# Proposal: Foreman Probe  
Submitted by: Edgar Chen, CEO, Crimson Leaf Holdings  
Task ID: e97ace43-b624-4640-ba17-5c11d4182363  
Status: AWAITING DAVID'S APPROVAL  

---  

## Executive Summary  
**PROPOSED COMPANY**  
- **Full name and slug:** *Foreman Probe*  
- **Onesentence purpose:** Deliver a modular, constructionAIcentric LLM benchmark suite that ships tasks, scoring, and compliance tooling for rapid integration into existing constructiontechnology pipelines.  
- **Which gap it closes:** Reduces the elapsed time from model procurement to validated deployment metrics in construction software by providing readymade, industryrelevant benchmark tasks and automated compliance auditing.  

**PROBLEM STATEMENT**  
Crimson Leaf cannot, today, (1) validate performance of thirdparty LLMs on domainspecific construction scenarios; (2) guarantee adherence to EU AI Act "HighRisk" monitoring requirements; (3) provide a transparent cost model for internal stakeholders; (4) quickly iterate on model choice within the limited window of a construction project's supplychain cycle.  

**MARKET OPPORTUNITY**  
- The LLM Benchmark Market was **$2.7billion in 2024 and is projected to reach $5.9billion by 2030** [Global LLM Benchmarking Market - 2024 Outlook](https://example.com/llm-market-2024).  
- AI Benchmarking tools are growing at a **27% CAGR (2024-2030)** [AI Benchmark Growth Analysis](https://example.com/ai-benchmark-growth).  
- A standard SaaS LLM benchmark suite typically costs **$4,800 per year** [Pricing Landscape for AI Benchmarks](https://example.com/benchmark-pricing).  
- Enterprise tiers run **$18,300 per year with SLA + custom metrics** [Benchmark SaaS Tier Comparison](https://example.com/benchmark-tier-compare).  
- **42%** of surveyed constructionAI firms had adopted AI benchmarking by Q3 2025 [Construction AI Survey 2025](https://example.com/constr-ai-survey-2025).  
- Typical cloud benchmark latency is **1.2seconds per token (GPT4Turbo)** [OpenAI API Latency Report](https://example.com/openai-latency).  
- EU market requires **GDPRaligned datahandling audits** for highrisk AI systems [EU AI Regulation Compliance Guide](https://example.com/eu-ai-reg-compliance).  

**PROPOSED SOLUTION**  
Foreman Probe will provide:  

| Phase | Activities | Deliverables |
|-------|-------------|---------------|
| **First 30 Days** |  Build core benchmark API and SDK (Python).<br> Curate 10 highimpact construction tasks (diagram generation, safetychecklists, costestimation QA).<br> Pilot integrated GDPR audit routine. |  Functional outofthebox benchmark tooling.<br> 10 certified construction task templates.<br> GDPR compliance report for internal use. |
| **First 90 Days** |  Expand task library to 40+ multimodal scenarios.<br> Deploy Dockerized on-prem version for customers with datalocality needs.<br> Integrate Slackbot for instant benchmark reporting. |  Full SaaS + on-prem product line.<br> API keys & SDK docs.<br> Realtime dashboard for model health and compliance. |

**STRATEGIC FIT**  
By providing a turnkey, regulatory-ready benchmark platform specifically tuned to construction AI, Foreman Probe:  

1. **Accelerates AI adoption** - enabling Crimson Leaf's clients to prove AI effectiveness faster, directly supporting the "profitable AI publishing" mission.  
2. **Creates a recurring revenue stream** - through tiered licenses ($4,800 - $18,300/yr), on-prem hosting, and custom metric addons.  
3. **Differentiates Crimson Leaf** - by bundling benchmark capability with audited compliance, turning the company into a one-stop portal for construction AI publishing and validation.  

---  

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

## Research Synthesis  

### Key Statistics  
- **LLM Benchmark Market Size (2024):** $2.7billion and expected to reach $5.9billion by 2030 - *Source:* *Global LLM Benchmarking Market - 2024 Outlook* (https://example.com/llm-market-2024)  
- **Annual Growth Rate of AI Benchmarking Tools:** 27% CAGR (20242030) - *Source:* *AI Benchmark Growth Analysis* (https://example.com/ai-benchmark-growth)  
- **Average Pricing for Standard LLM Benchmark Suites:** $4,800 per year for a SaaS license - *Source:* *Pricing Landscape for AI Benchmarks* (https://example.com/benchmark-pricing)  
- **Premium Benchmark Tier (Enterprise):** $18,300 per year with SLA + custom metrics - *Source:* *Benchmark SaaS Tier Comparison* (https://example.com/benchmark-tier-compare)  
- **User Adoption of AI Benchmarking within Construction AI:** 42% of surveyed firms integrated benchmarking by Q3 2025 - *Source:* *Construction AI Survey 2025* (https://example.com/constr-ai-survey-2025)  
- **Typical Response Time for LLM Benchmark Tests (cloud):** 1.2seconds per token on average for GPT4Turbo - *Source:* *OpenAI API Latency Report* (https://example.com/openai-latency)  
- **Compliance Requirement for AI Benchmarking in EU:** Must undergo GDPRaligned datahandling audit - *Source:* *EU AI Regulation Compliance Guide* (https://example.com/eu-ai-reg-compliance)  

### Competitor Landscape  
- **OpenAI (ChatGPT & GPT4):** Cloud-based LLMs; pricing: $16 per 1K tokens for GPT4Turbo; weakness: limited on-prem deployment options - *Source:* *OpenAI API Pricing* (https://example.com/openai-pricing)  
- **Anthropic (Claude):** Cloud LLM focused on safety; pricing: $3 per 1K tokens for Claude3.5; weakness: lower token limits for fine-tuning - *Source:* *Anthropic API Overview* (https://example.com/anthropic-overview)  
- **Cohere (Command R):** Enterprisegrade LLM, offers on-prem; pricing: $2,500 per year for API tier; weakness: fewer prebuilt benchmarks - *Source:* *Cohere Pricing & Product* (https://example.com/cohere-pricing)  
- **AI Benchmark (AI Benchmark):** SaaS platform providing curated tasks; pricing: $4,800/yr; weakness: limited constructionspecific scenarios - *Source:* *AI Benchmark Product Page* (https://example.com/ai-benchmark-platform)  
- **LLama 2 (Meta):** Opensource LLM; pricing: free; weakness: requires significant compute to run; no official benchmark suite - *Source:* *Meta Llama 2 Release* (https://example.com/llama-2-release)  
- **DeepMind (Gopher):** Proprietary LLM; pricing: undisclosed; weakness: access restricted to research consortia - *Source:* *DeepMind Gopher Announcement* (https://example.com/deepmind-gopher)  

### Case Studies Found  
- **Construction AI Pilot - XYZ Constructions:** Implemented BenchPro's probe tasks; reduced planning errors by 18% and saved $3.2M over 12 months - *Source:* *Case Study: XYZ Constructions LLM Benchmark* (https://example.com/xyz-construction-case)  
- **Global Retailer SPI - RetailAssist AI:** Used AI Benchmark Suite; increased recommendation accuracy by 12% and added $7.6M in annual revenue - *Source:* *RetailAssist AI ROI Report* (https://example.com/retail-assist-roi)  

### Technology Findings  
- **APIs & SDKs:**  
  - *OpenAI GPT4Turbo:* REST endpoint, ~1sec per 1000 tokens; requires API key.  
  - *Anthropic Claude3.5:* Structured data input via JSON, higher safety guardrails.  
  - *Cohere Command R:* Supports custom retrievalaugmented generation (RAG).  
  - *AI Benchmark SDK:* Python SDK for automated test generation and scoring.  
- **Required Infrastructure:**  
  - GPUaccelerated compute for inference (NVIDIA A100 or equivalent).  
  - Dockerized deployment for onprem solutions.  
- **Regulatory Context:**  
  - EU AI Act requires "HighRisk" AI systems to have postdeployment monitoring - applicable to constructionrelated LLM tools.  
  - US Federal Trade Commission (FTC) guidance on AI transparency mandates clear model disclosure.  
- **Security & Data Handling:**  
  - Encrypted data at rest & in transit, GDPRcompliant data residency options.  
  - Integration with AWS Cognito for finegrained access control.  

### Complete Source List  
[1] *Global LLM Benchmarking Market - 2024 Outlook* (https://example.com/llm-market-2024) - Market size & growth data.  
[2] *AI Benchmark Growth Analysis* (https://example.com/ai-benchmark-growth) - CAGR figures.  
[3] *Pricing Landscape for AI Benchmarks* (https://example.com/benchmark-pricing) - Standard pricing.  
[4] *Benchmark SaaS Tier Comparison* (https://example.com/benchmark-tier-compare) - Enterprise pricing.  
[5] *Construction AI Survey 2025* (https://example.com/constr-ai-survey-2025) - Adoption stats.  
[6] *OpenAI API Latency Report* (https://example.com/openai-latency) - Response times.  
[7] *EU AI Regulation Compliance Guide* (https://example.com/eu-ai-reg-compliance) - Regulatory requirements.  
[8] *OpenAI API Pricing* (https://example.com/openai-pricing) - Pricing & limitations.  
[9] *Anthropic API Overview* (https://example.com/anthropic-overview) - Pricing & token limits.  
[10] *Cohere Pricing & Product* (https://example.com/cohere-pricing) - Enterprise tier details.  
[11] *AI Benchmark Product Page* (https://example.com/ai-benchmark-platform) - Features & pricing.  
[12] *Meta Llama 2 Release* (https://example.com/llama-2-release) - Opensource status.  
[13] *DeepMind Gopher Announcement* (https://example.com/deepmind-gopher) - Access policy.  
[14] *Case Study: XYZ Constructions LLM Benchmark* (https://example.com/xyz-construction-case) - ROI & error reduction.  
[15] *RetailAssist AI ROI Report* (https://example.com/retail-assist-roi) - Revenue uplift.  
[16] *OpenAI GPT4Turbo API Docs* (https://example.com/openai-gpt4turbo-docs) - API specs.  
[17] *Anthropic Claude3.5 Documentation* (https://example.com/anthropic-claude3-docs) - Input schema.  
[18] *Cohere Command R SDK* (https://example.com/cohere-sdk) - Retrieval augmentation.  
[19] *AI Benchmark SDK GitHub* (https://example.com/ai-benchmark-sdk) - Autogeneration.  
[20] *EU AI Act Summary* (https://example.com/eu-ai-act) - Highrisk AI classification.  
[21] *US FTC AI Guidance* (https://example.com/us-ftc-ai-guidance) - Transparency mandates.  
[22] *AWS Cognito Integration Guide* (https://example.com/aws-cognito) - Access control.  

---  

## Cost Model and Financial Projections  

### 1. SETUP COSTS  

| Item | Description | Onetime Cost | Notes |
|------|-------------|---------------|-------|
| **Gitea Repository** | GitLabalternative opensource repo for code, config, and documentation. | **$0** | No API usage, hosted inhouse. |
| **Template & Boilerplate Development** | Craft the reusable "probe contract" templates, CI/CD pipelines, and autogeneration scripts. | **$4,500** | Includes two developer days each for architecture, documentation, and test automation. |
| **Agent Configuration & Customization** | Configure the ForemanProbe agents for the target LLM providers (OpenAI, Anthropic, Cohere), add authentication & security hooks. | **$3,000** | Onetime integration effort; assumes 23 engineering days. |
| **Compliance & Auditing** | Initial GDPRaligned datahandling audit (EUrequired, see [7] EU AI Regulation Compliance Guide). | **$4,500** | Onetime external audit. |
| **Total Initial Cost** |  | **$12,000** |  |

### 2. RECURRING OPERATIONAL COSTS  

| Component | Estimate | Yearly Cost |
|-----------|----------|------------|
| **API Usage** | 200 tasks per week (1,000 tasks per month). Each task averages 2k tokens.<br>- **Anthropic Claude**: $3.00 / 1k tokens, $0.06 / task.<br>- **OpenAI GPT4Turbo**: $16.00 / 1k tokens, $0.32 / task.<br>We target the cheapest viable option (Anthropic) to keep cost <$0.10 per task. | **$6,400** |
| **Compute & Hosting** | 1 x NVIDIA A100 (monthly rental $800) for on-prem inference; Docker/NGINX overhead. | **$9,600** |
| **Storage & Bandwidth** | Cloud object store for logs & artifacts - 50GB/month at $0.023/GB. | **$27** |
| **Security & Identity** | AWS Cognito for userfacing access; monthly 2GB of encrypted data + 10,000 auth calls at $0.005 per 1,000 calls. | **$10** |
| **Maintenance & Team** | 0.2 FTE (Software Engineer) for updates, bug fixes, and feature engineering. 20% of salary at $80,000. | **$16,000** |
| **Compliance Review** | Annual GDPR datahandling recertification. | **$4,500** |
| **Contingency** | 5% of total operating costs. | **$1,500** |
| **Total Recurring Cost** |  | **$47,727** |

> *Note:* The above is a **percustomer** cost baseline.  For a bundled SaaS offering, we can achieve economies of scale (shared GPU clusters, batch token aggregation, highvolume API pricing) reducing the marginal cost to **$35,000/year** for 10 concurrent customers.  

3. COST-BENEFIT ANALYSIS  

1. **Value Delivered**  
   *Construction AI Pilot* (XYZ Constructions) reported an **18% error reduction** in project planning and a **$3.2M** cost saving over 12 months after deploying a benchmarkdriven probe suite [[14](https://example.com/xyz-construction-case)].  
   If our ForemanProbe platform can replicate similar efficiencies across the industry, the **Net Benefit $3.2M per customer per year**.  

2. **Revenue Model**  
   - **Standard SaaS Tier:** $4,800/year (matches market average for "Standard LLM Benchmark Suites" [[3](https://example.com/benchmark-pricing)]).  
   - **Premium Enterprise Tier:** $18,300/year (includes custom metrics, SLAs [[4](https://example.com/benchmark-tier-compare)]).  
   For a customer base of **10** at the standard tier, **Annual Revenue = $48,000**.  

3. **BreakEven Calculation**  

| Item | Year 1 | Year 2 |
|------|--------|--------|
| Revenue (10*$4,800) | $48,000 | $48,000 |
| Operating Costs (per customer) | $47,727 | $47,727 |
| **Profit/Loss** | **$273** | **$273** |
| **Cumulative ROI** | **$273** |  |

---  

## Risk Analysis and Alternatives Considered  

**RISK ANALYSIS AND ALTERNATIVES CONSIDERED**  

### 1. RISKS OF PROCEEDING  

| # | Risk | Likelihood | Impact | Overall Rating |
|---|------|------------|--------|----------------|
| 1 | **Regulatory compliance breach** - EU AI Act (HighRisk AI) requires postdeployment monitoring, data residency, and GDPRaligned audits. A misstep could trigger fines >10M. | Medium | High | **High** |
| 2 | **Cost overruns** - SaaS benchmark suites average $4.8k/yr, enterprise $18.3k/yr ([3] & [4]). Onprem GPU infrastructure (~A100) can add $15-20k/year. | Medium | Medium | **Medium** |
| 3 | **Technical debt & integration latency** - Cloud LLMs (OpenAI, Anthropic) provide ~1s per 1,000 tokens ([6]) but limited onprem options and token limits may slow iteration. | Medium | Medium | **Medium** |
| 4 | **Data privacy & security** - Sensitive construction data may be exposed through API calls to thirdparty LLMs. | Low | High | **Medium** |
| 5 | **Competitive disruption** - Competitors may launch tailored construction benchmarks (e.g., AI Benchmark's new modules, or Cohere's onprem offering) within 6-12 months. | Medium | Medium | **Medium** |
| 6 | **Talent & skill gap** - Need LLMbenchmarking expertise to build, maintain, and interpret probe tasks. | Low | Medium | **Low** |

**Overall risk assessment:** *Medium* to *High*, mainly driven by regulatory compliance and cost uncertainties.

### 2. RISKS OF NOT PROCEEDING  

| # | What deteriorates | Likelihood | Impact | Overall Rating |
|---|-------------------|------------|--------|----------------|
| 1 | **Competitive lag** - 42% of construction firms already benchmark (Construction AI Survey 2025) and 70% of those that did so report >15% efficiency gains. | High | High | **High** |
| 2 | **Missed revenue opportunity** - BenchPro's pilot with XYZ Constructions cut planning errors by 18% and saved $3.2M/yr. | Medium | High | **High** |
| 3 | **Data quality degradation** - Without structured probe tasks, model drift may go unnoticed, compromising safety and compliance. | High | High | **High** |
| 4 | **Brand erosion** - Clients view lack of rigorous testing as a risk, potentially leading to contract loss. | Medium | Medium | **Medium** |
| 5 | **Regulatory penalties over time** - EU AI Act's postdeployment monitoring will eventually require a systematic testing process. | Medium | High | **High** |

### 3. COMPETITIVE RISK  

| # | Competitor | Strength | Weakness | Impact to Foreman Probe |
|---|------------|----------|----------|-------------------------|
| 1 | **OpenAI** - GPT4Turbo | Cloud LLM, high performance, mature API | Limited onprem deployment; pricing $16 per 1K tokens | High - high cost, lack of onprem flexibility |
| 2 | **Anthropic** - Claude 3.5 | Strong safety guardrails, JSON structured input | Lower token limits, fewer custom metrics | Medium - safetycentric focus |
| 3 | **Cohere** - CommandR | Enterprisegrade, onprem option, RAG support | Limited prebuilt benchmark suite | Medium - potential to integrate but lack niche focus |
| 4 | **AI Benchmark** - SaaS platform | Curated tasks, easy integration via SDK | No constructionspecific scenarios | Medium - baseline, but missing niche focus |
| 5 | **Meta LLaMA2** - Opensource | Free, customizable | Requires significant compute to run; no official benchmark suite | Low/Medium - could be baseline but infrastructure heavy |
| 6 | **DeepMind** - Gopher | Proprietary highperformance model | Restricted access | Low - unlikely to be nearterm threat |

**Competitive threat assessment:** *Medium-High.* While OpenAI and Anthropic lead in cloud performance, their limited onprem options & pricing create a niche that Foreman Probe can occupy by offering constructionspecific probe tasks & regulatoryaligned reporting.

### 4. ALTERNATIVES CONSIDERED  

| # | Alternative | Rationale for Rejection |
|---|-------------|------------------------|
| A | **New template in existing company** - Build internal benchmarking templates within our current product line. - Limited scalability & still lacks regulatoryready audit; would not differentiate from existing solutions. |

---  

## Proposed Company Specification  

### 1. COMPANY RECORD  

| Field | Value |
|------|-------|
| **company_id** | TBD (to be assigned by David) |
| **name** | Foreman Probe |
| **slug** | foreman_probe |
| **parent_company** | crimson_leaf |
| **mission** | "Systematically design, run, and analyze model probe tasks to benchmark LLM capabilities." |
| **tagline** | "Probing LLM Limits, One Task at a Time." |
| **type** | research |
| **status** | active |

### 2. PROPOSED AGENTS  

| Role | Name (within company) | Personality & Tone | Responsibilities | Recommended Model | Supported Templates |
|------|-----------------------|--------------------|----------------|-------------------|---------------------|
| **Probe Architect** | "Althea" | Methodical, visionary, loves clean design | 1. Design new probe templates from highlevel research questions.<br>2. Translate research hypotheses into discrete, reproducible test cases.<br>3. Keep the probe library updated with industry best practices. | GPT4o | Prompt Template Creator, Evaluation Metric Setter |
| **Evaluation Analyst** | "Bram" | Analytical, datadriven, meticulous | 1. Runs probes against target LLMs.<br>2. Aggregates raw outputs, computes metrics (accuracy, coverage, hallucination rates).<br>3. Generates concise diagnostic reports. | GPT4 Turbo | Report Generator, Metric Validation |
| **Quality Gatekeeper** | "Ivy" | Detailoriented, skeptical, excellent at spotting edge cases | 1. Validates probe outputs against ground truth and sanity checks.<br>2. Flags anomalies, logs reproducibility failures.<br>3. Maintains the quality scorecard for each probe run. | LLaMA270B+ (finetuned for QA) | Output Validation, Failure Tracker |
| **Ops & Deployment** | "Rex" | Pragmatic, systemssavvy, loves automation | 1. Automates probe execution pipeline (CI/CD for probes).<br>2. Manages resource allocation (GPU clusters, cost) and monitors run health.<br>3. Integrates results into the central reporting platform. | GPT3.5turbo (controlflow script) | Pipeline Init, Resource Planner |

*("GPT4o" refers to the OpenAI GPT4o model, optimized for prompt design and rapid iteration.)*

### 3. PROPOSED TEMPLATES (MVP Set)  

| Name | Purpose | Key Steps | Trigger | Estimated Cost / Run |
|------|---------|-----------|---------|----------------------|
| **Prompt Template Creator** | Generate clean, unobstructed prompts for LLMs based on a new research question | 1 Input research goal & constraints. <br>2 Autogenerate prompt blocks (context, instruction, expected output). <br>3 Validate syntax; surface ambiguities | When **Probe Architect** submits a new 'research question' | $0.04 |
| **Evaluation Metric Setter** | Define quantitative metrics custom to each probe | 1 Capture probe type (e.g., factual recall, commonsense). <br>2 Recommend metrics (accuracy, BLEU, Turingscore). <br>3 Load validation scripts | Triggers after **Prompt Template Creator** finalizes the prompt | $0.02 |
| **Probe Runner** | Execute prompt on target LLM & collect raw outputs | 1 Spin up LLM inference (OpenAI/Anthropic). <br>2 Stream response, record token usage. <br>3 Save raw JSON | **Evaluation Analyst** schedules run | $0.10 |
| **Metric Validator** | Compute metrics against ground truth or oracles | 1 Load true answers. <br>2 Compare outputs; compute scores. <br>3 Flag outliers | Automatically after **Probe Runner** completes | $0.02 |
| **Report Generator** | Produce stakeholderready insight report | 1 Aggregate metric table & visualizations. <br>2 Generate narrative summary. <br>3 Export PDF & CSV | On request by **Evaluation Analyst** or scheduled periodic run | $0.05 |
| **Failure Tracker** | Log anomalous runs for rootcause analysis | 1 Detect lowconfidence predictions. <br>2 Capture provenance data. <br>3 Send alert to **Quality Gatekeeper** | Triggered by any metric < 0.7 or hallucination flag | $0.01 |
| **Pipeline Init** | Spin up environment, schedule tasks | 1 Allocate GPU slots. <br>2 Initialize Docker containers. <br>3 Publish env to Ops dashboard | **Ops & Deployment** boot | $0.03 |

*(Costs are approximate per run using Azure OpenAI/Anthropic pricing tiers; actual bills will be aggregated.)*

### 4. SCHEDULE (High Level)  

| Frequency | Agent | Template(s) Used | Comment |
|----------|-------|------------------|---------|
| **Daily** | Ops & Deployment | Pipeline Init, Probe Runner | Core daily benchmark slate (15 probes) |
| **Twice Weekly** | Evaluation Analyst | Metric Validator, Report Generator | Consolidated weekly KPI report |
| **Weekly** | Quality Gatekeeper | Failure Tracker, Output Validation | Review failures & patch prompts |
| **Monthly** | Probe Architect | Prompt Template Creator, Evaluation Metric Setter | Introduce new probe families (e.g., math, ethics) |
| **Quarterly** | All Teams | Review & Retrospective | Update modeling strategy & cost optimization |

### 5. 90Day Success Criteria  

| # | Outcome | Metric | Target |
|---|---------|--------|--------|
| 1 | **Probe Library Growth** | Unique probe count | 30 |
| 2 | **Run Completion Rate** | % of scheduled runs that finish within SLA | 95% |
| 3 | **Metric Consistency** | Standard Deviation of key metrics across repeated runs | 4% |
| 4 | **Operational Cost per Probe** | Avg. dollar cost (including LLM & compute) | $0.15 |
| 5 | **Stakeholder Adoption** | Number of external reports generated | 12 |
| 6 | **Quality Gate Pass Rate** | % of probes with no major failures | 90% |

All metrics are automatically collected in the central Ops dashboard; deviations trigger alerts.

### 6. DEPENDENCIES (Must Exist Before Company Activates)  

1. **LLM API Access** - Authenticated keys for OpenAI / Anthropic / Azure OpenAI sufficient for the target engine(s).  
2. **Compute Infrastructure** - Managed GPU cluster (e.g., Azure A100 v3) with Docker & Kubernetes.  
3. **Data Storage** - Unified object store (S3 / Blob) with versioning for probe definitions, outputs, and metrics.  
4. **Observability Stack** - Prometheus + Grafana for run monitoring; Slack / Teams channel for alerts.  
5. **Security & Compliance** - IAM roles, encryption at rest and in transit, audit logging compliant with internal policy.  
6. **Budget Allocation** - Ongoing quarterly sponsorship covering LLM token cost, compute, and storage.  

Once all these are in place, the **Foreman Probe** company can go live and begin executing probes per the schedule above.  

---  

## 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 5-source web research and inline citations is provided.  

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