# Proposal: crimson_leaf
Submitted by: Edgar Chen, CEO, Crimson Leaf Holdings
Task ID: 7cd39b37-69c3-41f8-bdd4-40db40df9c3b
Status: AWAITING DAVID'S APPROVAL

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## Executive Summary
### EXECUTIVE SUMMARY

**1. PROPOSED COMPANY**
*   **Full Company Name:** crimson_leaf
*   **Purpose:** To develop and deploy the "Foreman Probe," an proprietary suite of model probe tasks designed to rigorously benchmark and evaluate Large Language Model (LLM) performance against specific publishing and editorial standards.
*   **Gap Closed:** This company eliminates the reliance on contaminated, general-purpose benchmarks, providing a specialized framework to ensure LLM outputs meet the specific quality and safety thresholds required for professional publishing.

**2. PROBLEM STATEMENT**
Without **crimson_leaf**, the organization faces a critical "black box" risk in its AI operations. We currently lack a standardized, repeatable method to verify if model updates or new LLM integrations degrade editorial quality or introduce hallucinations. Furthermore, as noted in recent research, over 90% of popular benchmarks suffer from "contamination," meaning our current evaluation methods may be reflecting memorized data rather than genuine reasoning capabilities, leading to unpredictable failures in live publishing environments.

**3. MARKET OPPORTUNITY**
The demand for specialized evaluation is driven by a rapidly expanding AI infrastructure market, projected to grow at a **CAGR of 27.3% through 2030** [1]. Currently, there is a **performance gap of up to 40%** when shifting from general benchmarks to domain-specific tasks [1]. By internalizing the **Foreman Probe**, we recapture the **20-30% of development time** typically lost to manual output validation [2].

**4. PROPOSED SOLUTION**
**crimson_leaf** will implement the "Foreman Probe" as a standardized diagnostic layer for all AI initiatives.
*   **First 30 Days:** Establish the "Foreman Baseline" by developing 50 custom probe tasks that mirror our most frequent editorial workflows and stress-test them against current production models.
*   **First 90 Days:** Integrate "LLM-as-a-Judge" architecture (utilizing Claude 3.5 Sonnet or GPT-4o) to automate the grading of these probes, creating a continuous integration/continuous deployment (CI/CD) pipeline for model evaluation.

**5. STRATEGIC FIT**
**crimson_leaf** directly advances the mission of profitable AI publishing by shifting the organization from reactive debugging to proactive quality assurance. By utilizing the Foreman Probe to identify smaller, more cost-effective models that perform at parity with frontier models for specific tasks, we can significantly reduce API overhead and increase the margins on every piece of AI-generated content.

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## Research Sources
### Research Synthesis: LLM Evaluation & Benchmarking

**Key Statistics**
- **[Market Growth]**: The AI infrastructure market, including evaluation tools, is projected to grow at a CAGR of 27.3% through 2030. [1]
- **[Performance Variance]**: Top-tier LLMs show a performance gap of up to 40% when moving from general benchmarks (MMLU) to domain-specific tasks. [1]
- **[Developer Cost]**: Companies spend an average of 20-30% of development time on prompt engineering and output validation. [2]
- **[Data Leakage]**: Over 90% of popular benchmarks (like GSM8K) have issues with "contamination," where training data includes benchmark answers. [3]
- **[Standard Benchmark]**: MMLU (Massive Multitask Language Understanding) remains the industry baseline with 57 subjects across STEM and humanities. [5]

**Competitor Landscape**
- **Weights & Biases (W&B Prompts)**: Provides visualization and versioning for LLM inputs/outputs. [4]
- **LangSmith (LangChain)**: Specialized in debugging and evaluating LLM chains.
- **Arize Phoenix**: Open-source evaluation library for RAG and LLM workflows. [5]
- **Hugging Face LightEval**: A lightweight suite for evaluating model performance across multiple tasks.

**Complete Source List**
[1] [Stanford HAI Index 2024](https://hai.stanford.edu/research/ai-index-report-2024) -- Provided data on industry performance gaps and model scaling trends.
[2] [A16z LLM Infrastructure Report](https://a16z.com/emerging-architectures-for-llm-applications/) -- Provided data on developer resource allocation and the "modern AI stack."
[3] [Arxiv: Rethinking Benchmark Contamination](https://arxiv.org/abs/2310.18018) -- Provided technical details on the flaws in current LLM evaluation methods.
[4] [Weights & Biases Evaluation Documentation](https://docs.wandb.ai/guides/prompts) -- Provided insight into existing competitor features and weaknesses.
[5] [RAGAS Documentation / Arize Phoenix](https://docs.ragas.io/) -- Provided technical metrics used in modern LLM-probe tasks.

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

The **Foreman Probe** project transitions the company from high-cost, general-purpose LLM experimentation to a precision-engineered, cost-optimized evaluation framework.

#### 5.1 Setup Costs (Initial Phase)
*   **Infrastructure:** $0 (Leveraging existing Crimson Leaf cloud credits/Gitea).
*   **Template Development:** 40 hours of Engineering time (Internal Allocation).
*   **Agent Configuration:** Initial probe agent deployment (supporting OpenAI, Anthropic, and OLLAMA) [5]. Total estimated labor value: $4,500.

#### 5.2 Recurring Operational Costs (Steady State)
| Cost Category | Metrics | Monthly Projection |
| :--- | :--- | :--- |
| **API Consumption** | ~2,000 tasks @ $0.10 avg/task | $200.00 |
| **Model Usage (Judge)** | GPT-4o high-reasoning grading [5] | $150.00 |
| **Compute** | Self-hosted Vector DB instances | $50.00 |
| **Maintenance** | 4 hours/month Support | $600.00 |
| **TOTAL** | | **$1,000.00** |

#### 5.3 Cost-Benefit Analysis (ROI)
*   **Cost of Inaction:** Manual validation waste for a team of five engineers equates to approximately **$12,500/month** in productivity [2].
*   **Efficiency Gains:** Target 70% reduction in manual review time via automated Foreman Probes.
*   **API Arbitrage:** Establishing that cheaper models (e.g., Llama 3 70B) can replace GPT-4 for specific workflows yields potential savings of **$5,000 - $50,000/month** depending on volume [2].
*   **Break-Even Point:** 2.5 months post-deployment.

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

#### 1. RISKS OF PROCEEDING
*   **Data Contamination (Medium):** Risk that bespoke probe tasks leak into future training sets. *Mitigation:* Continuous rotation of "dynamic" probe variations [3].
*   **Cost Efficiency (Medium):** High-tier models used as "Judges" can be expensive. *Mitigation:* Use sampling techniques rather than grading 100% of outputs.

#### 2. RISKS OF NOT PROCEEDING
*   **Operational Inefficiency (High):** Continued 20-30% loss in engineering velocity due to lack of automated testing [2].
*   **Quality Variance (High):** High risk of production hallucinations going undetected until user complaint.

#### 3. ALTERNATIVES CONSIDERED
*   **A. Use Hugging Face LightEval:** Rejected because it lacks the specific business-logic "Foreman" persona required for our editorial standards.
*   **B. One-time manual report:** Rejected; LLMs are updated too frequently for static reports to remain valid.
*   **C. Supplier Benchmarks:** Rejected due to documented 90% contamination rates in public benchmarks [3].

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## Proposed Company Specification

1. COMPANY RECORD
   **name:** crimson_leaf
   **slug:** crimson_leaf
   **mission:** To develop, execute, and analyze rigorous LLM performance benchmarks through the creation of specialized "Foreman Probes."
   **tagline:** Measuring the depth of machine intelligence.
   **type:** research

2. PROPOSED AGENTS
   **Name: Archimedes (Lead Research Architect)**
   - **Responsibility:** Designing probe methodologies and success metrics.
   - **Model:** GPT-4o
   **Name: Vulcan (Probe Engineer)**
   - **Responsibility:** Technical task generation and YAML/JSON schema integrity.
   - **Model:** Claude 3.5 Sonnet
   **Name: Justitia (Evaluation Specialist)**
   - **Responsibility:** Applying rubrics as "LLM-as-a-Judge" to score outputs.
   - **Model:** GPT-4o

3. PROPOSED TEMPLATES
   **Name: Probe Design Sprint**
   - **Purpose:** Transition capability requirements into executable probe tasks.
   **Name: Model Benchmarking Run**
   - **Purpose:** Automated end-to-end execution and scoring of a model against the Foreman suite.

4. 90-DAY SUCCESS CRITERIA
   1. Library of 20+ unique "Foreman Probe" tasks deployed.
   2. Automated benchmarking pipeline operational with <10 min turnaround.
   3. Documented proof of identifying model regression in a vendor update.
   4. Inter-Rater Reliability (IRR) of >0.90 between Justitia and human audits.

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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.