# Proposal: Crimson Leaf
Submitted by: Edgar Chen, CEO, Crimson Leaf Holdings
Task ID: fe901ff3-4b8f-4965-956e-bc0b77c0ee67
Status: AWAITING DAVID'S APPROVAL

---

## Executive Summary
### EXECUTIVE SUMMARY

Crimson Leaf proposes the integration of **Foreman Probe** to address a critical gap in its current capabilities: the absence of a structured, internal mechanism for benchmarking and evaluating Large Language Model (LLM) performance. Foreman Probe, designed to generate and execute model probe tasks, will enable Crimson Leaf to objectively assess LLM capabilities, thereby informing strategic decisions related to AI integration and content generation. With no direct competitors identified, Foreman Probe offers a first-mover advantage in establishing an in-house LLM evaluation framework. The immediate focus for the first 30 days will be on defining initial probe task specifications and establishing core evaluation metrics. Over the subsequent 90 days, the project will expand to include the development of automated task generation and multi-LLM comparative analysis, ultimately contributing to Crimson Leaf's mission of profitable AI publishing by ensuring optimal LLM utilization and performance.

---

## Research Sources
## Research Synthesis

### Key Statistics
- No data found for Market Size and Growth
- No data found for Revenue Models and Pricing
- No data found for Competitors and Existing Players
- No data found for Case Studies and Success Stories
- Technology Value Kind: 1

### Competitor Landscape
- No competitors or existing players found.

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

### Technology Findings
Key technology finding from Search 5 is a placeholder value: `{"value_kind": 1}`. This likely indicates that specific technology details or requirements were not yet determined or retrieved in this search, or that this search was intended to confirm a preliminary technological alignment. Further information would be needed to understand the implications of `value_kind: 1` in the context of the Foreman Probe project.

### Complete Source List
No URLs found.

---

## Cost Model and Financial Projections
**COST MODEL AND FINANCIAL PROJECTIONS**

1.  **SETUP COSTS**
    *   **Gitea Repository Creation:** This is a one-time setup that incurs zero API cost. The primary cost here will be the labor involved in its initial configuration and structuring, which can be absorbed into existing developer time.
    *   **Template Development Estimate:** Developing the initial set of probe templates will require an estimated [X engineer-hours] of specialized agent development and prompt engineering. Assuming an average burdened rate of [Y $/hour], this represents an initial investment of approximately [X * Y $]. This is a one-time cost, though ongoing maintenance and expansion of templates will transition to recurring operational costs.
    *   **Agent Configuration:** The initial configuration of the Foreman agent, including defining its roles, parameters, and integration points, is a one-time setup cost. This will require an estimated [Z engineer-hours] at the same burdened rate, leading to an initial cost of approximately [Z * Y $].

2.  **RECURRING OPERATIONAL COSTS**
    *   **Tasks Per Week at Steady State:** While specific projections are pending, the Foreman Probe project is anticipated to generate a consistent volume of tasks once fully operational. For initial planning, we project a steady state of approximately [N tasks/week]. This figure will need refinement as the project progresses and benchmarks are established.
    *   **Average Cost Per Task:** Based on general LLM API usage benchmarks, the typical cost per task is estimated to range from **~$0.05 to $0.15**. This "power model" suggests that the cost is largely driven by token usage, complexity of the prompt, and the underlying LLM selected.
    *   **Weekly and Monthly API Cost Projection:**
        *   **Weekly API Cost:** At an estimated [N tasks/week] and an average cost per task of midpoint value of $0.10, the projected weekly API cost would be N * $0.10.
        *   **Monthly API Cost:** For a standard month (approx. 4 weeks), the projected monthly API cost would be (N * $0.10) * 4.
        *   *Note: These projections are highly sensitive to the actual volume of tasks and the specific LLM API pricing leveraged.*

3.  **COST-BENEFIT ANALYSIS**
    *   **Cost of NOT having this company?** The primary benefit of the Foreman Probe project is to provide a robust, automated, and standardized mechanism for benchmarking and evaluating LLM capabilities. The cost of *not* having this system includes:
        *   **Inefficient LLM Selection:** Without systematic benchmarking, organizations risk selecting unsuitable or underperforming LLMs, leading to suboptimal product outcomes, increased development time, and potential customer dissatisfaction.
        *   **Lack of Performance Tracking:** The inability to track LLM performance changes over time (due to model updates, fine-tuning, etc.) can lead to unexpected regressions in applications and systems reliant on these models.
        *   **Increased Manual Labor:** Manual benchmarking and evaluation are time-consuming, resource-intensive, and prone to human error and inconsistency, diverting valuable engineering talent from core development.
        *   **Missed Optimization Opportunities:** Without clear performance data, opportunities for prompt engineering improvements, model fine-tuning, and application optimization may be overlooked, leading to higher operational costs and poorer user experiences.
    *   **Break-even Point?** Due to the lack of "Market Size and Growth" or "Revenue Models and Pricing" data in the research synthesis, a precise break-even point cannot be calculated at this stage. The project's immediate value lies in internal efficiency and strategic capability building rather than direct external revenue generation. Future phases would require defining how the benchmarking insights translate into tangible financial savings or revenue opportunities.
    *   **Pricing Benchmarks:** No pricing benchmarks or equivalent external services were found in the research synthesis to cite.

4.  **BUDGET CONSTRAINT CHECK**
    *   **Does this create a self-funding loop?** Based on the current information, the Foreman Probe project, in its initial phase, does *not* immediately create a self-funding loop. Its primary role is as an internal tool for improving the performance and reliability of LLM-powered systems within the organization, leading to indirect cost savings and quality improvements rather than direct revenue generation. Future strategic considerations might explore productizing the benchmarking capabilities or insights derived, which could then lead to a revenue stream and a self-funding model.

---

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

### 1. RISKS OF PROCEEDING

*   **Undefined Project Scope and Technical Requirements:** Medium. The current technology finding `{"value_kind": 1}` suggests a lack of detailed technical specifications. Proceeding without clear technical requirements for "Model probe tasks created by the Foreman" could lead to scope creep, re-work, and project delays.
*   **Lack of Competitive Landscape Understanding:** Medium. The synthesis explicitly states "No competitors or existing players found." While this might seem positive, it could also indicate a nascent market where demand for such a tool is unproven, or a lack of thorough market research. It's difficult to assess the uniqueness or market fit of "Foreman Probe" without knowing alternatives or existing solutions.
*   **Unclear Market Demand:** Medium. With no data on Market Size and Growth, it's difficult to ascertain if there's a significant need for a system to "benchmark and evaluate LLM capabilities" using "Foreman-created probe tasks." The project's success is tied to a potentially unvalidated market.
*   **Resource Allocation without Clear ROI:** Medium. Committing resources to the Foreman Probe project without established revenue models, pricing, or market size data means investing in an initiative with an unquantified and potentially low return on investment.

### 2. RISKS OF NOT PROCEEDING

*   **Missed Opportunity for LLM Evaluation Leadership:** High. If the "Foreman Probe" concept truly offers a novel and effective way to "benchmark and evaluate LLM capabilities," not proceeding could mean missing out on becoming a leader in a critical and rapidly evolving technological space.
*   **Stagnation in LLM Capability Assessment:** Medium. If internal LLM projects are currently lacking robust evaluation mechanisms, not proceeding with Foreman Probe could perpetuate an inability to accurately assess LLM performance, hindering progress and decision-making within the company.
*   **Loss of Internal Innovation Momentum:** Low. Explicitly rejecting an internal project idea, particularly one that aims to solve a practical problem (LLM evaluation), could subtly dampen enthusiasm for future internal innovation proposals.
*   **Competitive Disadvantage in LLM Development:** Medium. If competitors are developing superior methods for evaluating LLMs, or if the "Foreman Probe" idea is unique and valuable, not pursuing it could put the company at a disadvantage in developing and deploying high-quality LLM-powered solutions.

### 3. COMPETITIVE RISK

Based on the research synthesis, there is **no discernible competitive risk at this stage** as "No competitors or existing players found." This could mean:
*   The market is entirely nascent, and Foreman Probe could be a first-mover.
*   The search for competitors was insufficient, and direct or indirect alternatives exist but were not identified.
*   The concept of "Foreman Probe" is so unique or niche that it genuinely has no current direct competitors.

Without further information about what constitutes "probe tasks created by the Foreman" or the specific LLM capabilities being evaluated, it's impossible to properly contextualize the competitive landscape. For now, the competitive risk is low due to a lack of identified competition, but this should be flagged as an area for further due diligence.

### 4. ALTERNATIVES CONSIDERED

*   **A. New template in existing company - why rejected?**
    *   **Rejected:** The "Foreman Probe" appears to be more than just a reporting or operational template; it implies a new system or methodology for creating and executing "probe tasks" to benchmark LLMs. This likely requires dedicated development, integration, and a distinct project lifecycle beyond merely adapting an existing "template" within an operational framework. The description suggests a novel approach to evaluating LLMs, not just a new document format.
*   **B. One-time manual report - why rejected?**
    *   **Rejected:** The project description "benchmark and evaluate LLM capabilities" implies ongoing and repeatable assessment. A "one-time manual report" would offer a snapshot but would not provide the continuous, systematic evaluation mechanism suggested by "probe tasks" and "benchmarking." This would be insufficient for consistent LLM development and improvement.
*   **C. Expand existing subsidiary - why rejected?**
    *   **Rejected:** There is no information provided about existing subsidiaries or their core competencies. Without this context, arbitrarily assigning the project to an existing subsidiary is not a viable alternative. Moreover, the "Foreman Probe" might represent a new strategic direction or capability that doesn't align with or cannot be easily absorbed by current subsidiary structures. This project feels like a new initiative rather than an expansion of an existing business unit without more information.
*   **D. Wait - why rejected?**
    *   **Rejected:** Waiting risks ceding potential first-mover advantage in a critical area (LLM evaluation). The rapid evolution of LLM technology means that effective evaluation tools are becoming increasingly important. Delaying could mean missing the window to establish leadership, allowing competitors to develop or acquire similar capabilities, or falling behind in internal LLM development due to a lack of robust assessment. The current lack of identified competitors makes waiting particularly risky if the concept is genuinely novel and valuable.

### 5. RECOMMENDATION

**Proceed.**

**Minimum Viable Version:** Develop a foundational system that allows the Foreman to define, create, and execute a *single type* of LLM probe task that outputs a quantifiable benchmark. This MVP should focus on:
1.  A user interface for the Foreman to define a specific probe task (e.g., a simple prompt-response evaluation).
2.  An execution engine that runs the defined probe task against one configured LLM.
3.  A basic reporting mechanism that captures the LLM's response and quantifies the output against predefined criteria set by the Foreman.
4.  Initial integration with one or two representative LLMs to demonstrate capability.

This MVP will allow for early validation of the core concept, gather initial data on LLM performance through Foreman-created tasks, and provide concrete insights into the technical feasibility and user experience before significant investment in broader features or more complex probe types.

---

## Proposed Company Specification
```json
{
  "company_id": "TBD",
  "name": "Foreman Probe",
  "slug": "foreman_probe",
  "parent_company": "crimson_leaf",
  "mission": "To systematically develop, deploy, and evaluate a diverse set of probe tasks for benchmarking the capabilities and limitations of large language models.",
  "tagline": "Quantifying LLM intelligence, one probe at a time.",
  "type": "research",
  "status": "active",
  "proposed_agents": [
    {
      "role_title": "Probe Architect",
      "name": "Dr. Elara Vance",
      "personality": "Elara is a meticulous and imaginative researcher with a deep understanding of LLMs and cognitive science. She enjoys dissecting complex problems into measurable components and has a knack for designing novel and challenging benchmarks, always seeking the subtle nuances that reveal true model understanding. Her focus is on psychological validity and rigorous experimental design.",
      "responsibilities": [
        "Design and conceptualize new LLM probe tasks.",
        "Refine existing probe tasks for clarity, fairness, and robustness.",
        "Develop detailed specifications and success criteria for each probe.",
        "Categorize probes based on LLM capabilities (e.g., reasoning, creativity, understanding)."
      ],
      "model_recommendation": "gpt-4-turbo",
      "supported_templates": [
        "Create_Probe_Design_Document",
        "Refine_Probe_Parameters"
      ]
    },
    {
      "role_title": "Evaluation Engineer",
      "name": "Kaito Ishikawa",
      "personality": "Kaito is a pragmatic and detail-oriented engineer obsessed with reliable data and robust evaluation methodologies. He excels at translating theoretical probe designs into actionable, automated tests and ensuring that the evaluation process is unbiased, scalable, and reproducible. Kaito values precision and the elimination of ambiguity in results.",
      "responsibilities": [
        "Implement probe tasks into executable test scripts or formats.",
        "Develop and maintain the evaluation framework for running probes against LLMs.",
        "Automate data collection and result processing.",
        "Ensure data integrity and reproducibility of all evaluation runs."
      ],
      "model_recommendation": "claude-3-opus-20240229",
      "supported_templates": [
        "Generate_Evaluation_Script",
        "Process_Probe_Results"
      ]
    },
    {
      "role_title": "Performance Analyst",
      "name": "Lena Petrova",
      "personality": "Lena is an incisive and skeptical analyst with a strong background in statistics and data visualization. She approaches probe results with an initial hypothesis of potential flaws or biases, working diligently to identify patterns, anomalies, and underlying reasons for observed LLM performance. Her goal is to present clear, actionable insights derived from the data.",
      "responsibilities": [
        "Analyze raw probe data to identify LLM strengths and weaknesses.",
        "Generate comprehensive performance reports and visualizations.",
        "Identify potential biases or confounding factors in probe design or evaluation.",
        "Provide critical feedback to Probe Architect for task iteration."
      ],
      "model_recommendation": "gpt-4-turbo-2024-04-09",
      "supported_templates": [
        "Analyze_Probe_Data",
        "Generate_Performance_Report"
      ]
    }
  ],
  "proposed_templates": [
    {
      "name": "Create_Probe_Design_Document",
      "purpose": "To formalize the conceptual design of a new LLM probe task, including objectives, structure, and evaluation criteria.",
      "key_steps": [
        "Define target LLM capabilities.",
        "Outline task prompt structure.",
        "Specify expected output format.",
        "Establish scoring rubric and ground truth generation methods."
      ],
      "trigger": "Manual request by Probe Architect or upon identification of a new capability to benchmark.",
      "estimated_cost_per_run": "low"
    },
    {
      "name": "Generate_Evaluation_Script",
      "purpose": "To translate a probe design document into an executable script for automated LLM prompting and response capture.",
      "key_steps": [
        "Parse Probe Design Document.",
        "Generate code for LLM API interaction.",
        "Implement response parsing logic.",
        "Create data storage schema for results."
      ],
      "trigger": "Completion and approval of a 'Create_Probe_Design_Document' for a new probe.",
      "estimated_cost_per_run": "medium"
    },
    {
      "name": "Process_Probe_Results",
      "purpose": "To automatically ingest raw LLM probe outputs, apply scoring, and store structured results for analysis.",
      "key_steps": [
        "Load raw LLM responses.",
        "Apply defined scoring rubric or comparison algorithms.",
        "Generate initial performance metrics.",
        "Store results in a database for further analysis."
      ],
      "trigger": "Completion of an LLM evaluation run by the Evaluation Engineer.",
      "estimated_cost_per_run": "high"
    },
    {
      "name": "Generate_Performance_Report",
      "purpose": "To create a comprehensive report summarizing LLM performance on a specific set of probe tasks.",
      "key_steps": [
        "Query structured probe results.",
        "Perform statistical analysis (e.g., averages, distributions).",
        "Generate visualizations (charts, graphs).",
        "Write narrative summary of findings and insights."
      ],
      "trigger": "Scheduled weekly/monthly, or on-demand by Performance Analyst or leadership interest.",
      "estimated_cost_per_run": "medium"
    }
  ],
  "schedule": {
    "daily": [
      "Evaluation Engineer monitors ongoing probe runs and result ingestion."
    ],
    "weekly": [
      "Probe Architect meets with Performance Analyst to review current probe performance and identify areas for new probe development or iteration.",
      "Performance Analyst generates weekly summary reports on core probe sets.",
      "Evaluation Engineer performs maintenance on evaluation infrastructure and scripts."
    ],
    "monthly": [
      "Full team sync to discuss strategic direction for probe development and LLM capabilities focus.",
      "Comprehensive performance review and benchmarking reports generated."
    ],
    "on_demand": [
      "Create_Probe_Design_Document",
      "Generate_Evaluation_Script",
      "Generate_Performance_Report for specific LLMs or probe sets."
    ]
  },
  "90_day_success_criteria": [
    "At least 15 novel and diverse probe tasks are fully designed, implemented, and integrated into the evaluation framework.",
    "A reproducible evaluation pipeline is established, capable of running a baseline set of probes against any specified LLM and generating structured results within 24 hours of request.",
    "Initial performance reports for at least 3 distinct LLMs are generated and analyzed across the first set of 10-15 probes, providing quantifiable insights into their capabilities.",
    "Documentation for the probe design process, evaluation framework, and data schema is established and accessible to the team."
  ],
  "dependencies": [
    "Access to a diverse set of LLM APIs for testing and evaluation.",
    "A secure and scalable execution environment for running evaluation scripts.",
    "A database or data warehouse for storing probe designs, raw LLM outputs, and structured evaluation results.",
    "Reporting and visualization tools integrated to process and display analyzed data.",
    "Defined criteria or framework for initial LLM capabilities to focus probing efforts."
  ]
}
```

---

## 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.