# Proposal: Foreman Probe
Submitted by: Edgar Chen, CEO, Crimson Leaf Holdings
Task ID: 699b2914-37aa-4ccf-9c4a-eef45e67d3d3
Status: AWAITING DAVID'S APPROVAL

---

## Executive Summary
### EXECUTIVE SUMMARY

**1. PROPOSED COMPANY**

*   **Company:** Foreman Probe
*   **Purpose:** Foreman Probe will create model probe tasks generated by Foreman to benchmark and evaluate LLM capabilities,
*   **Gap:** This probes close the gap of effectively and systematically evaluating the capabilities of LLMs within the Foreman framework.

**2. PROBLEM STATEMENT**

Crimson Leaf cannot currently systematically benchmark and evaluate LLM capabilities generated by Foreman due to a lack of standardized probe tasks. This gaps their ability to objectively quantify performance improvements or regression of LLMs, understand their strengths and weaknesses across different domains, and make data-driven decisions about LLM selection and deployment for profitable AI publishing.

**3. MARKET OPPORTUNITY**

The LLM market presents a significant growth opportunity. The market size in 2024 is $26.47 billion and is projected to grow at a CAGR of 36.1% from 2024-2032 [LLM Market Size, Share, Trends, Growth, Forecast to 2032](https://www.futuremarketinsights.com/reports/large-language-model-market).The Generative AI market, in general, is expected to reach almost $1.3 trillion in the next 10 years [Generative AI: The insights you need](https://www.mckinsey.com/featured-insights/artificial-intelligence/what-is-generative-ai). Furthermore, 41% of enterprises have adopted AI [The state of AI in 2023: Generative AI's breakout year](https://www.mckinsey.com/capabilities/quantum-computing/our-insights/the-state-of-ai-in-2023), indicating a strong demand for solutions that can effectively leverage and evaluate AI models effectively.

**4. PROPOSED SOLUTION**

Foreman Probe will close the gap by providing a structured and automated system for generating and executing LLM benchmark tasks:

*   **First 30 Days:** Develop a prototype system for generating a basic set of probe tasks using Foreman, integrate with an LLM API (e.g., OpenAI), and establish a process for automating evaluation of LLM responses to these tasks.
*   **First 90 Days:** Expand the variety and complexity of probe tasks through Foreman integration. This enables the incorporation of different data types, complexity, and contextual variety. Refine the automation of benchmark execution and results analysis. Establish standardized metrics for measuring LLM performance.

**5. STRATEGIC FIT**

Foreman Probe directly advances Crimson Leaf's primary mission of profitable AI publishing by:

*   **Providing Objective LLM Evaluation:** Enabling data-driven decisions about which LLMs perform best for specific document types and tasks generated by Foreman.
*   **Optimizing Content Generation Workflows:** Identifying areas where LLMs excel or fall short within content generation.
*   **Reducing Costs:** By selecting the most efficient LLMs for each content domain of Foreman generated documents. By fine-tuning model selection for efficiency.

---

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

### Key Statistics
*   **LLM Market Size (2024):** $26.47 billion -- Source: [LLM Market Size, Share, Trends, Growth, Forecast to 2032](https://www.futuremarketinsights.com/reports/large-language-model-market)
*   **LLM Market CAGR (2024-2032):** 36.1% -- Source: [LLM Market Size, Share, Trends, Growth, Forecast to 2032](https://www.futuremarketinsights.com/reports/large-language-model-market)
*   **Generative AI Market Growth (Next 10 Years):** Expected to reach almost $1.3 trillion -- Source: [Generative AI: The insights you need](https://www.mckinsey.com/featured-insights/artificial-intelligence/what-is-generative-ai)
*   **AI Adoption in Enterprises:** 41% of enterprises have adopted AI -- Source: [The state of AI in 2023: Generative AI's breakout year](https://www.mckinsey.com/capabilities/quantum-computing/our-insights/the-state-of-ai-in-2023)
*   **Median Cost per Thousand Tokens (GPT-4):** $0.03 for 4K context, $0.06 for 32K context -- Source: [GPT-4 Pricing](https://openai.com/pricing)
*   **Maximum Output Tokens (GPT-4):** 4,096 tokens -- Source: [GPT-4 Pricing](https://openai.com/pricing)
*   **Cost to Train GPT-3:** Estimated $4.6 million in compute costs -- Source: [How Much Does it Cost to Train a Model: A Deep Dive into the $4.6M Question](https://lambdalabs.com/blog/how-much-does-it-cost-to-train-a-model)

### Competitor Landscape
*   **OpenAI:** Develops and offers access to LLMs like GPT-4. Primary product is API access. | Pricing based on token usage. | Potential weakness: cost, reliance on external infrastructure [GPT-4 Pricing](https://openai.com/pricing).
*   **Cohere:** Provides enterprise-grade LLMs and tools for building AI applications | Offers APIs for text generation, summarization, and search. | [Cohere](https://cohere.com/).
*  **AI21 Labs:** Offers Jurassic-2 models via API. | Pricing varies by model size and usage. | [AI21 Labs](https://www.ai21.com/).
*   **Google Cloud AI Platform:** Provides access to a range of AI models, including PaLM 2. |  Offers both pre-trained models and tools for custom training | [Google Cloud AI Platform](https://cloud.google.com/products/ai).
*  **John Snow Labs:** Provides specialized NLP models for healthcare and life sciences | Offers pre-trained models and tools for healthcare-specific NLP tasks | [John Snow Labs](https://www.johnsnowlabs.com/).
*   **Hugging Face:** A community and platform for sharing and using pre-trained models. | Offers a wide range of open-source models and tools | [Hugging Face](https://huggingface.co/).
*   **Mosaic ML:** (Now Databricks Mosaic AI): Provides tools for training and deploying custom LLMs. | Focuses on enabling enterprises to build their own models | [MLOps Stack](https://www.databricks.com/solutions/mlops-stack).
*   **Amazon SageMaker JumpStart:** A machine learning hub with pre-trained models, algorithms, and example notebooks. | Provides access to a variety of open-source and commercial models | [Amazon SageMaker JumpStart](https://aws.amazon.com/sagemaker/jumpstart/).
*   **NVIDIA NeMo:** A framework for building and customizing LLMs. | Designed for enterprise use cases | [NVIDIA NeMo](https://developer.nvidia.com/blog/nvidia-nemo-framework-for-building-custom-llms/).

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

### Technology Findings
*   **Python:** Programming language commonly used for interacting with LLM APIs -- [The state of AI in 2023: Generative AI's breakout year](https://www.mckinsey.com/capabilities/quantum-computing/our-insights/the-state-of-ai-in-2023).
*   **LLM APIs:** Access pre-trained models for various tasks -- [GPT-4 Pricing](https://openai.com/pricing)
*   **LangChain:** Framework for working with LLMs and building applications -- [LangChain AI](https://www.langchain.com/).
*   **TensorFlow/PyTorch:** Frameworks for model development and training -- [MLOps Stack](https://www.databricks.com/solutions/mlops-stack).
*   **MLOps:** Practices for managing machine learning models in production -- [MLOps Stack](https://www.databricks.com/solutions/mlops-stack).
*  **CUDA:** NVIDIA's parallel computing platform and API model -- [NVIDIA NeMo](https://developer.nvidia.com/blog/nvidia-nemo-framework-for-building-custom-llms/).

### Complete Source List
[1] [LLM Market Size, Share, Trends, Growth, Forecast to 2032](https://www.futuremarketinsights.com/reports/large-language-model-market) -- Provided data on LLM Market Size and CAGR.
[2] [Generative AI: The insights you need](https://www.mckinsey.com/featured-insights/artificial-intelligence/what-is-generative-ai) -- Provided data on the projected growth of the generative AI market.
[3] [The state of AI in 2023: Generative AI's breakout year](https://www.mckinsey.com/capabilities/quantum-computing/our-insights/the-state-of-ai-in-2023) -- Provided enterprise AI adoption rates and common programming languages.
[4] [GPT-4 Pricing](https://openai.com/pricing) -- Provided GPT-4 pricing details and token limits.
[5] [How Much Does it Cost to Train a Model: A Deep Dive into the $4.6M Question](https://lambdalabs.com/blog/how-much-does-it-cost-to-train-a-model) -- Provided cost estimates for training GPT-3.
[6] [GPT-3: Pricing, API & Alternatives in 2024](https://www.searchenginejournal.com/gpt-3-pricing/472874/) -- Provided market overview and competitive pricing.
[7] [Large Language Models](https://bdtechtalks.com/2023/01/18/what-is-generative-ai/) -- Provided overview of LLMs.
[8] [Cohere](https://cohere.com/) -- Provided information on Cohere's LLMs and APIs.
[9] [AI21 Labs](https://www.ai21.com/) -- Provided information on AI21 Labs Jurassic-2 models.
[10] [Google Cloud AI Platform](https://cloud.google.com/products/ai) -- Provided information on Google Cloud AI Platform and PaLM 2.
[11] [John Snow Labs](https://www.johnsnowlabs.com/) -- Provided information on John Snow Labs' NLP models for healthcare.
[12] [Hugging Face](https://huggingface.co/) -- Provided information on the Hugging Face platform.
[13] [MLOps Stack](https://www.databricks.com/solutions/mlops-stack) -- Provided information on MLOps and tools for model training and deployment.
[14] [Amazon SageMaker JumpStart](https://aws.amazon.com/sagemaker/jumpstart/) -- Provided information on Amazon SageMaker JumpStart.
[15] [NVIDIA NeMo](https://developer.nvidia.com/blog/nvidia-nemo-framework-for-building-custom-llms/) -- Provided information on NVIDIA NeMo.
[16] [LangChain AI](https://www.langchain.com/) -- Provided information on LangChain.

---

## Cost Model and Financial Projections
Okay, crafting the Cost Model and Financial Projections section based on the provided research synthesis and your outline.

### Cost Model and Financial Projections

This section outlines the costs associated with the "Foreman Probe" project, along with a cost-benefit analysis and budget constraint check.  All costs are estimated and may vary based on actual usage and vendor pricing.

**1. Setup Costs:**

*   **Gitea Repository Creation:** This is a one-time expense and can be considered negligible, as it involves creating a repository within our existing Gitea infrastructure and does not incur direct API costs. Cost: $0
*   **Template Development Estimate:** This involves the initial development of task templates for benchmarking LLM capabilities.  This will likely require substantial senior engineering time in the first month. Estimate: $20,000.00 (Based on senior AI engineer at ~$200/hr for 100 hours).
*   **Agent Configuration:** This assumes use of LangChain and also open-source frameworks. Estimate: $10,000.

**Subtotal Setup Costs: $30,000**

**2. Recurring Operational Costs:**

*   **Tasks per Week at Steady State:**  The number of tasks will vary depending on the specific benchmarking requirements and the rate at which new LLMs and capabilities emerge.  Let us assume a steady state of 100 tasks per week for projections.
*   **Average Cost per Task:**  Based on GPT-4 pricing ([GPT-4 Pricing](https://openai.com/pricing)), the median cost per 1000 tokens is $0.03 for 4K context and $0.06 for 32K context. Assuming an average task requires processing 2,000 tokens (input + output), the average cost per task is estimated at $0.06.  However, given the complexity and context-heavy nature of LLM benchmarking, a higher estimate of $0.10-$0.20 per task would be more realistic (depending on the context window needed for each probe). The cost may be lower using open-source stacks and lower if we use other less costly closed-source models, but we will estimate at $.15 for this analysis.

**Subtotal Average Cost per Task: $0.15**

*   **Weekly API Cost Projection:**
    *   100 tasks/week * $0.15/task = $15.00/week
*   **Monthly API Cost Projection:**
    *   $15.00/week * 4 weeks/month = $60.00/month

**3. Cost-Benefit Analysis:**

*   **Cost of *Not* Having This Company/Capability (Foreman Probe):**
    *   **Missed Opportunity to Lead in LLM Benchmarking:** Without a structured approach to benchmarking, we risk being unable to objectively assess and compare different LLMs. This includes a loss of potential revenue to the company.
    *   **Suboptimal Model Selection:** Choosing the wrong LLM for specific tasks can lead to reduced performance, increased costs, and potentially flawed outputs.
    *   **Lack of Data-Driven Insights:** We would lack quantifiable data to guide LLM selection, optimization, and fine-tuning efforts.
    *   **Competitive Disadvantage:** Other companies are investing heavily in LLM capabilities and benchmarking. Without Foreman Probe, we may fall behind and risk losing market share in the age of accelerating AI adoption, with 41% of enterprises adopting AI [The state of AI in 2023: Generative AI's breakout year](https://www.mckinsey.com/capabilities/quantum-computing/our-insights/the-state-of-ai-in-2023). McKinsey estimates the generative AI market will reach almost $1.3 trillion in the next 10 years. These are markets that can only be properly exploited if the company has strong, data-driven LLM usage and evaluation.
*   **Break-Even Point:** The break-even point is difficult to calculate precisely without specific revenue projections tied to LLM-enhanced product features. It's based on improved throughput of existing employees via AI tooling. The Foreman Probe accelerates safe deployment of such features. Further detail must be collected on this to build an appropriate model. However, the low operational costs ($60/month) and the benefits of better LLM selection suggest a relatively rapid break-even, potentially within 6-12 months of Foreman Probe development.
*   **Pricing Benchmarks:**
    *   **GPT-4 Pricing:** $0.03 per 1,000 tokens (4K context), $0.06 per 1,000 tokens (32K context) ([GPT-4 Pricing](https://openai.com/pricing))

**4. Budget Constraint Check:**

*   **Self-Funding Loop:** Given the initial setup costs ($30,000), the project is not initially self-funding. However, the low monthly operational costs ($60/month) mean that even within the first year, this system delivers value to the company at a low cost. Further analysis is needed to determine how the data generated will be used to increase revenue and build a model for self-funding.

---

## Risk Analysis and Alternatives Considered
Okay, here's the Risk Analysis and Alternatives Considered section for the Foreman Probe project proposal, incorporating the research synthesis you provided:

### RISK ANALYSIS AND ALTERNATIVES CONSIDERED

1.  **RISKS OF PROCEEDING**

*   **Technical Feasibility (Medium):** Successfully crafting a test suite that accurately benchmarks LLM capabilities requires careful design and may require multiple iterations and adjustments. This includes defining appropriate metrics and creating diverse and challenging test cases. Relying on LLM APIs like [GPT-4 Pricing](https://openai.com/pricing) also introduces the risk of the APIs changing.
*   **Cost Overruns (Medium):** Utilizing LLM APIs can be expensive, especially with large test suites and frequent evaluations. Token costs can quickly accumulate [GPT-4 Pricing](https://openai.com/pricing). Also, the initial cost to train LLMs can be very high [How Much Does it Cost to Train a Model: A Deep Dive into the $4.6M Question](https://lambdalabs.com/blog/how-much-does-it-cost-to-train-a-model).
*   **Data Security and Privacy (Medium):** If the Foreman probes involve any sensitive data, ensuring data security and compliance with privacy regulations will be crucial.
*   **Market Adoption (Low):** Given the growing need for LLM evaluation tools, there is a high likelyhood of adoption, but adoption of the specific *implementation* is not guaranteed.
*   **Maintenance and Updates (Medium):** LLMs are constantly evolving. The Foreman Probe will require ongoing maintenance and updates to remain relevant and accurate, including the potential for new testing paradigms.

2.  **RISKS OF NOT PROCEEDING**

*   **Missed Market Opportunity (High):** The LLM market is growing rapidly [LLM Market Size, Share, Trends, Growth, Forecast to 2032](https://www.futuremarketinsights.com/reports/large-language-model-market). Delaying entry could mean losing ground to competitors.
*   **Lack of LLM Performance Insight:** Without a structured evaluation tool, projects will lack a clear understanding of the capabilities and limitations of different LLMs, potentially leading to suboptimal model selection.
*   **Slower Innovation (Medium):** Without systematic benchmarking, developing and improving LLM workflows inside our company will be more difficult.
*   **Wasted Resources (Medium):** Ad-hoc evaluations will continue, but without the efficiency and consistency of the Foreman Probe.

3.  **COMPETITIVE RISK**

Various companies offer LLMs and associated services, creating competition:

*   **Established LLM Providers:** Companies like OpenAI [GPT-4 Pricing](https://openai.com/pricing), Cohere [Cohere](https://cohere.com/), and AI21 Labs [AI21 Labs](https://www.ai21.com/) directly compete by offering their LLMs for various tasks. If Foreman Probe evaluates these LLMs poorly, it could negatively affect the relationships.
*   **Cloud Platform Providers:** Google Cloud AI Platform [Google Cloud AI Platform](https://cloud.google.com/products/ai) and Amazon SageMaker JumpStart [Amazon SageMaker JumpStart](https://aws.amazon.com/sagemaker/jumpstart/) provide access to LLMs along with infrastructure and tools.
*   **Open-Source and Community Platforms:** Hugging Face [Hugging Face](https://huggingface.co/) offers many open-source models. If Foreman Probe does not offer substantial advantages, the community might view it as redundant.
*   **Specialized NLP Providers:** John Snow Labs [John Snow Labs](https://www.johnsnowlabs.com/) focuses on specific domains like healthcare. If Foreman Probe doesn't address specific needs or outperform these providers, it could limit its market.

4.  **ALTERNATIVES CONSIDERED**

*   **A. New Template in Existing Company (Rejected):** Creating a simple evaluation template within an existing system wouldn't provide the depth, flexibility, or scalability required to thoroughly benchmark LLMs across a range of capabilities. A template would fail to address the evolution of LLMs.
*   **B. One-Time Manual Report (Rejected):** A single manual evaluation would quickly become outdated given the rapid advancements in LLM technology. It would lack the consistency and objectivity needed for informed decision-making.
*   **C. Expand Existing Subsidiary (Rejected):** While leveraging an existing subsidiary might offer some synergies, it could also introduce bureaucratic overhead and limit the project's agility and focus. Existing subsidiaries might lack the specific expertise required for LLM benchmarking.
*   **D. Wait (Rejected):** Delaying entry into the LLM evaluation market risks losing ground to competitors and missing the opportunity to establish a leadership position. Waiting also means forgoing the benefits of improved LLM insights and faster innovation. The LLM market CAGR of 36.1% [LLM Market Size, Share, Trends, Growth, Forecast to 2032](https://www.futuremarketinsights.com/reports/large-language-model-market) makes waiting undesirable.

5.  **RECOMMENDATION**

Proceed with the Foreman Probe project.

**Minimum Viable Version:**

The MVP should focus on:

*   **A core set of benchmark tasks:** Start with key capabilities (e.g., text generation, summarization, question answering) and expand over time.
*   **Integration with a limited set of LLM APIs:** Focus on the most popular and relevant APIs (e.g., GPT-4, Cohere).
*   **A basic reporting dashboard:** Provide key performance metrics and allow for comparison across models.

This approach will allow us to validate the concept, gather user feedback, and iterate towards a more comprehensive and impactful solution.

---

## Proposed Company Specification
Okay, I understand. I'll draft the proposed company specification for "Foreman Probe" as a subsidiary of "crimson_leaf".

**1. COMPANY RECORD**

*   company_id: TBD (Assigned by David)
*   name: Foreman Probe
*   slug: foreman_probe
*   parent_company: crimson_leaf
*   mission: To objectively benchmark and evaluate Large Language Model (LLM) capabilities using Foreman-derived probe tasks.
*   tagline: "Probing LLMs for Truth."
*   type: Research
*   status: active

**2. PROPOSED AGENTS**

*   **Role Title:** Probe Architect
    *   **Name:** Anya Sharma
    *   **Personality:** Meticulous and detail-oriented, Anya thrives on structure and precise execution. She possesses a deep understanding of prompt engineering and evaluation methodologies. Her calm demeanor makes her excellent at handling complex and evolving requirements.
    *   **Responsibilities:** Designs and refines probe tasks based on Foreman data, ensuring they accurately assess targeted LLM capabilities. Develops evaluation metrics and scoring rubrics for each probe. Maintains a repository of probe tasks and evaluation results.
    *   **Model Recommendation:** GPT-4 (for advanced reasoning and generation of complex test cases), Claude-3 Opus (for understanding nuances and spotting biases).
    *   **Supported Templates:**  "Probe Task Design", "Evaluation Metric Definition", "Results Analysis Report".

*   **Role Title:** Automation Engineer
    *   **Name:** Kenji Tanaka
    *   **Personality:** Kenji is a pragmatic and efficient problem-solver. He enjoys automating processes and building robust systems.  He is dedicated to streamlining workflows and catching any bugs.
    *   **Responsibilities:** Automates the probe execution and data collection process. Develops scripts and tools to run probes against various LLMs. Manages the infrastructure required for probe execution and data storage.
    *   **Model Recommendation:** GPT-4 (for code generation and system integration)
    *   **Supported Templates:**  "Probe Execution Script", "Data Collection Workflow", "API Integration".

*   **Role Title:** Validation Analyst
    *   **Name:** Ingrid Olsen.
    *   **Personality:** Ingrid is detail-oriented, curious and highly logical in her approach to problem solving. She brings a skeptical eye to the company, to ensure no bias is present.
    *   **Responsibilities:** Responsible for checking that all probes are running successfully and producing accurate data.
    *   **Model Recommendation:** GPT-4 (for code generation and system integration)
    *   **Supported Templates:**  "Probe Execution Script", "Data Collection Workflow", "API Integration".

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

*   **Name:** Probe Task Design
    *   **Purpose:** To define a specific probe task to evaluate an LLM's capability (e.g., reasoning, factual recall, code generation).
    *   **Key Steps:** 1. Define the targeted LLM capability. 2. Formulate the probe task prompt and input data. 3. Establish expected outputs and acceptance criteria. 4. Document the rationale for the probe's design.
    *   **Trigger:** When a new LLM capability or vulnerability needs to be assessed or when Foreman provides new task cases.
    *   **Estimated Cost per Run:** $0.50 (depending on the complexity and length of the task).

*   **Name:** Evaluation Metric Definition
    *   **Purpose:** To establish a scoring system and metrics for objectively evaluating the performance of an LLM on a specific probe task.
    *   **Key Steps:** 1. Define clear and measurable metrics (e.g., accuracy, precision, recall, fluency). 2. Develop a scoring rubric that assigns points based on performance. 3. Establish guidelines for human raters (if manual evaluation is required). 4. Validate the reliability and consistency of the metrics.
    *   **Trigger:** After "Probe Task Design" is complete but before probe execution.
    *   **Estimated Cost per Run:** $0.25 (primarily human review time for rubric validation in early stages).

*   **Name:** Probe Execution Script
    *   **Purpose:** To automate the process of running probe tasks against LLMs and collecting their outputs.
    *   **Key Steps:** 1. Define the API endpoint or interface for the target LLM. 2. Develop a script to send probe tasks to the LLM. 3. Capture the response from the LLM. 4. Store the input, output, and relevant metadata.
    *   **Trigger:** After "Evaluation Metric Definition" is complete.
    *   **Estimated Cost per Run:** $0.10 (primarily infrastructure costs).

*   **Name:** Data Collection Workflow
    *   **Purpose:** To collect probe task, execution data, and output for long-term preservation and future analysis.
    *   **Key Steps:** 1. Upload the appropriate probe from the foreman. 2. Select the correct format and LLM target. 3. Run the script and generate the results. 4. Upload probe to long-term data store.
    *   **Trigger:** After "Probe task definition" is complete.
    *   **Estimated Cost per Run:** $0.10 (primarily infrastructure costs).

*   **Name:** Results Analysis Report
    *   **Purpose:** To summarize and interpret the results of the probe executions, providing insights into the strengths and weaknesses of LLMs.
    *   **Key Steps:** 1. Aggregate data from multiple probe executions. 2. Calculate performance metrics (e.g., average score, error rate). 3. Visualize the results using charts and graphs. 4. Draw conclusions about the LLM's capabilities and limitations.
    *   **Trigger:** After sufficient probe execution data has been collected.
    *   **Estimated Cost per Run:** $0.75 (including data analysis and report generation).

**4. SCHEDULE**

*   **Weekly:** Probe Architect designs and refines new probe tasks based on Foreman data. Automation Engineer automates recently submitted probes into the data collection workflow.
*   **Bi-weekly:** Probe executions are run in batches against targeted LLMs.
*   **Monthly:** Results Analysis Report is generated summarizing the findings from the previous month.
*   **Quarterly:** Review and refine the probe task design and evaluation metrics based on the findings. Review overall data quality with Ingrid Olsen, validate and upload new probes.

**5. 90-DAY SUCCESS CRITERIA**

*   Successfully designed and implemented at least 20 unique Foreman-derived probe tasks.
*   Generated data for evaluation report of LLM accuracy and bias for those 20 probes.
*   Automated the execution of probe tasks and data collection, achieving at least 95% system uptime.
*    Produced at least 2 comprehensive Results Analysis Reports highlighting the performance of several LLMs on Foreman-derived tasks.
*   Demonstrated a decrease in data extraction and cleaning processing time by 20% by end of the project.

**6. DEPENDENCIES**

*   Access to Foreman data and API.
*   Access to LLM APIs (e.g., OpenAI, Anthropic, Google).
*   Infrastructure for running probe executions and storing data (e.g., cloud computing resources, databases).
*   Agreement on security protocols and access controls for Foreman data.
*   Clear definition of evaluation criteria and scoring rubrics.

---

## 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.