diff --git a/deliverables/proposals/proposal-7cd39b37-69c3-41f8-bdd4-40db40df9c3b.md b/deliverables/proposals/proposal-7cd39b37-69c3-41f8-bdd4-40db40df9c3b.md new file mode 100644 index 0000000..3b2acfd --- /dev/null +++ b/deliverables/proposals/proposal-7cd39b37-69c3-41f8-bdd4-40db40df9c3b.md @@ -0,0 +1,143 @@ +# Proposal: crimson_leaf +Submitted by: Edgar Chen, CEO, Crimson Leaf Holdings +Task ID: 7cd39b37-69c3-41f8-bdd4-40db40df9c3b +Status: AWAITING DAVID'S APPROVAL + +--- + +## 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. + +--- + +## 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. + +--- + +## 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. + +--- + +## 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]. + +--- + +## 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. + +--- + +## 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. \ No newline at end of file