# Proposal: Crimson Leaf Proposes: Foreman Probe
Submitted by: Edgar Chen, CEO, Crimson Leaf Holdings
Task ID: f75e117d-cf95-4045-b8dc-4a7dedd2ce2a
Status: AWAITING DAVID'S APPROVAL

---

## Executive Summary
## Crimson Leaf Proposes: Foreman Probe

**1. PROPOSED COMPANY**

*   **Company:** Foreman Probe
*   **Purpose:** Foreman Probe provides a suite of benchmark tasks created to evaluate the capabilities of large language models.
*   **Gap Closure:** Foreman Probe enables Crimson Leaf to rigorously evaluate and compare different LLMs, thereby improving the quality and reliability of AI content generation.

**2. PROBLEM STATEMENT**

Crimson Leaf lacks a standardized, repeatable, and comprehensive method for evaluating the performance of LLMs used in content creation, making it difficult to objectively compare models, identify areas for improvement, and ensure consistent quality across different AI-generated outputs. Without this, Crimson Leaf relies on subjective assessments and may miss crucial performance bottlenecks, leading to suboptimal content and inefficient resource allocation.

**3. MARKET OPPORTUNITY**

The generative AI market is rapidly expanding, presenting a significant need for robust evaluation tools. The [Generative AI Market Size, Share, Growth, Statistics | Forecast 2029](https://www.fortunebusinessinsights.com/generative-ai-market-106712) projects this market to reach $207.80 billion by 2029, up from $66.60 billion in 2024. As content creation increasingly relies on AI, the demand for quality assurance and objective LLM evaluation will grow substantially. While figures are not specifically listed, the [AI in Healthcare Market Forecasts to 2033 - GlobalData](https://www.globaldata.com/store/report/ai-in-healthcare-market-forecast/) at $146.42 billion and the [Artificial Intelligence (AI) in Manufacturing Market Size, Share | Industry Report, 2032](https://www.fortunebusinessinsights.com/artificial-intelligence-ai-in-manufacturing-market-100068) at $224.17 billion by 2032 indicate the broad need for the technologies LLMs enable.

**4. PROPOSED SOLUTION**

Foreman Probe offers a structured framework for evaluating LLMs used in Crimson Leaf's content creation workflows.

*   **First 30 Days:** Implement Foreman Probe's standard benchmark tasks to evaluate the performance of existing LLMs. Integrate initial results with Crimson Leaf's content creation pipelines to identify immediate areas for improvement like factual accuracy.
*   **First 90 Days:** Customize Foreman Probe to include tasks derived from typical Crimson Leaf content scenarios. Develop a dashboard to track LLM performance metrics, enabling objective model comparison and informed selection based on content type and quality requirements.

**5. STRATEGIC FIT**

Implementing Foreman Probe aligns directly with Crimson Leaf's mission of profitable AI publishing by ensuring high-quality, reliable AI-generated content. By using benchmark data to optimize LLM selections and workflows, Crimson Leaf can reduce editing costs, improve content quality, and efficiently scale content production, driving increased profitability.

---

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

### Key Statistics
- [Global AI in Healthcare Market Size Forecast]: $146.42 billion by 2033 -- Source: [AI in Healthcare Market Forecasts to 2033 - GlobalData](https://www.globaldata.com/store/report/ai-in-healthcare-market-forecast/)
- [AI in Manufacturing Market Size Forecast]: $34.78 billion market in 2024, projected to grow to $224.17 billion by 2032 -- Source: [Artificial Intelligence (AI) in Manufacturing Market Size, Share | Industry Report, 2032](https://www.fortunebusinessinsights.com/artificial-intelligence-ai-in-manufacturing-market-100068)
- [AI Sales Enablement Market Growth]: Expected to grow from USD 4.1 billion in 2024 to USD 10.4 billion by 2029 -- Source: [AI Sales Enablement Market by Offering (Solutions and Services), Application (Content Management, Activity Management, Performance Management, Pipeline Management), Deployment Mode (Cloud, On-Premises), Vertical and Region - Global Forecast to 2029](https://www.marketsandmarkets.com/Market-Reports/ai-sales-enablement-market-258542878.html)
- [NVIDIA's AI market dominance]: Over 80% market share -- Source: [Nvidia Dominates the AI Chip Market. Here Are Its Potential Challengers. - Barron's](https://www.barrons.com/articles/nvidia-stock-ai-chips-72819a20)
- [Generative AI Market Size Forecast]: $66.60 billion in 2024, projected to reach $207.80 billion by 2029 -- Source: [Generative AI Market Size, Share, Growth, Statistics | Forecast 2029](https://www.fortunebusinessinsights.com/generative-ai-market-106712)
- [Databricks Valuation]: $43 billion valuation -- Source: [Databricks' New Valuation Makes It One Of The World's Most Valuable Private Companies](https://news.crunchbase.com/ai-robotics/databricks-new-valuation-makes-it-one-of-the-worlds-most-valuable-private-companies/)
- [IBM's Total Revenue in 2023]: $61.9 billion -- Source: [IBM shares rise on revenue beat, strong AI and software demand - CNBC](https://www.cnbc.com/2024/01/24/ibm-ibm-earnings-q4-2023.html)

### Competitor Landscape
- [Arthur]: Provides model evaluation, monitoring, and feedback tools for enterprises | Pricing not stated | N/A-- Source: [Model Evaluation, Monitoring, and Feedback for Enterprises - Arthur](https://www.arthur.ai/)
- [Fiddler AI]:  Provides model monitoring and explainable AI solutions | Pricing not stated | N/A -- Source: [Fiddler AI | Enterprise Model Monitoring & Explainable AI](https://www.fiddler.ai/)
- [Arize AI]:  ML observability platform that provides model monitoring, drift detection, and performance analytics | Pricing not stated | N/A -- Source: [ML Observability Platform: Monitor, Detect & Improve ML Models | Arize AI](https://www.arize.com/)
- [WhyLabs]: AI observability platform for monitoring and improving AI models | Pricing not stated | N/A -- Source: [AI Observability Platform | WhyLabs](https://whylabs.ai/)
- [Verta.ai]: Model management and deployment platform for ML models | Pricing not stated | Focuses on deployment rather than specific LLM evaluation-- Source: [Model Management and Deployment Platform | Verta AI](https://www.verta.ai/)
- [MLflow]: Open-source platform for managing the end-to-end ML lifecycle | Open Source | Requires in-house expertise to set up and maintain-- Source: [MLflow](https://mlflow.org/)
- [Weights & Biases]: Platform for tracking and visualizing ML experiments | Offers free and paid tiers | Focus is on experimentation rather than post-deployment LLM evaluation-- Source: [Weights & Biases](https://wandb.ai/site)
- [Comet]: Platform for tracking, comparing, and optimizing ML experiments | Offers free and paid tiers | Focus is on experimentation rather than post-deployment LLM evaluation-- Source: [Comet](https://www.comet.com/site/)
- [IBM Watson]: A suite of AI-powered services including natural language processing, machine learning, and knowledge representation | Pricing varies widely depending on the services | Can be complex to implement and integrate-- Source: [IBM - United States](https://www.ibm.com/us-en)
- [Google AI Platform]: A comprehensive platform for developing, deploying, and managing machine learning models | Pay-as-you-go pricing | Can be expensive for large-scale deployments-- Source: [Google Cloud AI Platform: AI/ML Solutions for Every Industry](https://cloud.google.com/ai-platform)
- [AWS SageMaker]: A fully managed machine learning service that enables data scientists and developers to build, train, and deploy machine learning models quickly. | Pay-as-you-go pricing | Can be complex to configure and manage-- Source: [Amazon SageMaker - Build, Train, and Deploy Machine Learning Models | Amazon Web Services](https://aws.amazon.com/sagemaker/)

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

### Technology Findings
- **NVIDIA GPUs:** Essential for training and running large language models. -- Source: [Nvidia Dominates the AI Chip Market. Here Are Its Potential Challengers. - Barron's](https://www.barrons.com/articles/nvidia-stock-ai-chips-72819a20)
- **Langchain:** Framework for building applications powered by language models. -- Source: [What is Langchain: All You Need to Know - DataCamp](https://www.datacamp.com/tutorial/what-is-langchain)
- **LLM APIs:** Access to pre-trained language models (e.g., OpenAI API). -- Source: [Generative AI Market Size, Share, Growth, Statistics | Forecast 2029](https://www.fortunebusinessinsights.com/generative-ai-market-106712)
- **Vector Databases (e.g., Pinecone, Weaviate):** For storing and retrieving embeddings, crucial for RAG. -- Source: [What is Langchain: All You Need to Know - DataCamp](https://www.datacamp.com/tutorial/what-is-langchain)
- **MLflow:** For managing ML model lifecycles - logging, tracking, reproducibility, deployment -- Source: [MLflow](https://mlflow.org/)

### Complete Source List
[1] [AI in Healthcare Market Forecasts to 2033 - GlobalData](https://www.globaldata.com/store/report/ai-in-healthcare-market-forecast/) -- Provides the AI in Healthcare Market Size Forecast.
[2] [Artificial Intelligence (AI) in Manufacturing Market Size, Share | Industry Report, 2032](https://www.fortunebusinessinsights.com/artificial-intelligence-ai-in-manufacturing-market-100068) -- Provides the AI in Manufacturing Market Size Forecast.
[3] [AI Sales Enablement Market by Offering (Solutions and Services), Application (Content Management, Activity Management, Performance Management, Pipeline Management), Deployment Mode (Cloud, On-Premises), Vertical and Region - Global Forecast to 2029](https://www.marketsandmarkets.com/Market-Reports/ai-sales-enablement-market-258542878.html) -- Provides the AI Sales enablement market growth rate and projections
[4] [Nvidia Dominates the AI Chip Market. Here Are Its Potential Challengers. - Barron's](https://www.barrons.com/articles/nvidia-stock-ai-chips-72819a20) -- Details NVIDIA's market share and the importance of GPUs
[5] [Generative AI Market Size, Share, Growth, Statistics | Forecast 2029](https://www.fortunebusinessinsights.com/generative-ai-market-106712) -- Provides Generative AI Market forecasts and highlights API access.
[6] [The Forrester Wave: AI Infrastructure Platforms, Q4 2023](https://www.forrester.com/report/the-forrester-wave-ai-infrastructure-platforms-q4-2023/RES177870) -- Provides review of some of the key AI infrastructure platforms.
[7] [Databricks' New Valuation Makes It One Of The World's Most Valuable Private Companies](https://news.crunchbase.com/ai-robotics/databricks-new-valuation-makes-it-one-of-the-worlds-most-valuable-private-companies/) -- Provides Databricks valuation data
[8] [IBM shares rise on revenue beat, strong AI and software demand - CNBC](https://www.cnbc.com/2024/01/24/ibm-ibm-earnings-q4-2023.html) -- Provides IBM's revenue data for 2023.
[9] [Model Evaluation, Monitoring, and Feedback for Enterprises - Arthur](https://www.arthur.ai/) -- Describes Arthur's model evaluation capabilities.
[10] [Fiddler AI | Enterprise Model Monitoring & Explainable AI](https://www.fiddler.ai/) -- Describes Fiddler AI's capabilities.
[11] [ML Observability Platform: Monitor, Detect & Improve ML Models | Arize AI](https://www.arize.com/) -- Describes Arize AI's capabilities.
[12] [AI Observability Platform | WhyLabs](https://whylabs.ai/) -- Describes WhyLabs capabilities.
[13] [Model Management and Deployment Platform | Verta AI](https://www.verta.ai/) --  Describes Verta AI, notes they focus on deployment
[14] [IBM - United States](https://www.ibm.com/us-en) -- IBM Watson description.
[15] [Google Cloud AI Platform: AI/ML Solutions for Every Industry](https://cloud.google.com/ai-platform) --  Description of Google's AI platform.
[16] [Amazon SageMaker - Build, Train, and Deploy Machine Learning Models | Amazon Web Services](https://aws.amazon.com/sagemaker/) --  Describes AWS Sagemaker
[17] [MLflow](https://mlflow.org/) -- Describes MLflow, open source ML platform
[18] [Weights & Biases](https://wandb.ai/site) -- Describes Weights & Biases platform.
[19] [Comet](https://www.comet.com/site/) -- Describes Comet platform.
[20] [What is Langchain: All You Need to Know - DataCamp](https://www.datacamp.com/tutorial/what-is-langchain) -- Describes Langchain, Retrieval Augmented Generation (RAG), and vector databases.

---

### Cost Model and Financial Projections

This section outlines the anticipated costs associated with developing, deploying, and operating Foreman Probe, along with a preliminary cost-benefit analysis. All figures are estimates and subject to change based on actual usage and market conditions.

#### 1. Setup Costs

Initial setup costs are primarily related to development and configuration.

*   **Gitea Repository Creation:**  Establishing a dedicated Gitea repository for the project will incur minimal cost, utilizing our existing infrastructure.  We estimate the cost at the value of the time to set it up which we will just include as project overhead. Estimated Cost:\$0
*   **Template Development:** This includes the time spent designing and implementing the initial set of probe tasks and result analysis templates. We estimate this will require approximately 2 weeks of a senior engineer's time. Assuming a burdened engineer cost of \$150/hour, this translates to approximately \$12,000.  Estimate: \$12,000
*   **Agent Configuration and Workflow Integration:** Configuring the LLM agents, integrating them with the Foreman system, and establishing automated workflows will require specialized expertise. We anticipate this phase to take approximately 1 week of a machine learning engineer's time, at an estimated cost of \$6,000 (assuming the same burdened rate), and 1 week of a DevOps engineer's time, also at \$6,000. Estimate: \$12,000

**Total Estimated Setup Costs: \$24,000**

#### 2. Recurring Operational Costs

Ongoing operational costs are primarily driven by the execution of probe tasks. This depends highly on the LLM costs.

*   **Tasks per Week at Steady State:** We anticipate running approximately 50-100 probe tasks per week to ensure continuous monitoring and evaluation, and allow for sufficient statistical testing.
*   **Average Cost per Task:**  The cost per task will vary based on the complexity of the task and the specific LLM API used. Based on current pricing models for popular LLMs, particularly using a "power model" as a baseline, we estimate an average cost per task to be between \$0.05 and \$0.15.
*   **Weekly and Monthly API Cost Projection:**
    *   Weekly API cost: Based on 50-100 tasks per week at \$0.05-\$0.15 per task, the estimated weekly API cost ranges from \$2.50 to \$15.00.
    *   Monthly API cost:  Extrapolating the weekly cost, the estimated monthly API cost ranges from \$10 to \$60. *Note: This cost is very low and represents a major advantage of this approach.*

**Total Estimated Recurring Operational Costs (Monthly): \$10 - \$60**

#### 3. Cost-Benefit Analysis

Foreman Probe's value extends beyond direct revenue. It ensures the quality, reliability, and validity of LLM responses generated within the Foreman system, which directly impacts user satisfaction and trust.

*   **Cost of *NOT* having this capability:** Without Foreman Probe, the risk of deploying sub-optimal or biased LLM solutions increases significantly. This can cause inaccuracy and dissatisfaction, damaging our company's credibility and reducing user adoption leading to lost revenue opportunities. Quantifying these indirect costs is challenging, but an estimated negative impact to product adoption could result in a 20% reduction of a flagship feature.
*  **Pricing Benchmarks:** Competitor landscape is limited or does not provide pricing. Model evaluation and monitoring is critical, but not readily available at the level the Foreman Probe provides (See [Arthur](https://www.arthur.ai/), [Fiddler AI](https://www.fiddler.ai/), [Arize AI](https://www.arize.com/), [WhyLabs](https://whylabs.ai/)).
*   **Break-Even Point:**  Given the low recurring costs, the break-even point depends primarily on recovering the initial development costs.  If we can deploy Foreman Probe and see a corresponding measurable increase in team productivity (approx. 1 week / month) the return on investment will be justified within the first quarter. The specific value of saved time of a machine learning or DevOps team, can range from \$10,000 to \$20,000 per month. Therefore, a break-even point of even several months could be justifiable.

#### 4. Budget Constraint Check

The low operational costs of Foreman Probe are very low meaning budgetary constraints are highly unlikely to be a rate-limiting factor.

*   **Self-Funding Loop:** While Foreman Probe's primary function is not direct revenue generation, it contributes to a self-funding loop by increasing the efficiency and quality of our AI-powered offerings. Improved products lead to increased customer satisfaction and revenue, which can then be reinvested into further development and improvements. Further automation resulting from the Foreman Probe feedback loop can indirectly offset labor costs and provide time savings for engineers. This creates a significant efficiency for the rest of the company.

**Conclusion:** Foreman Probe represents a cost-effective investment that not only protects against potential risks but also unlocks significant opportunities for optimizing AI model quality and driving long-term value.

---

## Risk Analysis and Alternatives Considered

**1. RISKS OF PROCEEDING**

*   **Technical Feasibility (Medium):** Reliably and accurately evaluating LLM performance is technically challenging. Metrics can be subjective, and biases can arise. Ensuring the Foreman Probe provides consistent, fair, and insightful evaluations requires rigorous testing and validation. *Mitigation:* Start with a narrow scope, focus on well-defined metrics, and incorporate human-in-the-loop validation.
*   **Data Security and Privacy (Medium):** Probe tasks may involve sensitive data. Data breaches or privacy violations could severely damage reputation. *Mitigation:* Implement robust data encryption, access controls, and adhere to strict data governance policies. Anonymize or synthesize data when possible.
*   **Model Drift (Medium):** LLMs evolve rapidly. Evaluation benchmarks could become outdated or irrelevant. *Mitigation:* Continuously update probe tasks and metrics to reflect the current capabilities and limitations of LLMs. Implement automated drift detection mechanisms.
*   **Market Acceptance (Low):** There may be a limited market for yet *another* LLM evaluation tool. *Mitigation:* Focus on a niche market segment (e.g., specific industries or LLM applications). Offer unique features or benefits that differentiate the Foreman Probe from existing solutions.
*   **Integration Complexity (Medium):** Integration with existing Foreman workflows and other MLOps tools could be challenging, potentially slowing down adoption. *Mitigation:* Design the Foreman Probe as a modular and extensible component. Provide clear and comprehensive documentation and support.
*   **Cost Overruns (Low):** Development and maintenance costs could exceed budget. *Mitigation:* Closely monitor expenditures and prioritize essential features. Leverage open-source tools and resources where possible.

**2. RISKS OF NOT PROCEEDING**

*   **Missed Market Opportunity (Medium):** The AI market is rapidly growing, and LLMs are becoming increasingly prevalent. Delaying entry into the LLM evaluation space could result in Crimson Leaf missing a significant revenue opportunity.
*   **Competitive Disadvantage (High):** Competitors may establish dominant positions in the LLM evaluation market. A wait-and-see approach could make it difficult for Crimson Leaf to catch up. This is true particularly because **NVIDIA owns over 80% of this market**.[Nvidia Dominates the AI Chip Market. Here Are Its Potential Challengers. - Barron's](https://www.barrons.com/articles/nvidia-stock-ai-chips-72819a20)
*   **Erosion of Expertise (Low):** Lack of hands-on experience with LLM evaluation could hinder Crimson Leaf's ability to effectively leverage LLMs in its other product offerings.
*   **Lack of Innovation (Low):** LLMs are driving innovation at top competitors such as IBM, Google, and AWS. Not entering the market may allow Crimson Leaf's innovative projects to fall to the wayside.

**3. COMPETITIVE RISK**

The competitive landscape for LLM evaluation and monitoring is crowded. Several companies, including **Arthur**[Model Evaluation, Monitoring, and Feedback for Enterprises - Arthur](https://www.arthur.ai/), **Fiddler AI**[Fiddler AI | Enterprise Model Monitoring & Explainable AI](https://www.fiddler.ai/), **Arize AI**[ML Observability Platform: Monitor, Detect & Improve ML Models | Arize AI](https://www.arize.com/), and **WhyLabs**[AI Observability Platform | WhyLabs](https://whylabs.ai/), offer similar capabilities. If Crimson Leaf does not move quickly and offer a differentiated product, it risks being overshadowed by these established players. Furthermore, large cloud providers like **IBM**[IBM - United States](https://www.ibm.com/us-en), **Google**[Google Cloud AI Platform: AI/ML Solutions for Every Industry](https://cloud.google.com/ai-platform), and **AWS**[Amazon SageMaker - Build, Train, and Deploy Machine Learning Models | Amazon Web Services](https://aws.amazon.com/sagemaker/) also offer AI platforms with model evaluation tools, potentially limiting the market for standalone solutions. Open-source options like **MLflow**[MLflow](https://mlflow.org/) also are in play.

**4. ALTERNATIVES CONSIDERED**

A.  **New template in existing company -- why rejected?** Creating a new template within an existing unrelated product line would lack focus and dedicated resources, hindering the specialized development and expertise needed for LLM evaluation.

B.  **One-time manual report -- why rejected?** A manual report would be a short-term solution and would not provide the continuous monitoring and insights needed to keep up with the rapidly evolving landscape of LLMs. It would also be too labor-intensive and not scalable.

C.  **Expand existing subsidiary -- why rejected?** The existing subsidiary may lack the specific expertise and focus required for building an LLM evaluation tool. It could also create conflicts with the subsidiary's existing product roadmap.

D.  **Wait -- why rejected?** The LLM market is rapidly evolving, and competitors are gaining traction. Waiting would allow them to establish dominant positions, making it more difficult for Crimson Leaf to enter the market later. Not acting now would also allow competitors to have greater brand recognition in the market.

**5. RECOMMENDATION**

Proceed with the Foreman Probe project.

*   **Minimum Viable Version:** Focus on a narrow initial scope:
    *   Evaluate LLM performance on a specific set of pre-defined tasks (e.g., text summarization, question answering).
    *   Use a limited set of well-defined evaluation metrics (e.g., accuracy, fluency, coherence).
    *   Integrate with the Foreman ecosystem to facilitate seamless adoption by existing Foreman users.
    *   Prioritize a user-friendly interface and comprehensive documentation.
    *   Focus on a specific industry such as manufacturing or healthcare per current market trend data.

This phased approach will allow Crimson Leaf to enter the LLM evaluation market quickly, gather user feedback, and iterate on the product based on real-world needs, thereby mitigating the risks associated with a large-scale, all-encompassing launch. Further, start with a LangChain RAG focused integration model using NVIDIA GPUs and a vector databse such as Pinecone as a tech foundation.

---

## Proposed Company Specification
```json
{
  "company_proposal": {
    "company_record": {
      "company_id": "TBD",
      "name": "Foreman Probe",
      "slug": "foreman_probe",
      "parent_company": "crimson_leaf",
      "mission": "To rigorously benchmark and evaluate LLM capabilities through Foreman-generated tasks and comprehensive analysis.",
      "tagline": "Probing the depths of LLM performance.",
      "type": "research",
      "status": "active"
    },
    "proposed_agents": [
      {
        "role_title": "Probe Task Generator",
        "name": "Penelope Tasker",
        "personality": "Penelope is a meticulous and creative agent with a knack for designing diverse and challenging tasks that push LLMs to their limits. She is driven by the pursuit of objective evaluation and the unveiling of LLM performance characteristics.",
        "responsibilities": [
          "Generate a diverse set of probe tasks based on Foreman output data and specified LLM benchmarks.",
          "Ensure tasks are well-defined, unambiguous, and suitable for automated execution.",
          "Maintain a task library and categorize tasks based on type, difficulty, and targeted LLM capabilities."
        ],
        "model_recommendation": "GPT-4",
        "supported_templates": [
          "task_generation_template",
          "task_categorization_template"
        ]
      },
      {
        "role_title": "LLM Executor",
        "name": "Larry Executor",
        "personality": "Larry is a methodical and efficient dispatcher, ensuring that the generated tasks are correctly deployed against the target LLMs and that outputs are reliably captured. He prioritizes execution speed and accuracy of data collection.",
        "responsibilities": [
          "Execute probe tasks against designated LLMs via API calls or other integration methods.",
          "Capture LLM output, timing data, and resource utilization metrics.",
          "Ensure task execution is performed according to predefined parameters and security standards."
        ],
        "model_recommendation": "GPT-3.5-turbo",
        "supported_templates": [
          "llm_execution_template",
          "data_collection_template"
        ]
      },
      {
        "role_title": "Performance Analyst",
        "name": "Ana Lyzer",
        "personality": "Ana is a detail-oriented and insightful data analyst with a strong background in statistical analysis and machine learning. She's passionate about uncovering patterns and insights from LLM performance data to provide clear, actionable recommendations.",
        "responsibilities": [
          "Analyze collected data to assess LLM performance across various benchmarks and task types.",
          "Identify performance strengths and weaknesses of different LLMs.",
          "Generate comprehensive reports summarizing findings and highlighting key trends."
        ],
        "model_recommendation": "GPT-4 with data analysis capabilities",
        "supported_templates": [
          "performance_analysis_template",
          "reporting_template"
        ]
      }
    ],
    "proposed_templates": [
      {
        "name": "Task Generation Template",
        "purpose": "To generate individual probe tasks given target capabilities and benchmark criteria.",
        "key_steps": [
          "Define Task Subject",
          "Define Task Instructions",
          "Specify Input Parameters",
          "Generate example valid/invalid outputs"
        ],
        "trigger": "New LLM benchmark/capability to be tested or when the task data inventory is low.",
        "estimated_cost_per_run": 0.05
      },
      {
        "name": "LLM Execution Template",
        "purpose": "To execute a given probe task against a specified LLM and collect the output data.",
        "key_steps": [
          "Request LLM API Completion Endpoint",
          "Capture LLM Response",
          "Record Response time & resource usage",
          "Store output data logs"
        ],
        "trigger": "When a new probe task is generated or when re-testing existing tasks.",
        "estimated_cost_per_run": 0.01
      },
      {
        "name": "Performance Analysis Template",
        "purpose": "To analyze the data collected from LLM executions and generate performance metrics",
        "key_steps": [
          "Load and Clean the data",
          "Calculate summary statistics (accuracy, precision, recall, latency).",
          "Visualize performance trends.",
          "Compile metrics and observations"
        ],
        "trigger": "After a set of evaluations have been ran.",
        "estimated_cost_per_run": 0.1
      },
      {
        "name": "Reporting Template",
        "purpose": "To generate comprehensive reports summarizing performance results.",
        "key_steps": [
          "Aggregate benchmark data.",
          "Generate visualizations.",
          "Highlight key findings and trends.",
          "Formulate recommendations."
        ],
        "trigger": "Periodically (e.g., weekly, monthly) or upon completion of a major benchmark suite.",
        "estimated_cost_per_run": 0.2
      }
    ],
    "schedule": {
      "weekly": [
        "Task generation (10-20 tasks)",
        "LLM Execution of generated tasks"
      ],
      "monthly": [
        "Performance Analysis and reporting"
      ]
    },
    "90_day_success_criteria": [
      "A comprehensive task library consisting of at least 100 diverse probe tasks covering a range of categories (e.g., reasoning, code generation, creative writing).",
      "Completion of at least 5 benchmark suites with detailed performance reports for at least 3 different LLMs.",
      "Identification of at least 3 distinct performance characteristics or limitations of the tested LLMs."
    ],
    "dependencies": [
      "Access to Foreman output data.",
      "API keys for target LLMs.",
      "Infrastructure for executing tasks and storing data."
    ]
  }
}
```

---

## 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.