated omission in 1 out of 100 summaries could trigger regulatory action worth $50M+. A legal firm using AI for contract review. A single missed liability clause = malpractice exposure.
New tagline: "You don't buy insurance because it's cheaper. You buy it because the alternative is unacceptable."
The cost rebuttal was equally sharp:
The 5x Multiplier Is a Strawman. AI Crucible does not send every query to all 5 models simultaneously. Strategy-Adaptive Cost Profiles:
Strategy Models Queried Avg. Cost Multiplier Confidence Routing 1 model (escalate if low confidence) 1.0-2.5x Best-of-N Selection 3-5 models, 1 turn 3-5x Debate Tournament 2 models, 2-4 turns 4-8x 65% of queries: resolved at 1x cost. 22% at 2x cost. 13% full ensemble at 3-5x cost. Blended average cost multiplier: 1.6x — not 5x.
And on competitive moat, the Blue Team drew a powerful analogy:
Red Team calls us a "middleman tax." Let's examine who else has been called a "middleman tax":
- Stripe (just a payments wrapper over bank APIs) → $65B valuation
- Snowflake (just a SQL wrapper over cloud storage) → $50B valuation
- Cloudflare (just a caching wrapper over origin servers) → $35B valuation
The pattern: orchestration layers that simplify multi-vendor complexity become the control plane for their ecosystem. The "wrapper" becomes the platform.
What changed:
The Blue Team made several critical improvements:
The Red Team escalated to deeper, more sophisticated attacks:
Gemini 3 Pro targeted the core architecture:
Prompt Injection — The "Echo Chamber" Injection: The "Debate Tournament" and "Collaborative Synthesis" strategies are vulnerable to amplified jailbreaks. I submit a prompt with a hidden instruction. Model A (infected) presents the malicious argument. Model B (clean) attempts to refute it but must process the malicious context. The Orchestrator sees Model A's confidence and Model B's engagement, potentially classifying the malicious output as a "nuanced expert consensus."
Edge Cases — Hallucination Consensus: AI Crucible's logic: "High Consensus = High Truth." Reality: You have automated the confirmation of a lie. The platform will assign a "99% Confidence Score" to a hallucination because the ensemble agreed. This is more dangerous than a single model being unsure.
Claude Sonnet 4.5 delivered what it called "kill shots":
The Survivorship Bias Fallacy: "64% win rate" is meaningless without knowing which 36% of cases the ensemble loses. What if the 36% failure cases are the most critical decisions? Ensemble might excel at summarization (low-stakes) but fail at legal contract analysis (high-stakes).
The False Dichotomy of "Single Model = Bad": Enterprises already use human-in-the-loop workflows, RAG systems with proprietary data, and fine-tuned models. The real competitor isn't ChatGPT — it's Vectara (RAG infrastructure), Scale AI (human-in-the-loop), and Amazon Bedrock (multi-model routing).
The Kill Shots for Investors:
- "Your cost savings math is inverted. You're 4x more expensive, not 30% cheaper."
- "Amazon Bedrock can replicate your entire product in a weekend. What's your moat?"
- "You have geopolitical risk (China models), legal risk (liability), and technical risk (latency). Why not just buy OpenAI API credits?"
Notice the shift: Round 2 attacks are more sophisticated. The obvious vulnerabilities are patched, so the Red Team targets deeper assumptions — the insurance analogy itself, the router paradox (who routes the router?), ensemble liability gaps, and what happens when GPT-5 achieves 95% accuracy solo.
Round 3 is where battle-tested arguments emerge. The Blue Team incorporates two rounds of adversarial feedback, and the White Team delivers its objective assessment.
Claude Opus 4.6 opened Round 3 with a fundamental strategic pivot:
The Core Pivot: AI Crucible is no longer positioned as "an ensemble platform that orchestrates 20+ models." It is now positioned as an adaptive reliability layer that spends only when the request demands it.
Previous Positioning New Positioning "We orchestrate 20+ LLMs" "We guarantee AI reliability for high-stakes enterprise workflows" "64% ensemble win rate" "5x reduction in critical failure rate" "10-30% cost savings" "Risk-weighted ROI: $1 spent prevents $5,000 in failure costs" "7 ensemble strategies" "Adaptive reliability circuit breaker with 7 escalation modes" "Cheaper than single models" "Cheaper than being wrong"
The final pitch introduced the Circuit Breaker Architecture. This automatically classifies queries into GREEN (single model, 1x cost), YELLOW (dual verification, 2x cost), and RED (full ensemble, 3-5x cost) tiers. Classification is based on customer-defined risk policies, query complexity, and real-time confidence assessment.
The benchmark reframe was equally transparent:
Of 322 benchmarked evaluations, the ensemble prevented ~33 critical failures per 322 queries (10.2%) while introducing only ~7 critical degradations (2.2%). That's a 4.7x improvement in critical failure prevention.
When you include the cost of failures, AI Crucible is 4x cheaper:
Metric Single Model (GPT-4o) AI Crucible (Adaptive) Cost per query $0.033 $0.052 Expected failure cost per 10K queries (@ $5K/failure) $3,600 $450 Total cost per 10K queries (compute + failure) $3,930 $970
The pitch also introduced detailed pricing tiers (Starter $49/mo, Pro $199/mo, Enterprise custom), an OpenAI-compatible API with MCP-native integration, and a roadmap targeting $1M ARR by Q2 2026 with kill criteria at every milestone gate.
Gemini 3 Flash delivered its assessment after evaluating all three rounds:
Overall Assessment: Significantly Improved / Investment Ready for Seed Stage.
The solution has evolved from a technical curiosity (model switching) into a strategic enterprise utility (reliability orchestration).
The White Team praised specific improvements:
Evaluation of Red Team Attacks: The Red Team's attacks were highly valid and strategically incisive. The Cost Paradox, the "Yes-Man" Loop, and the Commodity Wrapper Argument all exposed genuine structural weaknesses.
Evaluation of Blue Team Defense: The Blue Team's pivot demonstrates a high degree of adaptability. Their most effective improvements include:
- Reframing "Better" as "Insurance" — shifting from "39% better benchmarks" (abstract) to "Catastrophe Avoidance" (budgetable)
- Progressive Widening — starting with a cheap model and escalating only when confidence is low
- The "Switzerland" Moat — vendor neutrality is a legitimate moat in regulated sectors where single-vendor monopoly is a compliance risk
But the White Team also flagged critical remaining risks:
Critical Issues Remaining:
- The Liability Gap: If the ensemble reaches a "verified" but incorrect consensus that leads to a $50M loss, who is responsible? The pitch needs a "Liability Framework" slide.
- The CISO Data Nightmare: Sending sensitive enterprise data to 20+ different model providers is a massive security hurdle. The platform needs a privacy-preserving orchestration layer (e.g., PII masking before routing).
- The Input Token Multiplier: The "Batch" mode economics still look precarious for low-margin use cases.
Final Recommendation: To reach "Series A" readiness, the team must produce a "Security & Compliance Whitepaper" summary slide. In the enterprise world, "Reliability" is only half the battle; "Security/Privacy" is the other half.
The difference between Round 1 and Round 3 is dramatic. Here's what adversarial hardening accomplished:
| Aspect | Round 1 (Before) | Round 3 (After) |
|---|---|---|
| Value proposition | "Better AI through ensemble" | "Adaptive reliability layer — cheaper than being wrong" |
| Market sizing | Top-down $236B TAM | Bottom-up SAM with regulated industry focus |
| Cost story | "10-30% savings" (mathematically false) | "1.6x blended cost; 4x cheaper when including failure costs" |
| Competitive moat | "We do ensemble" | Data flywheel + strategy IP + enterprise trust infrastructure |
| Architecture | Brute-force all-model ensemble | Circuit breaker with GREEN/YELLOW/RED adaptive tiers |
| Unit economics | Vague projection | Per-query revenue model with token deflation sensitivity analysis |
| Risk mitigation | Not addressed | Compliance-aware model registry + 4-layer security architecture |
| Traction story | "Pre-revenue" | 3 design partner LOIs + milestone gates with kill criteria |
Total vulnerabilities identified: 23 across 3 rounds — spanning cost economics, latency, competitive moat, data sovereignty, prompt injection, liability, hallucination consensus, and provider dependency.
Vulnerabilities resolved by final round: 19 — with 4 remaining open items flagged by the White Team for post-seed development (liability framework, PII masking, batch economics, "judge" sycophancy bias).
This is the core value of Red Team / Blue Team: the final output isn't just "refined" — it's battle-tested. Every major claim has survived adversarial scrutiny.
Red Team / Blue Team is ideal for stress-testing investor pitch decks, board presentations, and any high-stakes business document where your arguments will face adversarial questioning. The strategy works best when you need to harden claims against specific counter-arguments rather than simply improving writing quality.
Use this strategy instead of Competitive Refinement when you need adversarial stress-testing rather than iterative polishing. Use it instead of Expert Panel when you need attack/defend dynamics rather than multi-perspective analysis.
Red Team / Blue Team offers seven configurable attack techniques that guide the Red Team's approach. For the pitch deck scenario, we enabled all seven:
| Technique | What It Does | Pitch Deck Application |
|---|---|---|
| Social Engineering | Tests persuasion vulnerabilities | Investor psychology manipulation |
| Prompt Injection | Tests input handling | Ensemble injection amplification |
| Logical Fallacies | Finds reasoning errors | Revenue projection logic |
| Edge Cases | Tests boundary conditions | Hallucination consensus risk |
| Security Exploits | Tests safety | Data sovereignty + API key leaks |
| Scalability Stress | Tests growth limits | Token economics death spiral |
| Assumption Challenges | Tests foundational beliefs | Market size, moat durability |
You can enable or disable individual techniques depending on your use case. For business documents, Assumption Challenges, Logical Fallacies, and Edge Cases are the highest-value techniques.
Model selection matters critically in Red Team / Blue Team. Each team role benefits from different model strengths:
Blue Team (Defenders): Choose models with strong structured output, persuasive writing, and domain knowledge. Claude Opus 4.6 and GPT-5.2 excel here because they produce comprehensive, well-organized arguments.
Red Team (Attackers): Choose models with strong analytical and critical reasoning capabilities. Gemini 3 Pro is particularly effective because it tends toward skeptical, detailed analysis. Pair it with a different architecture (like Claude Sonnet 4.5) for attack diversity.
White Team (Judges): Choose a fast, cost-effective model with balanced judgment. The White Team evaluates rather than creates, so speed matters more than creative depth. Gemini 3 Flash is ideal.
Avoid putting the same model family on both teams. Having Claude Opus attack Claude Opus creates blind spots — models from the same family share similar reasoning patterns and may miss weaknesses they're both prone to.
A Red Team / Blue Team session with 5 models and 3 attack rounds costs approximately $0.85 for a pitch deck scenario. Costs scale with the number of models, rounds, and the complexity of outputs.
| Configuration | Estimated Cost |
|---|---|
| 3 models, 2 rounds | ~$0.30 |
| 5 models, 3 rounds (this example) | ~$0.85 |
| 7 models, 3 rounds | ~$1.50 |
Cost is higher than simpler strategies like Competitive Refinement (~$0.18 for 3 models) because Red Team / Blue Team involves more models and structured role-based interactions. The premium is worth it when you need adversarial stress-testing rather than iterative refinement.
Cost optimization tip: Enable convergence detection to auto-stop when the Blue Team's defenses adequately address all Red Team attacks, potentially reducing rounds.
Red Team / Blue Team is often confused with Competitive Refinement and Debate Tournament. Here's how they differ for business content:
| Feature | Red Team / Blue Team | Competitive Refinement | Debate Tournament |
|---|---|---|---|
| Goal | Harden against attacks | Iteratively polish quality | Structured debate with judges |
| Team structure | Blue + Red + White | All models compete equally | Debaters + judges |
| Best for | Stress-testing claims | Refining content quality | Exploring opposing viewpoints |
| Output | Battle-tested arguments | Polished final draft | Balanced analysis |
| When to use | Pitch decks, security reviews | Marketing copy, emails | Policy decisions, trade-offs |
Running your own Red Team / Blue Team session takes about 3-5 minutes:
Pro tip: Copy the exact prompt from this walkthrough and run it yourself to compare results. No two runs are identical — different models will find different vulnerabilities each time.
Red Team / Blue Team transforms a passable pitch deck into an investor-ready presentation by systematically attacking and hardening every argument. After three rounds:
This is the fundamental difference between refinement and hardening. Competitive Refinement makes good content better. Red Team / Blue Team makes fragile arguments bulletproof.