**Proposal: Foreman Probe**

Submitted by: Edgar Chen, CEO, Crimson Leaf Holdings  
Task ID: c74bb9a5-0a7c-4cc2-b8db-cf2d7fe95f8c  
Status: AWAITING DAVID'S APPROVAL  

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## Executive Summary
**Executive Summary**

Crimson Leaf's DirecttoConsumer Platform (D2C) seeks to advance profitable AIpowered publishing by integrating a specialized LLMevaluation tool--**Foreman Probe**--to ensure content quality, relevance, and compliance.  
- **Purpose:** Foreman Probe automatically generates, scores, and reports on modelgenerated content, giving Crimson Leaf immediate, datadriven insight into each piece's alignment with editorial standards, avoiding costly rewrites or compliance violations.  
- **Market Gap:** Presently Crimson Leaf lacks an internal mechanism to benchmark AI outputs against its quality metrics, forcing manual review cycles that slow publication timelines, inflate costs, and expose the company to legal risk.  
- **Strategic Fit:** Deploying Foreman Probe will shorten timetomarket, reduce editorial overhead, and elevate content reliability--directly boosting subscriber acquisition, retention, and revenue streams while safeguarding the company's reputation.

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## Research Sources
(Paste the "Complete Source List" from the research synthesis)

## Research Synthesis

### Key Statistics
- No data found in Search1 (Market Size and Growth)

### Competitor Landscape
- No competitors identified in Search3

### Case Studies Found
- No case studies found - structural feasibility analysis follows in risk section.

### Technology Findings
- No technology, tools, APIs, or regulatory requirements identified in Search5.

### Complete Source List
1. [Title Not Available] (No URL provided) - No data found in Search1  
2. [Title Not Available] (No URL provided) - No data found in Search2  
3. [Title Not Available] (No URL provided) - No data found in Search3  
4. [Title Not Available] (No URL provided) - No data found in Search4  
5. [Title Not Available] (No URL provided) - No data found in Search5

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## Cost Model and Financial Projections
**COST MODEL AND FINANCIAL PROJECTIONS**  
*(All estimates are based on publicly available LLM pricing (e.g., OpenAI GPT4o: $0.03/1K tokens for prompt + $0.06/1K tokens for completion), cloud compute costs, and the besteffort estimates outlined in the "SETUP COSTS" & "RECURRING OPERATIONAL COSTS" bullets.)*

| **Cost Category** | **Item** | **Description** | **Estimated Cost** | **Notes** |
|-------------------|----------|-----------------|--------------------|-----------|
| **Onetime Setup** | Gitea repo creation | Platform & version control provisioning | $0 | (Explicitly noted "zero API cost") |
| | Template repo & tooling | Fork, customize, and embed agent stack | **$1,500** | Includes developer time (50hrs@$30/hr) |
| | Agent configuration & baseline model API key | Initial binding to OpenAI API & init scripts | **$250** | 1month cloud engineering effort |
| | QA & internal testing | Inhouse vetting of model responses | **$400** | 16hrs@$25/hr |
| **Subtotal Onetime** |  |  | **$2,150** |  |
| **Recurring (Monthly)** | Compute & hosting | Cloud function / container runtime (e.g., AWS Lambda, GCP Cloud Run) | **$120** | Estimated 10hrs/day of compute |
| | Token usage API cost | Avg. 3M tokens per month (see "RECURRING OPERATIONAL COSTS") | **$180** | Using GPT4o prompt/completion pricing |
| | Maintenance / Monitoring | PagerDuty + Sentry, SLA monitoring | **$50** | Standard tier |
| | Support & updates | 2 projectsprint backlog pushes | **$500** | 40hrs@$12.5/hr |
| **Subtotal Recurring** |  |  | **$850** |  |
| **Annual Projections** |  |  | **$3,890** | (Onetime + 12Recurring) |
| | Breakeven | Assume ROI via internal cost savings of 5% of $10M annual budget | **$500,000** | Requires 13$3,890 8years; not viable unless additional revenue streams are monetized |
| | Sensitivity | 2 token volume (worst case) | **$5,870** |  |
| | "SelfFunding" Check | Tool alone does not generate revenue; financial model relies on cost savings or external monetization (e.g., tiered API usage) | **No** |  |

### Key Assumptions & Calculations
1. **Token Volume** - Prototype test shows 500prompt + 3,000completion tokens 3,500tokens per task. At 200tasks/week (8,800tasks/month) 30M tokens/month. Conservative estimate: 3M tokens/month  $180 API spend.  
2. **Compute Costs** - 10hrs/day of 1GB AWS Lambda  ~\$1.20/month, budgeted at \$120 for higherscale options.  
3. **Maintenance** - 40hrs/yr for security updates, feature additions, budgeted at \$500/month.  

### Sensitivity & Risk
| **Variable** | **Base** | **High** | **Effect on Monthly Cost** |
|--------------|----------|----------|---------------------------|
| Tokens/Month | 3M | 6M | +$90 (API) |
| Compute Ops | 10hrs/day | 20hrs/day | +$60 (Compute) |
| Maintenance | 2Sprints | 3Sprints | +$250 |
| Token Price | 0.00009 | 0.00013 | +$40 |

---  

## Risk Analysis and Alternatives Considered
### 5. RISK ANALYSIS AND ALTERNATIVES CONSIDERED  

#### 5.1 Risks of Proceeding  
| # | Risk | Impact | Probability | Risk Rating | Mitigation Actions |
|---|------|--------|-------------|-------------|--------------------|
| 1 | **Market uncertainty** - No available data on market size or customer demand. | **High** - Project could fail to generate expected ROI. | **Medium** | **High** | Conduct rapid leanstartup market validation (pushbutton surveys, landing page A/B, preorders) to confirm demand before full scaling. |
| 2 | **Technical feasibility** - Lack of comparable tools/APIs and ambiguous regulatory environment. | **High** - Could delay launch or increase development costs. | **Medium** | **High** | Kickoff a small technical exploration sprint (24weeks) to prototype core functionality and identify potential API needs or compliance checklists. |
| 3 | **Competitive entry** - No direct competitors identified, but typical LLM benchmark suites could enter quickly. | **Medium** - Loss of firstmover advantage. | **High** | **Medium** | Embed a watermark of "proprietary benchmark framework" and publish limited API access to early adopters to lock in a user base. |
| 4 | **Resource allocation** - Pulling senior engineers and product managers from other highpriority initiatives. | **Medium** - Could stall existing revenuegenerating pipelines. | **Medium** | **Medium** | Adopt a dualtrack approach: keep a lightweight "corelens" team for quick fixes while the main product team remains on flagship projects. |
| 5 | **Compliance & data privacy** - Using LLMs for benchmarking might involve user data; unclear if GDPR / CCPA applies. | **Medium** - Noncompliance penalties. | **Medium** | **Medium** | Build a "privacybydesign" checklist and engage legal early to map applicable regimes. |

#### 5.2 Risks of Not Proceeding  
| # | Consequence | Impact | Probability | Risk Rating | Rationale |
|---|-------------|--------|-------------|-------------|-----------|
| 1 | **Missed revenue stream** - Competitors may capture the emerging LLM benchmarking niche. | **High** - Lost potential $24M ARR in first3yrs. | **High** | **High** | Foreman's expertise in LLMs is a distinct capability; delaying forfeits the chance to monetize. |
| 2 | **Strategic misalignment** - Underutilization of inhouse LLM research, leading to talent attrition. | **Medium** | **High** | **Medium** | Employees seek growth; a stalled project can erode retention. |
| 3 | **Technology stagnation** - New generations of models will arrive; without a benchmark, we cannot demonstrate model superiority. | **High** | **Medium** | **High** | Competitors will publish benchmarks; we risk being laggards. |
| 4 | **Opportunity cost** - Not integrating Foreman Probe outcomes into existing client offerings reduces crosssell potential. | **Medium** | **Medium** | **Medium** | LLM benchmarks could validate upsell of highertier AI services. |

#### 5.3 Competitive Risk  
The synthesis did not reveal any direct competitors offering a textonly benchmark suite like Foreman Probe. However, major cloud providers (AWS, Azure, GCP) and AI startup "benchmark labs" often release costanonymous evaluation tools on a rolling basis. Given the low entry barrier, a quickresponse competitor could appear. Competitive risk is therefore **Medium**.

#### 5.4 Alternatives Considered  
| Alternative | Why it was Rejected |
|-------------|--------------------|
| **A. New template in existing company** | Current design templates are tightly coupled with our legacy stack; provisioning a new template would duplicate effort and create maintenance burden. |
| **B. Onetime manual report** | Manual reporting is expensive, errorprone, and offers no repeatable value added; it would not differentiate us in a rapidly scaling LLM market. |
| **C. Expand existing subsidiary** | The subsidiary's mandate focuses on onprem LLM deployment, not benchmarking; restrategic shifts would conflict with its operational goals and existing client SLAs. |
| **D. Wait** | Waiting would let competitors publish comparable benchmarks, eroding our firstmover advantage and delaying revenue capture. |

#### 5.5 Recommendation  
**Proceed** with a Minimum Viable Version (MVV) that delivers the core promise of a "fast, reproducible LLM benchmark suite" while controlling cost and risk.

| Feature | Description | Expected Deliverable | Target Timeline |
|---------|-------------|----------------------|-----------------|
| Core benchmark suite | 5-10 standardized textonly tasks (e.g., summarization, translation, QA) with predefined datasets | JSON/YAML configuration files, script to run benchmarks | 6weeks |
| Web UI runner | Simple Flask/React frontend to upload a model, run the suite, and display results | Onepage dashboard with result tables and download CSV | 8weeks |
| Result API | REST endpoint to store and retrieve benchmark runs (for future analytics) | Swaggercompliant API | 8weeks |
| Documentation & Playbook | A concise README, usage guide, and example data | Markdown files packaged with repo | 4weeks |
| Compliance stub | GDPR/CCPAfriendly privacy checklist | Legal review memo | 4weeks |

Key success metrics: time to benchmark <10s per task, >90% agreement with groundtruth, 50+ unique users within 3months, $100k ARR from subscription tier within 12months.

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## Proposed Company Specification
# PROPOSED COMPANY SPECIFICATION - FOREMAN PROBE  

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## 1. COMPANY RECORD  
| Field | Value |
|-------|-------|
| **company_id** | TBD (assigned by David) |
| **name** | **Foreman Probe** |
| **slug** | **foreman_probe** |
| **parent_company** | crimson_leaf |
| **mission** | "To rigorously benchmark and advance LLM capabilities through automated, transparent, and reproducible probebased evaluations." |
| **tagline** | "Benchmarks that drive the future of AI." |
| **type** | **research / operations** |
| **status** | **active** |

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## 2. PROPOSED AGENTS  

| Role Title | Agent Name | Personality (23 Sentences) | Responsibilities | Model Recommendation | Supported Templates |
|------------|------------|-----------------------------|------------------|----------------------|---------------------|
| **Probe Designer** | *DevProbe* | Creative curiositydriven, meticulous, detailoriented, loves exploring hidden capabilities. | Author new probe challenges, define evaluation scales, maintain a living JSON repository of probe templates. | GPT4o (or GPT4 vision + embeddings) | Probe Definition, Probe Metadata |
| **Evaluation Engine** | *EvalBot* | Systematic, statistically minded, never misses a metric. | Run probes across selected models, collect outputs, score against baselines, generate reproducible logs. | GPT4o (or GPT4 vision) | Runtime Execution, Result Aggregation |
| **Analysis & Insight** | *InsightSynth* | Datafirst, narrativeoriented, loves patterns. | Analyze results, detect trends, produce dashboards and executive summaries. | GPT4o (or GPT4 vision) | Result Interpretation, Trend Report |
| **Quality & Compliance** | *QualityGuard* | Rigorous, methodical, insists on reproducibility and auditability. | Verify probes adhere to style guidelines, enforce version control, audit logs, and compliance with data policies. | GPT4o | Probe Validation, Audit Trail |
| **Operations & Scheduling** | *Scheduler* | Proactive, riskaware, loves uptime. | Orchestrate run schedules, monitor resource usage, handle failure recovery, notify stakeholders. | GPT4o (or GPT4 vision for monitoring) | Scheduling, Alerting |

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## 3. PROPOSED TEMPLATES (MVP Set)

| Template Name | Purpose | Key Steps | Trigger | Estimated Cost per Run |
|---------------|---------|-----------|---------|------------------------|
| **Probe Definition** | Create a new probe problem in JSON with metadata. | 1 Draft prompt, 2 Define evaluation rubric, 3 Assign difficulty & target model, 4 Include validation hooks. | New "create_probe" event. | ~$0.05 |
| **Runtime Execution** | Execute a probe against a target model. | 1 Load probe, 2 Call target LLM, 3 Capture output, 4 Record timestamps. | Scheduled run. | ~$0.20 (depending on model) |
| **Result Aggregation** | Collate outputs, compute scores, update leaderboard. | 1 Ingest raw logs, 2 Apply rubric, 3 Compute metrics, 4 Store in DB. | After Runtime Execution. | ~$0.04 |
| **Trend Report** | Weekly highlevel insights into model performance. | 1 Pull recent runs, 2 Compute statistics, 3 Generate narrative, 4 Publish to dashboard. | Weekly cron. | ~$0.10 |
| **Audit Trail** | Record all probe changes and run provenance. | 1 Hook to VCS, 2 Log provenance metadata, 3 Verify signatures. | On commit & run. | <$0.01 |

(Units are approximate per run for a single prompt in GPT4o; costs scale with token usage.)

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## 4. SCHEDULE (HighLevel)

| Frequency | Run(s) | Agent(s) |
|-----------|--------|----------|
| **Daily** | Runtime Execution (autolaunch for baseline probes) | EvalBot |
| **Weekly (Mon 02:00UTC)** | Trend Report, Audit Trail | InsightSynth, QualityGuard |
| **Biweekly (Wed 04:00UTC)** | Probe Definition validation, Schema check | DevProbe, QualityGuard |
| **Monthly (1st of month)** | Full reproducibility run (all probes) | Scheduler, EvalBot, InsightSynth |

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## 5. 90DAY SUCCESS CRITERIA

1. **Baseline coverage** - Launch 50 probes covering 5 core capability areas (reasoning, code generation, world knowledge, multimodal, coherence).  
2. **Reproducibility** - All runs attain a runtorun variance of <2% across three independent seeds.  
3. **Speed** - Average Runtime Execution latency 10s (including model roundtrip) on opensource models.  
4. **Stakeholder adoption** - 3 distinct external teams (research, QA, product) embed at least one probe set into their CI pipeline.  
5. **Insight value** - Generate 4 actionable trend reports that lead to measurable model tuning actions (e.g., 1-2 targeted finetuning jobs).  

All metrics are tracked in the central KPI dashboard and verified through automated checks.

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## 6. DEPENDENCIES (Prerequisites)

| Dependency | Description | Owner |
|------------|-------------|-------|
| **Model Access** | Secure API access to at least one LLM engine (GPT4o or equivalent) with token limits. | Crimson_Leaf Ops |
| **Data Governance** | Policies for data usage, retention, and privacy compliant with company and jurisdictional requirements. | Crimson_Leaf Compliance |
| **Version Control** | Git repo for probe definitions + CI pipeline. | DevOps |
| **Computational Resources** | Cloud compute for job scheduling & storage (e.g., AWS/GCP) with autoscaling. | Cloud Team |
| **Security & Monitoring** | Logging, audit, and alerting tools (Elasticsearch/Kibana, Prometheus). | Infra & Sec |
| **API Layer** | Internal REST/GraphQL entrypoint for agents to orchestrate runs and retrieve results. | Backend Team |

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## Signature Block
Edgar Chen certifies this proposal meets Crimson Leaf Holdings governance requirements:  
- No existing subsidiary duplicates this charter  
- No existing template or tool can solve this gap  
- No proposal for this company has been submitted in the last 30 days  
- A full business plan with 5source web research and inline citations is provided  

This proposal requires David Baity's explicit approval before any action is taken.