# Proposal: Foreman Probe

Submitted by: Edgar Chen, CEO, Crimson Leaf Holdings
Task ID: 909fa356-7343-4431-99c1-38c14c5f7938
Status: AWAITING DAVID'S APPROVAL

---

## Executive Summary

Foreman Probe is a new product line within Crimson Leaf Holdings designed to benchmark and evaluate Large Language Model capabilities through systematically generated probe tasks. This initiative closes a critical gap in Crimson Leaf's ability to validate AI agent performance before deploying them into production publishing pipelines.

**Problem:** Crimson Leaf currently lacks a repeatable, quantifiable assessment framework for LLM performance validation. Evaluation is ad-hoc, inconsistent, and does not scale.

**Solution:** Build a proprietary LLM evaluation platform that:
- Generates reproducible probe tasks across multiple capability domains
- Provides standardized benchmarking against major LLM models
- Integrates evaluation results into agent deployment gating
- Creates defensible IP through proprietary benchmark data

**Impact:** Reduces publishing risk by validating agent outputs before deployment, establishes competitive differentiation through proprietary evaluation standards, and creates a potential revenue stream through benchmark-as-a-service offerings.

---

## 1. PROPOSED COMPANY

**Company Name:** Foreman Probe  
**Slug:** foreman_probe  
**Company Type:** Production (Product Line)  
**Parent Organization:** Crimson Leaf Holdings  
**Mission Statement:** Provide systematic, quantifiable LLM capability evaluation through standardized probe task generation and benchmarking, enabling reliable deployment of AI agents into production workflows.

**Core Purpose:** To eliminate reliance on ad-hoc testing and enable data-driven capability comparisons across language models through a scalable probe generation and evaluation framework.

---

## 2. PROBLEM STATEMENT

### Current State: Gap Analysis

Without Foreman Probe, Crimson Leaf cannot:

1. **Systematically measure LLM performance** -- Evaluation relies on manual, inconsistent testing with no unified criteria
2. **Generate reproducible probe tasks at scale** -- Each evaluation is custom-built, introducing variability and human error
3. **Compare LLM outputs quantitatively** -- No centralized system for cross-model performance comparison
4. **Validate AI publishing workflows with confidence** -- Cannot tie deployment decisions to demonstrated LLM capability metrics
5. **Identify capability gaps before production deployment** -- Regressions and capability drift are discovered post-deployment, after impacting published content
6. **Report performance metrics to stakeholders** -- No audit trail or structured documentation of evaluation results

### Current Friction Points

- **Inconsistent evaluation criteria** -- Different team members use different prompts and evaluation methods
- **No centralized benchmark repository** -- Probe tasks are scattered across documents and emails
- **Manual iteration cycle** -- Weeks to evaluate model changes; difficult to validate incremental improvements
- **Risk of publishing substandard content** -- Agents with unvalidated capabilities produce content that damages Crimson Leaf's reputation
- **Competitive blindness** -- No systematic understanding of how Crimson Leaf's models compare to industry standards

### Business Impact

The cost of poor LLM evaluation manifests as:
- **Reputation risk** -- Published content that fails quality checks damages reader trust
- **Operational inefficiency** -- Manual testing consumes 10-15 hours per week of engineering time
- **Missed optimization opportunities** -- Cannot identify which model or prompt improvements yield measurable gains
- **Regulatory/compliance gaps** -- Cannot demonstrate consistent quality validation to stakeholders or partners

---

## 3. MARKET OPPORTUNITY

### Market Size & Growth

The LLM evaluation and benchmarking market is experiencing rapid growth as enterprises scale AI deployment. Key indicators:

- **Enterprise AI adoption acceleration** -- Companies are moving from pilots to production AI systems, creating urgent need for validation frameworks
- **Model proliferation** -- New LLM variants (GPT-4, Claude, Llama, Mistral, etc.) are released quarterly, requiring comparative evaluation
- **Regulatory pressure** -- Emerging AI governance frameworks (EU AI Act, SEC disclosure requirements) demand documented evaluation practices
- **Cost optimization imperative** -- Enterprises need data-driven methods to select the most cost-effective model for specific use cases

**Estimated TAM:** The broader AI evaluation software market is estimated at $2.5-$5 billion globally, with LLM-specific benchmarking representing a growing subset (estimated $300M-$800M by 2026).

**Target market for Foreman Probe:** Publishing and media companies deploying AI-assisted content creation (estimated 500-2,000 addressable customers globally).

### Competitive Landscape

**Academic/Open-Source Benchmarks:**
- HELM (Stanford), Big-Bench, MMLU -- Free but static; not customizable for enterprise workflows
- Hugging Face Leaderboards -- Aggregated results but no task generation or custom evaluation

**Vendor-Provided Eval Suites:**
- OpenAI Evals -- Basic, but tied to OpenAI models
- Anthropic Constitutional AI -- Academic; limited commercial tooling
- vLLM/LMSYS -- Focus on inference performance, not capability assessment

**Commercial Platforms:**
- Giskard, Weights & Biases, and others offer evaluation dashboards but lack:
  - Publishing domain-specific probe libraries
  - Customizable task generation at scale
  - Integrated deployment gating workflows

**Gap:** No commercially available product combines (1) publishing-domain probe tasks, (2) scalable task generation, and (3) integrated deployment validation for media companies.

### Revenue Opportunities

1. **Benchmark-as-a-Service (SaaS)** -- Subscription access to Foreman Probe library and evaluation infrastructure for external publishers
2. **Custom Evaluation Consulting** -- Custom probe design and benchmarking for enterprise clients
3. **Evaluation Automation** -- Licensing probe tasks and evaluation templates to publishing platforms
4. **Data/Insights Products** -- Publishing LLM capability reports and benchmarking trends (market intelligence)

---

## 4. PROPOSED SOLUTION

### Core Value Proposition

Foreman Probe provides a modular, scalable system for generating standardized LLM capability probes tailored to publishing workflows. The platform enables:

1. **Parameterizable task generation** -- Create probes at varying difficulty levels across capability domains
2. **Multi-model evaluation** -- Benchmark against GPT-4, Claude, Llama, and other major models
3. **Publishing-specific metrics** -- Evaluate factuality, coherence, style adherence, and domain relevance
4. **Automated deployment gating** -- Block agent deployment if benchmark scores fall below thresholds
5. **Audit trail and reporting** -- Traceable evaluation history for compliance and stakeholder communication

### Implementation Roadmap

#### Phase 1: MVP (Weeks 1-4)

**Objectives:**
- Design probe task taxonomy (6-8 capability categories)
- Build task generator API with 3-5 parameterizable difficulty levels
- Create baseline benchmark dataset for GPT-4, Claude 3.5, and Llama 2
- Integrate eval harness into Crimson Leaf's internal agent deployment pipeline

**Deliverables:**
- Probe taxonomy documentation
- Task generator API (internal use)
- Baseline benchmark report (3 major models)
- Deployment gating integration

**Resources:** 1 senior engineer (40 hrs), 1 LLM specialist (20 hrs), 1 product manager (10 hrs)

#### Phase 2: Scalability (Weeks 5-12)

**Objectives:**
- Expand probe library to 300+ standardized tasks across 8 capability domains
- Develop evaluation dashboard with filtering and comparison views
- Begin external pilot with 2-3 early-adopter publishing partners
- Document methodology for reproducibility and external validation

**Deliverables:**
- Expanded probe library (300+ tasks)
- Public beta evaluation dashboard
- Early-customer pilot program
- Methodology whitepaper

**Resources:** 2 engineers (60 hrs combined), 1 LLM specialist (30 hrs), 1 product/GTM lead (40 hrs)

#### Phase 3: Commercialization (Months 4-6)

**Objectives:**
- Launch public benchmark service (SaaS)
- Establish pricing and licensing model
- Recruit 10-20 paying beta customers
- Build customer support and onboarding processes

**Deliverables:**
- Public SaaS platform
- Pricing model and customer agreements
- Customer onboarding documentation
- Support playbooks

**Resources:** Full product team (5-6 people), marketing/sales support

### Probe Design: Example Capability Domains

1. **Factual Accuracy** -- Verify claims against known facts; assess hallucination rates
2. **Coherence & Clarity** -- Evaluate writing quality, logical flow, and comprehensibility
3. **Domain Relevance** -- Assess subject-matter correctness for specific content verticals (finance, health, tech)
4. **Style Adherence** -- Verify compliance with brand voice and tone guidelines
5. **Reasoning & Analysis** -- Evaluate multi-step reasoning, inference, and synthesis
6. **Content Safety** -- Check for harmful, biased, or inappropriate content
7. **Code Generation** -- If applicable, assess correctness and efficiency of generated code
8. **Structured Output** -- Validate JSON/XML formatting and schema compliance

---

## 5. STRATEGIC FIT

### Alignment with Crimson Leaf Mission

Foreman Probe advances Crimson Leaf's core mission of **profitable, reliable AI publishing** by:

1. **Reduces Publishing Risk**
   - Validates agent outputs before they reach readers
   - Prevents publication of low-quality or factually incorrect content
   - Protects brand reputation and reader trust
   - Creates audit trail demonstrating due diligence in AI deployment

2. **Enables Profitable Agent Deployment**
   - Data-driven model selection (GPT-4 vs. Claude vs. cheaper alternatives) based on capability benchmarks
   - Identifies which models deliver sufficient quality at lowest cost
   - Reduces iteration cycles from weeks to days
   - Justifies API spend through documented performance improvements

3. **Creates Defensible IP & Competitive Advantage**
   - Proprietary probe library becomes product differentiator
   - Publishing-domain evaluation data is not publicly available elsewhere
   - Benchmark insights inform Crimson Leaf's own model selection and fine-tuning decisions
   - Can be leveraged as premium feature for publishing partners

4. **Establishes Revenue Stream**
   - Benchmark-as-a-service offering (subscription for external publishers)
   - Custom evaluation consulting for enterprise clients
   - Potential licensing of probe tasks to publishing platforms
   - Creates recurring revenue independent of publishing volumes

5. **Accelerates Agent Optimization Loop**
   - Continuous measurement of agent capability drives iterative improvement
   - Enables A/B testing of prompt changes, model updates, and fine-tuning
   - Data-driven feedback loop replaces guesswork
   - Compounds competitive advantage over time

### Strategic Dependencies

**For success, Foreman Probe requires:**
- Internal LLM expertise (probe design, evaluation methodology)
- Engineering capacity for platform development
- Publishing domain knowledge (understanding of quality signals for content)
- Customer discovery and market validation
- Potential partnerships with LLM providers for discounted API access

---

## 6. COST MODEL AND FINANCIAL PROJECTIONS

### Setup Costs (One-time)

| Component | Estimate | Notes |
|-----------|----------|-------|
| Probe taxonomy design & documentation | 15 hrs @ $150/hr | $2,250 |
| Task generator API development | 40 hrs @ $150/hr | $6,000 |
| Baseline benchmark creation (3 models) | 20 hrs @ $150/hr | $3,000 |
| Deployment integration & testing | 12 hrs @ $150/hr | $1,800 |
| Documentation & runbooks | 8 hrs @ $100/hr | $800 |
| **Total Setup** | -- | **$14,000-$15,000** |

### Recurring Operational Costs (Monthly)

#### Task Volume Assumptions
- **Baseline scenario:** 25 probe runs per week (100/month)
- **Model distribution:** 40% GPT-4, 35% Claude 3.5 Sonnet, 25% Llama 2
- **Average task size:** 2,000 input tokens, 1,500 output tokens

#### Per-Task Cost Breakdown (Baseline)

| Model | Input Cost | Output Cost | Per-Task | Monthly |
|-------|-----------|-----------|---------|---------|
| GPT-4 | ~$0.003 | ~$0.015 | ~$0.018 | $0.72 |
| Claude 3.5 | ~$0.0015 | ~$0.0075 | ~$0.009 | $0.36 |
| Llama 2 | ~$0 (self-hosted or free) | ~$0 | ~$0 | $0 |
| **Weighted avg.** | -- | -- | **~$0.0105** | **~$0.42** |

**Monthly API Costs (100 tasks/month @ $0.0105/task):** ~$1.05

#### Infrastructure & Support Costs

| Category | Monthly Cost | Notes |
|----------|-------------|-------|
| Dashboard/platform hosting | $50-100 | AWS, Vercel, or equivalent |
| LLM API account management | $20-50 | Rate negotiation, billing, access keys |
| Data storage & backups | $10-20 | Probe library, results, metadata |
| Monitoring & logging | $10-20 | Error tracking, usage analytics |
| **Subtotal** | **$90-190** | -- |

**Total Monthly Operational Cost (Baseline):** ~$91-191 (conservative: ~$150/month)

#### Scaling Scenarios

| Scenario | Tasks/Month | API Cost | Infrastructure | Total/Month |
|----------|-----------|---------|---------------|-----------|
| **Conservative** (25/week) | 100 | $1 | $150 | **$151** |
| **Moderate** (50/week) | 200 | $2 | $200 | **$202** |
| **Growth** (100/week) | 400 | $4 | $300 | **$304** |
| **Enterprise** (200/week) | 800 | $8 | $500 | **$508** |

*Note: Costs scale sub-linearly due to volume discounts on API pricing.*

### Revenue Model & Financial Projections

#### SaaS Pricing Strategy (Benchmark-as-a-Service)

**Tier 1: Starter** -- $499/month
- Access to 150+ core probe library
- Up to 50 evaluations/month across all models
- Basic dashboard and reporting
- Target: Individual consultants, small publishers

**Tier 2: Professional** -- $1,999/month
- Full probe library (300+)
- 500 evaluations/month
- Advanced filtering, custom dashboards, API access
- Priority support
- Target: Mid-size publishing companies, agencies

**Tier 3: Enterprise** -- $5,999/month (custom)
- Unlimited evaluations
- Custom probe design and domain-specific benchmarks
- Dedicated support, SLA guarantee
- On-premise or white-label options
- Target: Large publishers, media platforms

#### Unit Economics (Year 1 Projection)

| Metric | Conservative | Moderate | Optimistic |
|--------|--------------|----------|-----------|
| **Paid customers (end of Y1)** | 3 | 8 | 15 |
| **Avg. tier mix** | Starter (60%), Pro (30%), Ent (10%) | Pro (50%), Ent (30%) | Pro (40%), Ent (50%) |
| **Blended ARPU** | $900 | $2,500 | $4,000 |
| **Monthly recurring revenue** | $2,700 | $20,000 | $60,000 |
| **Annual revenue** | $32,400 | $240,000 | $720,000 |
| **Customer acquisition cost** | $1,500 | $1,000 | $800 |
| **Payback period (months)** | 20 | 5 | 2 |

#### 3-Year Projection

**Assumptions:**
- Customer acquisition ramps from 1-2 per month (Y1) to 5-8 per month (Y2-3)
- Churn rate: 5% per month (customers tend to sticky; benchmarking platform is sticky)
- Annual price increases: 15% as product matures

| Year | Customers | MRR | Annual Revenue | Gross Margin |
|------|-----------|-----|-----------------|--------------|
| **Y1** | 8-15 | $15K-$40K | $180K-$480K | 70-75% |
| **Y2** | 40-60 | $100K-$150K | $1.2M-$1.8M | 75-80% |
| **Y3** | 100-150 | $300K-$500K | $3.6M-$6M | 78-82% |

### Cost-Benefit Analysis: ROI for Crimson Leaf

#### Internal Benefits (Cost Avoidance)

1. **Prevented Publishing Failures** -- Each prevented low-quality publication costs ~$5K-$25K (reputation damage, reader churn, correction cycles)
   - Historical rate: 1-2 incidents per quarter
   - Foreman Probe reduces risk by ~60%
   - **Annual benefit: $15K-$60K**

2. **Operational Efficiency** -- Automation of manual eval reduces engineering labor
   - Current manual testing: 12 hrs/week @ $150/hr = $7,200/month
   - Automation savings: ~70% = $5,040/month
   - **Annual benefit: $60,480**

3. **Model Optimization** -- Data-driven model selection saves 20-30% on LLM API costs
   - Current LLM spend: ~$40K/month
   - Savings from optimization: ~$8K-$12K/month
   - **Annual benefit: $96K-$144K**

4. **Time-to-Market Improvement** -- Faster iteration enables competitive advantage
   - Difficult to quantify but significant (strategic value)

**Total Annual Internal Benefit: $171K-$271K**
**Setup Cost: $14K-$15K**
**Monthly Operational Cost: $150/month ($1.8K/year)**
**Year 1 Net Benefit: $154K-$255K**
**ROI: 1,033-1,700% (Year 1)**

#### External Revenue Potential

- Conservative Year 1 revenue: $180K-$480K
- Gross margin: 70-75%
- Gross profit: $126K-$360K

**Combined Year 1 Value: $280K-$615K**

---

## 7. RISK ANALYSIS

### Key Risks

| Risk | Probability | Impact | Mitigation |
|------|-------------|--------|-----------|
| **Incomplete probe design** | Medium | Product fails to detect real capability gaps; users lose confidence | Run alpha testing with 3 internal users before public launch; iterate on probe categories |
| **Competitive entry** | Medium-High | OpenAI, Anthropic, or other vendors launch similar offering | Move quickly (Q1 2024 launch); establish customer relationships early; build proprietary domain data |
| **Low customer adoption** | Medium | Market not ready to pay for benchmarking; prefer free alternatives | Validate demand with 3-5 customer conversations before full build-out; consider freemium tier |
| **API cost inflation** | Low-Medium | LLM pricing increases; unit economics worsen | Negotiate volume discounts with model providers; diversify to open-source models |
| **Scope creep** | High | Project expands beyond original scope; delays launch | Define MVP strictly; use two-week sprint cycles; gate feature additions |
| **Talent/retention** | Low | Key engineer leaves; project loses momentum | Cross-train team; document architecture and decisions; maintain engagement through clear roadmap |
| **Technical debt** | Medium | Early MVP accumulates technical debt; slows future iteration | Allocate 20% of engineering time to refactoring; use modular architecture from day one |
| **Regulatory changes** | Low-Medium | New AI governance rules affect evaluation methodologies | Monitor EU AI Act, SEC rules, and industry standards; build flexibility into evaluation framework |

### Risk Mitigation Strategy

1. **Pre-Launch Validation (Weeks 1-2)**
   - Conduct 5 customer discovery interviews: "Would you subscribe to LLM benchmarks?"
   - Internal dogfooding: Crimson Leaf team uses MVP for 2 weeks
   - Refine probe design based on feedback

2. **MVP Discipline**
   - Strict scope: 3 capability domains, 2 models, internal-only
   - Two-week sprint cycles with clear deliverables
   - Weekly stakeholder check-ins to prevent scope creep

3. **Competitive Monitoring**
   - Weekly scans for competitor launches
   - Quarterly strategic review of market positioning
   - Fast iteration on differentiation (publishing domain focus)

4. **Customer Lock-in**
   - Build API integrations with customer platforms early
   - Create data exports and benchmarking reports that become part of customer workflows
   - Establish annual contracts with 3+ month notice for cancellation

5. **Financial Controls**
   - Monthly budget tracking and variance analysis
   - Milestone-based funding: Each phase requires explicit go/no-go decision
   - Quarterly ROI check-in against projections

---

## 8. ALTERNATIVES CONSIDERED

### Alternative A: Outsource Evaluation to Third-Party Vendor

**Approach:** Use existing platforms (e.g., Giskard, W&B) instead of building

**Pros:**
- Faster to market (weeks vs. months)
- No build/maintenance burden
- Access to vendor's infrastructure and scaling

**Cons:**
- Lack of control over probe design (not publishing-specific)
- Inability to differentiate
- No IP created; no revenue stream
- Vendor lock-in and pricing increases out of our control
- Doesn't solve the "custom benchmarking" need

**Why rejected:** Foreman Probe's value lies in proprietary publishing-domain benchmarks and tight integration with Crimson Leaf's publishing workflows. Third-party tools are generic and commodity.

### Alternative B: One-Time Consulting Report

**Approach:** Commission external firm to conduct custom LLM evaluation report; don't build platform

**Pros:**
- Low immediate investment
- Quick turnaround for one-time need
- Outsources expertise

**Cons:**
- No repeatable asset or scaling
- No revenue opportunity
- Competitive advantage is temporary (report becomes stale in 2-3 months)
- Doesn't solve ongoing validation needs

**Why rejected:** Misses the strategic opportunity. Publishing is continuous; evaluation needs recur monthly/quarterly. One-time consulting doesn't drive long-term value.

### Alternative C: Embed Evaluation into Existing Foreman Template

**Approach:** Add evaluation features to existing Foreman agent template library instead of creating new company

**Pros:**
- Lower complexity
- Leverages existing product distribution
- Faster deployment

**Cons:**
- Dilutes Foreman's positioning (agent execution, not benchmarking)
- Cannot create separate revenue stream
- Doesn't establish Foreman Probe as distinct brand
- Benchmarking requires different go-to-market (customer base differs from task creators)

**Why considered:** Initial instinct to leverage existing platform

**Why rejected:** Benchmarking is a distinct product with different customers, pricing, and business model. Embedding it waters down both Foreman and Probe positioning.

### Alternative D: Acquire Existing Benchmark Provider

**Approach:** Buy a smaller benchmarking company instead of building

**Pros:**
- Instant product and customer base
- Reduced execution risk
- Acquires talent and IP

**Cons:**
- Capital outlay ($5M-$20M+ likely)
- Integration risk and cultural mismatch
- Likely not publishing-focused (would need significant retooling)
- Slower than build-from-scratch (deal cycle + integration)

**Why rejected:** Not economically justified at Crimson Leaf's current scale. Build-from-scratch is faster and lower-capital for MVP validation.

---

## 9. PROPOSED ORGANIZATIONAL STRUCTURE

### Governance

**Owner & P&L Responsibility:** Head of AI Products (to be assigned; recommend promoting senior IC or external hire)

**Reporting Line:** To Chief Technology Officer or Chief Product Officer

**Board Oversight:** Quarterly review with CEO and CFO; explicit approval required for Phase 2 and Phase 3

### Proposed Team

**Phase 1 (MVP, Weeks 1-4):**
- 1 Senior AI/ML Engineer (40 hrs/week) -- probe design, API development, testing
- 1 LLM Specialist (20 hrs/week) -- benchmark design, model evaluation methodology
- 1 Product Manager (10 hrs/week) -- scoping, prioritization, stakeholder management
- **Total: 70 hours/week, estimated cost: $14K/month**

**Phase 2 (Scale-out, Weeks 5-12):**
- 2 Engineers (80 hrs/week combined) -- dashboard, library expansion, integrations
- 1 LLM Specialist (30 hrs/week) -- probe quality assurance, methodology documentation
- 1 Product/GTM Lead (40 hrs/week) -- customer discovery, pilot program, go-to-market strategy
- **Total: 150 hours/week, estimated cost: $30K/month**

**Phase 3 (Commercialization, Months 4-6):**
- Add sales/customer success lead (40 hrs/week)
- Add product marketing lead (20 hrs/week)
- Expand engineering as needed
- **Total team: 5-7 people; estimated cost: $60K-$80K/month**

### Decision Rights & Escalation

- **Probe design/methodology:** LLM Specialist + Product Manager (weekly sync)
- **Engineering priorities:** Senior Engineer + Product Manager (bi-weekly planning)
- **Customer commitments:** Product Manager + Head of AI Products
- **Budget overruns >10%:** Require CEO approval
- **Phase transitions (MVP  Scale  Commercialization):** Require CEO + CFO approval

---

## 10. SUCCESS CRITERIA & KPIs

### Phase 1 Success (Weeks 1-4)

**Go/No-Go Metrics:**
- [ ] Probe taxonomy documented and internally validated (3/5 team members agree categories are comprehensive)
- [ ] Task generator API functional and tested (generates valid probes across all categories)
- [ ] Baseline benchmark completed for GPT-4, Claude, Llama (all 3 models evaluated on 50 tasks)
- [ ] Deployment gating integrated into 1 internal Crimson Leaf publishing workflow
- [ ] No critical bugs; system stability >95%

**Qualitative validation:**
- Internal stakeholder feedback: "Probes catch real capability gaps we care about"
- LLM specialist assessment: "Benchmark design is sound and reproducible"

### Phase 2 Success (Weeks 5-12)

**Quantitative Metrics:**
- [ ] Probe library expanded to 250+ tasks (target: 300+)
- [ ] Dashboard completed with filtering, comparison, and export functionality
- [ ] 3+ early-access customers enrolled in pilot program
- [ ] Methodology whitepaper completed and reviewed by external expert
- [ ] 0 critical production incidents; 95%+ uptime

**Qualitative Validation:**
- Early-customer feedback: "Probes are relevant to our use cases; dashboard is usable"
- Market validation: 2-3 customers express interest in paying for full product
- Internal NPS: Recommend to peers (Crimson Leaf team usage survey)

### Phase 3 Success (Months 4-6)

**Go/No-Go Metrics for Commercialization:**
- [ ] SaaS platform launched and customer-ready
- [ ] Pricing model defined and validated with 5+ prospective customers
- [ ] 10+ customers in beta program; 3 paying customers
- [ ] Product documentation complete (API docs, user guide, support playbook)
- [ ] Customer acquisition cost (CAC) <$1,500

**Revenue & Efficiency Metrics:**
- [ ] Monthly recurring revenue (MRR): $5K-$15K by end of month 6
- [ ] Churn rate: <5% per month
- [ ] Gross margin: >70%
- [ ] Customer satisfaction (NPS): >50

### Long-Term Success Metrics (Year 1)

- **Adoption:** 8-15 paying customers by end of Year 1
- **Revenue:** $180K-$480K annual recurring revenue
- **Product:** Probe library expanded to 500+ tasks; support for 5+ LLM models
- **Competitive Position:** Recognized as leading publishing-domain LLM benchmark (industry awareness)
- **Internal ROI:** >1,000% (cost savings + revenue exceeds investment)

---

## 11. DEPENDENCIES & PREREQUISITES

### Technical Dependencies

- Access to Claude, GPT-4, and Llama model APIs
- Existing Crimson Leaf task execution infrastructure (Foreman platform)
- Data storage and analytics platform (existing infrastructure assumed available)
- Deployment tooling and CI/CD integration

### Organizational Dependencies

- Product management bandwidth to own go-to-market and customer discovery
- LLM expertise within Crimson Leaf team (or hiring budget to acquire)
- CEO/CFO commitment to milestone-based funding and go/no-go decisions
- Engineering capacity (cannot proceed if engineering is at >90% utilization)

### Market Dependencies

- Validation that customers will pay for publishing-domain benchmarking (customer discovery pre-flight)
- LLM API pricing remains stable (risk: inflation could worsen unit economics)
- Continued publishing demand (Crimson Leaf's core business remains strong)

### Milestone Dependencies

**Foreman Probe can only proceed to Phase 2 if Phase 1 delivers:**
1. Validated probe taxonomy (internal + external expert review)
2. Functional task generator and baseline benchmark
3. Deployment integration working without critical issues
4. Clear customer demand signal from 2-3 discovery conversations

**Phase 2  Phase 3 gates:**
1. Early-access customer(s) reporting positive impact (qualitative)
2. >2 customers willing to discuss paid pilot
3. Unit economics validated (API costs, infrastructure costs align with projections)
4. Team capacity to support commercialization phase

---

## 12. TIMELINE & MILESTONES

### Month 1: MVP Build-Out

**Week 1:**
- Probe taxonomy finalized
- Task generator architecture designed
- API specification documented

**Week 2:**
- Task generator API skeleton implemented
- Sample probes for 2 capability categories created
- Begin evaluation framework design

**Week 3:**
- Baseline benchmark runs initiated (GPT-4, Claude, Llama)
- Deployment gating integration begins
- Initial documentation drafted

**Week 4:**
- Baseline benchmark completed
- Internal dogfooding and feedback collection
- Phase 1 go/no-go decision (CEO + CFO approval required)

### Months 2-3: Scale-Out & Validation

**Week 5-6:**
- Expand probe library (250+ tasks)
- Dashboard UI/UX design completed
- Early-access customer recruitment

**Week 7-8:**
- Dashboard MVP launched (internal)
- Early-access pilots begin (2-3 customers)
- Methodology documentation continues

**Week 9-10:**
- Dashboard refinements based on feedback
- Probe library quality assurance
- Competitive landscape analysis

**Week 11-12:**
- Phase 2 deliverables finalized
- Pilot customer feedback collected
- Commercialization strategy reviewed
- Phase 2  Phase 3 go/no-go decision

### Months 4-6: Commercialization

**Week 13-16:**
- SaaS platform hardening (security, compliance, scalability)
- Pricing model finalized
- Customer onboarding playbook documented

**Week 17-20:**
- Public beta launch
- Beta customer cohort on-boarded
- Sales/customer success processes built

**Week 21-24:**
- General availability launch
- Marketing materials prepared
- Targets: 5-10 paying customers, $5K-$15K MRR

---

## 13. FINANCIAL SUMMARY

### Investment Required

| Phase | Timeline | Investment | Notes |
|-------|----------|-----------|-------|
| **Phase 1 (MVP)** | Weeks 1-4 | $14K-$15K (one-time setup) + $0.5K (ops) | Minimal external spend |
| **Phase 2 (Scale)** | Weeks 5-12 | $30K/month  8 weeks = $240K | Primarily personnel |
| **Phase 3 (GTM)** | Months 4-6 | $70K/month  3 months = $210K | Personnel + marketing |
| **Total Year 1** | -- | **$465K-$470K** | Fully loaded cost |

### Projected Returns

| Metric | Conservative | Optimistic |
|--------|--------------|-----------|
| **Internal benefit (cost savings + risk reduction)** | $170K | $270K |
| **External revenue (SaaS)** | $180K | $480K |
| **Total Year 1 benefit** | **$350K** | **$750K** |
| **Year 1 net** (benefit - investment) | **-$115K** | **+$280K** |
| **Payback period** | 14-16 months | 6-9 months |

**Note:** Year 1 is investment-heavy due to build-out and market development. Profitability is achieved in Year 2 as customer base scales and operational costs become fixed.

### Go/No-Go Decision Framework

**PROCEED (Green Light) if:**
- CEO and CFO approve initial $14K-$15K Phase 1 investment
- Customer discovery (5 interviews) shows 2 companies expressing willingness to pay
- Engineering capacity is available (no critical project delays)
- Internal LLM expertise is available or budget exists to hire

**CONDITIONAL PROCEED (Yellow Light) if:**
- Phase 1 customer interviews show moderate interest (1 out of 5 willing to pay)
- Proceed with Phase 1 MVP only; pause Phase 2 until customer validation is stronger
- Use Phase 1 output to refine positioning and target customer profile

**DO NOT PROCEED (Red Light) if:**
- Customer discovery shows zero willingness to pay or perceived value
- Engineering team is >90% utilized (cannot spare capacity)
- CEO/CFO signal low confidence in LLM evaluation market
- Competing product launches with significant funding during Phase 1

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## APPENDIX A: Governance Certification

**Edgar Chen, CEO, Crimson Leaf Holdings, certifies:**

- [ ] No existing Crimson Leaf subsidiary or division duplicates the charter of Foreman Probe
- [ ] No existing Foreman template or tool can fulfill this business need
- [ ] No proposal for a company bearing this or a similar name has been submitted within the last 30 days
- [ ] This proposal includes a complete business plan with research synthesis, financial projections, and risk analysis
- [ ] All sections of this document (Executive Summary through Financial Summary) are complete and ready for decision

**This proposal requires explicit approval from David Baity before any action is taken.**

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**Status:** AWAITING DAVID'S APPROVAL  
**Submitted:** [Current Date]  
**Contact:** Edgar Chen, CEO  
**Next Review:** Upon receipt of Phase 1 go/no-go decision