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+# Proposal: Foreman Probe
+Submitted by: Edgar Chen, CEO, Crimson Leaf Holdings
+Task ID: 91e70062-b06f-4d8a-8053-9e6fe4779955
+Status: AWAITING DAVID'S APPROVAL
+
+---
+
+## Executive Summary
+### EXECUTIVE SUMMARY
+
+1. **PROPOSED COMPANY**  
+   - **Full name and slug**: Foreman Probe (foreman-probe)  
+   - **One-sentence purpose**: Foreman Probe creates dynamic model probe tasks generated by the Foreman AI to benchmark and evaluate LLM capabilities across reasoning, agentic tasks, and publishing-specific workflows.  
+   - **Which gap it closes**: Fills the void in automated, creative task generation for adaptive LLM evals, surpassing static competitors like Hugging Face Leaderboard and enabling Crimson Leaf's custom probes.
+
+2. **PROBLEM STATEMENT**  
+   Crimson Leaf cannot today autonomously generate scalable, Foreman-driven probe tasks for dynamic LLM benchmarking--such as agentic reasoning on publishing workflows (e.g., content ideation, fact-checking chains)--forcing reliance on costly third-party static tools (Scale AI at $0.01-0.10/inference) or manual processes, resulting in 30-50% higher deployment risks, slower model iteration (vs. 4x faster with benchmarks), and inability to validate LLMs for profitable AI publishing at enterprise scale.
+
+3. **MARKET OPPORTUNITY**  
+   The global AI testing market was $1.2 billion in 2023, projected to reach $5.8 billion by 2030 at 25% CAGR [Global AI Testing Market Size](https://www.grandviewresearch.com/industry-analysis/ai-testing-market-report). 68% of AI companies use automated benchmarks, up from 42% in 2022 [LLM Evaluation Tools Adoption](https://www.stateof.ai/). Companies using LLM benchmarks report 30-50% reduction in deployment risks [Benchmarking Cost Savings](https://www.gartner.com/en/information-technology/insights/artificial-intelligence/ai-evaluation). Hugging Face Open LLM Leaderboard runs 10,000+ model evals monthly with 2.5M inferences [Open LLM Leaderboard Evaluations](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard). Chatbot Arena has 1.5 million monthly users [Chatbot Arena User Base](https://lmsys.org/blog/2024-annual-review/). Fortune 500 firms spend average $500K/year on eval tools [Enterprise LLM Benchmark Spend](https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-in-2024-survey). Adaptive eval platforms grew 40% YoY [Dynamic Benchmark Growth](https://www.marketsandmarkets.com/Market-Reports/ai-benchmarking-market-247193728.html). Benchmarks enable 4x faster model iteration cycles [ROI from Benchmarks](https://scale.com/blog/llm-benchmarks-impact).
+
+4. **PROPOSED SOLUTION**  
+   Foreman Probe closes the gap by deploying Foreman AI as a task generator for novel, adaptive probes (e.g., GAIA-style agentic evals integrated with EleutherAI LM Harness and Prometheus), automating beyond static datasets like MMLU for Crimson Leaf's publishing needs.  
+   - **First 30 days**: Build MVP task generator (Python 3.10+ on A100 GPUs), integrate with Hugging Face Inference API, generate 100+ Foreman probes, run evals on 10 open LLMs, and deploy internal dashboard.  
+   - **First 90 days**: Launch full API/platform with RLHF tracing (TRL library), custom publishing benchmarks (e.g., hallucination probes), pilot integrations with Crimson Leaf pipelines, achieve 1K inferences/day, and secure EU AI Act-compliant transparency logs.
+
+5. **STRATEGIC FIT**  
+   Foreman Probe advances Crimson Leaf's primary mission of profitable AI publishing by enabling rigorous LLM evals that accelerate content model iteration (4x faster cycles), reduce hallucination risks (25% as in Anthropic/Claude), secure enterprise contracts (40% faster GTM like Cohere), and monetize premium benchmarking-as-a-service, directly boosting ARR through superior, validated AI-generated publishing outputs.
+
+---
+
+## Research Sources
+(Paste the "Complete Source List" from the research synthesis)
+## Research Synthesis
+
+### Key Statistics
+- [Global AI Testing Market Size]: $1.2 billion in 2023, projected to reach $5.8 billion by 2030 at 25% CAGR -- Source: [Grand View Research: AI Testing Market Report](https://www.grandviewresearch.com/industry-analysis/ai-testing-market-report)
+- [LLM Evaluation Tools Adoption]: 68% of AI companies use automated benchmarks, up from 42% in 2022 -- Source: [State of AI Report 2024 by Nathan Benaich](https://www.stateof.ai/)
+- [Benchmarking Cost Savings]: Companies using LLM benchmarks report 30-50% reduction in deployment risks -- Source: [Gartner AI Evaluation Insights 2024](https://www.gartner.com/en/information-technology/insights/artificial-intelligence/ai-evaluation)
+- [Open LLM Leaderboard Evaluations]: Over 10,000 models evaluated monthly, with 2.5M inferences -- Source: [Hugging Face Open LLM Leaderboard Stats](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
+- [
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