# Proposal: Crimson Leaf
Submitted by: Edgar Chen, CEO, Crimson Leaf Holdings
Task ID: e89c6cc6-b077-423f-b74a-0ac71cc6483c
Status: AWAITING DAVID'S APPROVAL

---

## Executive Summary
## Foreman Probe: Executive Summary

**1. PROPOSED COMPANY: Crimson Leaf**

*   **Company:** Crimson Leaf
*   **Purpose:** Crimson Leaf aims to be a leader in AI-driven educational resources and publishing.
*   **Gap:** This project allows Crimson Leaf to systematically evaluate and benchmark LLM performance, a key capability not currently possessed.

**2. PROBLEM STATEMENT**

Crimson Leaf currently lacks a standardized, automated, and scalable methodology for evaluating the capabilities of different Large Language Models (LLMs) on complex, educational-focused tasks. Without the "Foreman Probe" project, Crimson Leaf cannot rigorously assess which LLMs are best suited for different educational applications, hindering informed decision-making and product development. Specifically, Crimson Leaf lacks the ability to create, execute, and monitor diverse "probe tasks" designed to evaluate agentic reasoning, tool use, and multi-step planning capabilities within LLMs.

**3. MARKET OPPORTUNITY**

The AI in Education market presents a significant growth opportunity. The [Global AI in Education Market Size Forecast (2032)](https://www.grandviewresearch.com/industry-analysis/artificial-intelligence-ai-in-education-market) projects a market size of $25.7 Billion by 2032, with a [CAGR of AI in Education Market (2024-2032)](https://www.grandviewresearch.com/industry-analysis/artificial-intelligence-ai-in-education-market) of 28.8% from 2024 to 2032. While [Global EdTech Funding in 2023 Drops to US$8.1B, But the Sector Remains Resilient and Innovation Continues](https://holon.iq/news/global-edtech-funding-in-2023-drops-to-us8-1b-but-the-sector-remains-resilient-and-innovation-continues), over [$50 Billion](https://holon.iq/news/global-edtech-funding-in-2023-drops-to-us8-1b-but-the-sector-remains-resilient-and-innovation-continues) has been invested in EdTech between 2020 and 2023, indicating sustained interest and investment in the sector.

**4. PROPOSED SOLUTION**

The "Foreman Probe" project will develop a framework for creating, executing, and evaluating LLM performance on educationally relevant, complex reasoning tasks.

*   **First 30 Days:** Establish the core architecture for probe task execution and monitoring. Begin developing a library of initial probe tasks focused on basic agentic reasoning. Set up API access to target LLMs (e.g., OpenAI).
*   **First 90 Days:** Expand the probe task library with more complex, multi-step tasks involving tool use and planning. Implement automated evaluation metrics and reporting. Conduct initial benchmark tests of several LLMs using the framework.

**5. STRATEGIC FIT**

The "Foreman Probe" project directly advances Crimson Leaf's mission of profitable AI publishing. By establishing a robust LLM evaluation framework, Crimson Leaf can:

*   Identify the most effective LLMs for specific educational applications.
*   Reduce the risk associated with adopting unproven AI technologies.
*   Develop superior AI-powered educational products that meet market demands.
*   Establish a competitive advantage through data-driven AI product development.

---

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

### Key Statistics
- [Global AI in Education Market Size Forecast (2032)]: $25.7 Billion -- Source: [AI In Education Market Size, Share & Trends Report 2032](https://www.grandviewresearch.com/industry-analysis/artificial-intelligence-ai-in-education-market)
- [CAGR of AI in Education Market (2024-2032)]: 28.8% -- Source: [AI In Education Market Size, Share & Trends Report 2032](https://www.grandviewresearch.com/industry-analysis/artificial-intelligence-ai-in-education-market)
- [Typical cost per credit hour for college courses in USA]: Approx. $300-$600  -- Source: [Education Tuition Cost Statistics | EducationData.org](https://educationdata.org/average-cost-of-college)
- [Investment into edtech (2023)]: $8.1 billion -- Source: [Global EdTech Funding in 2023 Drops to US$8.1B, But the Sector Remains Resilient and Innovation Continues](https://holon.iq/news/global-edtech-funding-in-2023-drops-to-us8-1b-but-the-sector-remains-resilient-and-innovation-continues)
- [EdTech Investment (2020-2023)]:  Over $50 Billion -- Source: [Global EdTech Funding in 2023 Drops to US$8.1B, But the Sector Remains Resilient and Innovation Continues](https://holon.iq/news/global-edtech-funding-in-2023-drops-to-us8-1b-but-the-sector-remains-resilient-and-innovation-continues)
- [AI Foundation Model training costs]: Can range from a few million to hundreds of millions of dollars. -- Source: [The economic potential of generative AI: The next productivity frontier](https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier)

### Competitor Landscape
- Gradescope: AI-powered grading and feedback tool for educators. It helps automate grading, provide personalized feedback, and track student performance. [Gradescope](https://www.gradescope.com/)
- Crowdmark:  Another collaborative grading and assessment platform.   [Crowdmark](https://crowdmark.com/)
- Turnitin: Focuses on academic integrity, plagiarism detection, and AI writing detection. [Turnitin](https://www.turnitin.com/)
- ExamSoft: Secure assessment platform for high-stakes exams in various fields, focusing on preventing cheating and ensuring exam integrity. [ExamSoft](https://examsoft.com/)
- Questionmark: Assessment management system for creating, delivering, and reporting on quizzes, tests, and exams. [Questionmark](https://www.questionmark.com/)
- Proctortrack: AI-powered proctoring solution for online exams, focusing on preventing cheating and verifying student identity. [Proctortrack](https://www.verificient.com/proctortrack)

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

### Technology Findings
- LLMs: Large Language Models (GPT, Bard, etc.) are the fundamental AI.
- APIs:  Access to LLMs require APIs (e.g., OpenAI API).
- Cloud Computing:  Infrastructure for running LLMs and storing data.
- Python: Popular language for development and API interfacing.
- Evaluation Metrics:  BLEU, ROUGE for text generation.  Custom metrics will need to be developed to evaluate agentic reasoning.
- Need to model or generate diverse "probe tasks" that can credibly evaluate complex agentic reasoning. This must include tool-use and multi-step planning.
- Requires an environment/sandbox to execute and monitor LLM performance on probe tasks. This likely consists of an event-driven system for tracking API calls, errors and progress.
- Regulatory: GDPR and educational privacy laws apply to student data and usage.

### Complete Source List
[1] [AI In Education Market Size, Share & Trends Report 2032](https://www.grandviewresearch.com/industry-analysis/artificial-intelligence-ai-in-education-market) -- Market size and growth projections for AI in education.
[2] [Education Tuition Cost Statistics | EducationData.org](https://educationdata.org/average-cost-of-college) -- Tuition cost statistics for colleges in the USA.
[3] [Global EdTech Funding in 2023 Drops to US$8.1B, But the Sector Remains Resilient and Innovation Continues](https://holon.iq/news/global-edtech-funding-in-2023-drops-to-us8-1b-but-the-sector-remains-resilient-and-innovation-continues) -- Provides data on edtech funding trends and investment.
[4] [The economic potential of generative AI: The next productivity frontier](https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier) -- General overview of Generative AI market.

[5] [Gradescope](https://www.gradescope.com/) -- Information about Gradescope's AI-powered grading tool.
[6] [Crowdmark](https://crowdmark.com/) --  Platform for collaborative grading.
[7] [Turnitin](https://www.turnitin.com/) --  Details on Turnitin's plagiarism and AI writing detection capabilities.
[8] [ExamSoft](https://examsoft.com/) -- Secure assessment platform information.
[9] [Questionmark](https://www.questionmark.com/) -- Assessment management system features.
[10] [Proctortrack](https://www.verificient.com/proctortrack) -- AI-powered proctoring solution details.

---

## Cost Model and Financial Projections
Okay, here's the "Cost Model and Financial Projections" section for the Foreman Probe project proposal, incorporating the research synthesis where applicable:

**COST MODEL AND FINANCIAL PROJECTIONS**

This section outlines the costs associated with the development and operation of the Foreman Probe project, along with a preliminary cost-benefit analysis.

**1. Setup Costs**

*   **Gitea Repository Creation:**  Creating a Gitea repository for code and project management is a one-time cost with minimal to no direct API cost.  Estimated Cost: $0 for Crimson Leaf -- already established.
*   **Template Development Estimate:**  This encompasses the time and resources required to develop the initial probe task templates and the evaluation framework. We estimate this will require 2-3 senior engineers approximately 2 weeks resulting in -- Estimated Cost: $20,000 - $30,000 (Based on assumed loaded engineer cost of $5,000/week/engineer).
*   **Agent Configuration & Sandbox Environment:** Setting up the initial agent configurations, API access, and the sandbox environment for executing and monitoring LLM performance. We include an event-driven system for tracking api calls and errors. Estimated Cost: $10,000 - $20,000 (Infrastructure and Engineering)

**Total Estimated Setup Costs: $30,000 - $50,000**

**2. Recurring Operational Costs**

*   **Tasks Per Week at Steady State:** This is highly variable and depends on the number of probes we aim to run and the frequency of evaluation. As our goal is ongoing continual testing we will set preliminary estimate goal of running 100 probe tasks per week.
*   **Average Cost Per Task:** The cost per task is primarily driven by API usage costs. Based on current LLM pricing models (e.g., OpenAI API), the typical cost per task of this complexity can range from $0.05 to $0.15.  We will also add fixed cost of infrastructure costs associated with maintenance, storage, and monitoring: Estimated at $0.02-0.05 / task.
*   **Weekly API & Infrastructure Cost Projection:** Assuming 100 tasks per week and accounting for infrastructure, the weekly cost is projected to be between $7 and $20 per task, so $700 to $2,000.  Monthly cost: $2,800 to $8,000.

**Total Estimated Monthly Operational Costs: $2,800 - $8,000**

**3. Cost-Benefit Analysis**

*   **Cost of *NOT* having this company?** This is where the tangible benefits become clear. The lack of rigorous LLM evaluation can lead to:
    *   **Development of inadequate or inappropriate systems:**  Investing in a system that proves to be ineffective or outright wrong will waste development resources and time on the market.
    *   **Increased QA & security costs:** Addressing issues retroactively is substantially more costly.
    *   **Reduced user trust:** Inaccurate or inconsistent responses can erode user trust leading to poor user experience and adoption .
    *   **Missed Market Opportunity:** Competitors who can reliably deliver better systems will gain market share.
*   **Break-Even Point:** A simple estimate on break-even point would require that the product or service developed using the probe becomes at least 10 customers -- although that is more applicable in the case of a commercial entity. We are testing AI in education use case and the benefits are far more strategic as the AI in Education market size is forecast at $25.7 Billion by 2032 [1].
*   **Pricing Benchmarks:** While direct pricing comparisons are difficult, tools like Gradescope ([Gradescope](https://www.gradescope.com/))  provide a data point. They offer tiered pricing based on enrollment size, suggesting a value proposition around efficient grading and feedback. Our probe would ideally be part of the development cycle of such AI enhanced tools.

**4. Budget Constraint Check**

*   **Does this create a self-funding loop?** The potential for a self-funding loop depends on how this probe is integrated into the larger product, service, or research. If the probe leads to improved system performance, reliability, and user trust as demonstrated by a case study the result would be significant.

**Summary**

The Foreman Probe project requires an initial investment of $30,000 - $50,000 and ongoing monthly operational costs of $2,800 to $8,000. While these costs need to be carefully managed, the benefits associated with proactive and rigorous LLM evaluation provide significant value, especially within rapidly growing AI in Education market, projected to reach $25.7 billion by 2032 [1].

---

## Risk Analysis and Alternatives Considered
OK. I will generate the "RISK ANALYSIS AND ALTERNATIVES CONSIDERED" section for the Foreman Probe project proposal.

```text
## Risk Analysis and Alternatives Considered

### 1. Risks of Proceeding

*   **Technical Feasibility (Medium):** Developing probe tasks that effectively evaluate complex agentic reasoning, including tool use and multi-step planning, is challenging. Requires significant R&D in prompt engineering and evaluation metrics.
*   **LLM Dependence (Medium):** Reliance on external LLM APIs (e.g., OpenAI) introduces risks related to API availability, pricing fluctuations, and potential changes in LLM capabilities.
*   **Cost Overruns (Medium):** Costs associated with LLM API usage, cloud computing infrastructure, and personnel may exceed initial estimates. Training foundation models is extremely expensive [The economic potential of generative AI: The next productivity frontier](https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier).
*   **Data Privacy & Security (High):** Handling student data requires strict adherence to GDPR, FERPA, and other relevant privacy regulations. Data breaches or non-compliance can lead to significant legal and reputational damage.
*   **Market Acceptance (Low):** While the AI in Education market is growing [AI In Education Market Size, Share & Trends Report 2032](https://www.grandviewresearch.com/industry-analysis/artificial-intelligence-ai-in-education-market), educators might be hesitant to adopt new tools for evaluating LLMs, especially if the value proposition isn't clear.
*   **Evaluation Metric Accuracy (Medium):** Current evaluation metrics (BLEU, ROUGE) are inadequate for agentic reasoning. Developing new custom metrics and validating their effectiveness is essential.
*   **Regulatory Changes (Low):** New AI regulations could impact the legality and ethical considerations of the project.

### 2. Risks of Not Proceeding

*   **Missed Market Opportunity (High):** The AI in Education market is projected to reach $25.7 billion by 2032 with a CAGR of 28.8% [AI In Education Market Size, Share & Trends Report 2032](https://www.grandviewresearch.com/industry-analysis/artificial-intelligence-ai-in-education-market) Not pursuing the project means missing out on a potentially lucrative market.
*   **Competitive Disadvantage (Medium):** Competitors like Gradescope [Gradescope](https://www.gradescope.com/) and Turnitin [Turnitin](https://www.turnitin.com/) already offer AI-powered tools, and not developing Foreman Probe could lead to a loss of market share.
*   **Delayed Innovation (Medium):** Prevents Crimson Leaf from taking a leading role in AI-driven educational assessment and innovation.
*   **Talent Drain (Low):** High-skilled AI engineers may not be interested in no-AI R&D, and leave for competitor firms as a result.

### 3. Competitive Risk

Several competitors already exist in the AI-powered education assessment space. Turnitin [Turnitin](https://www.turnitin.com/) and Gradescope [Gradescope](https://www.gradescope.com/) are established players. Turnitin focuses on academic integrity and AI writing detection, while Gradescope offers AI-powered grading and feedback tools. ExamSoft [ExamSoft](https://examsoft.com/) provides a secure assessment platform. Ignoring these competitors could lead to a smaller market share for Crimson Leaf. The differentiation of Foreman Probe must be significant enough to stand out.

### 4. Alternatives Considered

*   **A. New Template in Existing Company (Rejected):** Creating a template within an existing assessment company would limit the scope and flexibility of the project. Foreman Probe requires a dedicated and independent team to focus on the unique challenges of evaluating LLM agents.
*   **B. One-Time Manual Report (Rejected):** A one-time report would not provide ongoing and scalable assessment capabilities. The rapidly evolving nature of LLMs requires a continuous evaluation framework.
*   **C. Expand Existing Subsidiary (Rejected):** Existing subsidiaries may lack the necessary expertise in AI agent evaluation and the agility required for this project. A new standalone entity allows for focused development.
*   **D. Wait (Rejected):** The AI in Education market is rapidly evolving. Delaying the project would provide competitors with a first-mover advantage and make it more difficult to enter the market later.

### 5. Recommendation

Proceed with the Foreman Probe project.

The minimum viable version should consist of:

1.  A functional environment/sandbox for running and monitoring LLM performance.
2.  A starter set of 10-20 probe tasks specifically designed to test tool-use and multi-step planning.
3.  A custom evaluation metric suite tailored for agentic reasoning.
4.  Basic data privacy and security measures implemented.

This MVP will allow for initial testing, validation, and iteration, while minimizing initial investment.
```

---

## Proposed Company Specification
Okay, I will draft a proposed company specification for "Foreman Probe" under "Crimson Leaf," following the requested format.

***

**PROPOSED COMPANY SPECIFICATION: FOREMAN PROBE**

1.  **COMPANY RECORD**
    *   company_id: TBD
    *   name: Foreman Probe
    *   slug: foreman_probe
    *   parent_company: crimson_leaf
    *   mission: To design and execute benchmark probes derived from Foreman tasks to rigorously evaluate the performance of large language models.
    *   tagline: "Probing the depths of LLM capabilities, one Foreman task at a time."
    *   type: research
    *   status: active

2.  **PROPOSED AGENTS**

    *   **Role:** Probe Architect
        *   **Name:** Anya Sharma
        *   **Personality:** Anya is a highly analytical and detail-oriented researcher with expertise in task decomposition and experimental design. She is meticulous in ensuring probe methodologies are robust and results are statistically significant, while fostering open communication and feedback.
        *   **Responsibilities:** Design and document probe tasks reflecting real-world Foreman workflows. Define evaluation metrics and scoring rubrics. Analyze probe results and identify areas for LLM improvement.
        *   **Model Recommendation:** GPT-4 (for reasoning and complex task analysis)
        *   **Supported Templates:** "Probe Definition," "Evaluation Metric Definition," "Results Analysis Report"

    *   **Role:** Probe Executor
        *   **Name:** Ben Carter
        *   **Personality:** Ben is an efficient and reliable system administrator with a strong understanding of LLM APIs and Foreman. He is responsible for orchestrating prompt executions, capturing outputs, and pre-processing data for analysis always seeking to improve process automation.
        *   **Responsibilities:** Run probes against designated LLMs via their APIs. Collect and store LLM outputs and performance data. Automate probe execution and data collection processes.
        *   **Model Recommendation:** GPT-3.5 Turbo (for speed and cost-effectiveness in bulk execution)
        *   **Supported Templates:** "Execution Script," "Data Capture Protocol," "Error Handling Log"

    *   **Role:** Performance Analyst
        *   **Name:** Chloe Davis
        *   **Personality:** Chloe is a insightful data scientist with a passion for uncovering trends and insights within large datasets. They are skilled in statistical analysis, data visualization, and translating complex information into actionable recommendations.
        *   **Responsibilities:** Perform statistical analysis on probe results. Generate reports summarizing LLM performance on various Foreman tasks. Identify patterns of strengths and weaknesses in LLM capabilities.
        *   **Model Recommendation:** Specialist Data Analysis model (local or API based if available)
        *   **Supported Templates:** "Statistical Analysis Script," "Visualization Dashboard Configuration," "Performance Report Template"

3.  **PROPOSED TEMPLATES (MVP Set)**

    *   **Name:** Probe Definition
        *   **Purpose:** Clearly define the Foreman task to be probed, its inputs, expected outputs, and variations.
        *   **Key Steps:** Task description, input parameter specification, output validation criteria, edge case identification.
        *   **Trigger:** New Foreman task identified for probing, request from Crimson Leaf engineering.
        *   **Estimated Cost Per Run:** $1-2 (primarily for architecture agent usage)

    *   **Name:** Execution Script
        *   **Purpose:** Automate the execution of a defined probe against a specific LLM API.
        *   **Key Steps:** API authentication, prompt formatting, execution of the API call with the probe, capture of response data.
        *   **Trigger:** A completed Probe Definition.
        *   **Estimated Cost Per Run:** $0.10 - $0.50 (depends on LLM API cost & number of runs)

    *   **Name:** Performance Report Template
        *   **Purpose:** Standardized format for reporting the results of probe executions, highlighting LLM performance metrics.
        *   **Key Steps:** Data aggregation, statistical analysis results, visualizations of key metrics, summary of findings.
        *   **Trigger:** Completion of a set of probe executions for a specific Foreman task.
        *   **Estimated Cost Per Run:** $0.5 - $1 (analyst agent driven summary)

4.  **SCHEDULE**

    *   **Weekly:** Review of new Foreman tasks for potential probe candidates.
    *   **Bi-weekly:** Design of new probes based on selected Foreman tasks.
    *   **Continuous:** Execution of existing probes against target LLMs.
    *   **Monthly:** Generation of performance reports summarizing LLM capabilities.

5.  **90-DAY SUCCESS CRITERIA**

    *   Completion of probe definitions for at least 10 key Foreman tasks.
    *   Automation of probe execution, achieving a minimum of 100 probe runs per task per LLM
    *   Generation of at least 3 monthly performance reports summarizing LLM performance on Foreman tasks.
    *   Identification of 3 actionable recommendations for improving LLM integration with Foreman.
    *   Documented Standard Operation Procedures for all tasks (Probe Definition, Execution, Results Analysis).

6.  **DEPENDENCIES**

    *   Access to Foreman task definitions and specifications.
    *   API access to target LLMs (e.g., OpenAI API, Cohere API).
    *   Clear definition of desired Foreman integration workflow with the LLMs.
    *   Stable development and analysis environment with necessary programming tools and data storage.

***

---

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.