# Proposal: Foreman Probe
Submitted by: Edgar Chen, CEO, Crimson Leaf Holdings
Task ID: 4c29405d-bb5f-42f8-a1a8-ec69d5b990ac
Status: AWAITING DAVID'S APPROVAL

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## Executive Summary
### EXECUTIVE SUMMARY

**1. PROPOSED COMPANY**
- **Name:** Foreman Probe
- **Purpose:** To create a standardized suite of internal tasks for systematically benchmarking and evaluating the capabilities of Large Language Models.
- **Gap:** Closes the gap in our ability to objectively measure, compare, and track LLM performance for our specific use cases.

**2. PROBLEM STATEMENT**
Without a dedicated and standardized evaluation framework, Crimson Leaf cannot reliably quantify the performance differences between various LLMs or even different versions of the same model. We are forced to rely on subjective assessments, public benchmarks that may not align with our business needs, and anecdotal evidence. This prevents us from making data-driven decisions on model selection, fine-tuning investments, and resource allocation, ultimately risking the deployment of sub-optimal or unnecessarily expensive models in our products.

**3. MARKET OPPORTUNITY**
No quantitative market data was found in the provided research. However, a structural analysis indicates a significant internal opportunity. As the LLM landscape becomes more competitive and commoditized, the key differentiator shifts from mere access to models to the intelligent application and optimization of them. By developing a proprietary benchmarking suite, Crimson Leaf creates a strategic asset. This internal capability allows us to build a deep, defensible understanding of which models perform best on the tasks that matter to our AI publishing business, granting us a competitive edge in quality and cost-efficiency that external benchmarks cannot provide.

**4. PROPOSED SOLUTION**
Foreman Probe will establish a rigorous, internal system for evaluating LLMs on dimensions critical to Crimson Leaf's success. This closes the current capability gap by replacing subjective analysis with objective, repeatable measurement.

- **First 30 Days:** Define the initial capability verticals (e.g., instruction following, creative writing, factual recall, safety). Develop and validate the first 20 "probes" (evaluation tasks) within this framework. Run initial benchmarks on our current production models to establish a performance baseline.
- **First 90 Days:** Expand the library to 100+ probes covering more nuanced capabilities. Automate the evaluation pipeline to allow for rapid testing of new or updated models. Create a v1 dashboard to visualize comparison data and performance trends, providing actionable insights to product and engineering teams.

**5. STRATEGIC FIT**
Foreman Probe directly advances our primary mission of profitable AI publishing.
- **Profitability:** It enables us to identify the most cost-effective model for each specific task, reducing operational expenses and improving margins.
- **AI Publishing:** By ensuring we deploy the highest-performing models for our needs, we improve the quality and reliability of our AI-generated content and products. This data-driven quality control strengthens our brand and our ability to "publish" state-of-the-art AI experiences efficiently and with confidence.

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

### Key Statistics
- No data found in provided research materials.

### Competitor Landscape
- No competitors or existing players were identified in the provided research.

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

### Technology Findings
- No specific tools, APIs, or technical requirements were identified in the provided research.

### Complete Source List
No sources were provided in the research queries.

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

This analysis is based on estimates, as the provided research synthesis yielded no specific quantitative data, pricing benchmarks, or case studies. The projections are derived from the project description and general operational assumptions for a project of this nature.

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### 1. SETUP COSTS
Setup costs are primarily one-time investments of engineering effort. Direct API or capital expenditure is minimal.

*   **Gitea Repo Creation:** A one-time administrative task with zero direct API cost.
*   **Template Development Estimate:** The initial creation of probe task templates will require a one-time allocation of engineering resources. This is a human-effort cost to design, write, and test the initial set of benchmarking tasks.
*   **Agent Configuration:** A one-time engineering effort is required to configure the Foreman agent(s) to correctly execute the probe tasks, parse results, and store data.

### 2. RECURRING OPERATIONAL COSTS
Recurring costs are driven exclusively by LLM API usage.

*   **Tasks Per Week (Steady State):** We project an initial operational tempo of **20 probe tasks per week**. This volume is sufficient to generate a consistent stream of performance data without incurring significant costs.
*   **Average Cost Per Task:** Based on typical usage of powerful models for moderately complex tasks, we estimate a cost-per-task in the range of **$0.05 - $0.15**.
*   **Weekly & Monthly API Cost Projection:**
    *   **Weekly Cost:** 20 tasks/week * ($0.05 to $0.15)/task = **$1.00 to $3.00 per week**.
    *   **Monthly Cost:** ~$4.30 to $13.00 per month.

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## Risk Analysis and Alternatives Considered
1.  **RISKS OF PROCEEDING**
[Content for this section was not provided in the source material.]

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## Proposed Company Specification
### PROPOSED COMPANY SPECIFICATION

**1. COMPANY RECORD**
*   **company_id**: TBD
*   **name**: Foreman Probe
*   **slug**: foreman_probe
*   **parent_company**: crimson_leaf
*   **mission**: To systematically benchmark and evaluate Large Language Model capabilities through a standardized set of Foreman-designed probe tasks.
*   **tagline**: Quantifying AI capabilities, one probe at a time.
*   **type**: research
*   **status**: active

**2. PROPOSED AGENTS**

*   **AGENT 1**
    *   **role**: Probe Operator
    *   **name**: `prober`
    *   **personality**: Meticulous, systematic, and objective. It follows instructions with absolute precision, logging every detail of its execution without deviation. It is purely functional, concerned only with the accurate execution of the probe and the faithful recording of the outcome.
    *   **responsibilities**: Execute model probe tasks against specified LLMs, collect the raw outputs, format the results according to a standard schema, and log all execution metadata (e.g., model version, timestamp, parameters).
    *   **model_recommendation**: claude-3-haiku-20240307
    *   **supported_templates**: `run_probe`

*   **AGENT 2**
    *   **role**: Probe Analyst
    *   **name**: `evaluator`
    *   **personality**: Analytical, critical, and discerning. It compares model outputs against established rubrics and ground truths with an impartial eye. It identifies patterns, scores performance, and synthesizes findings into concise, data-driven summaries.
    *   **responsibilities**: Receive formatted probe results, apply a scoring rubric to evaluate the model's output, calculate performance metrics, and generate a summary report for each probe run or suite.
    *   **model_recommendation**: claude-3-opus-20240229
    *   **supported_templates**: `score_probe_results`, `generate_summary_report`

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

*   **TEMPLATE 1**
    *   **name**: `run_probe`
    *   **purpose**: To execute a single, defined probe task against a target LLM.
    *   **key_steps**:
        1.  Receive a probe definition (prompt, parameters, target model) and an execution ID.
        2.  Construct the final prompt for the target model.
        3.  Execute the API call to the target LLM.
        4.  Capture the raw output, latency, and any error messages.
        5.  Package the raw output with all execution metadata into a standardized result artifact.
    *   **

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## 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.