4.8 KiB
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
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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.
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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. -
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. 68% of AI companies use automated benchmarks, up from 42% in 2022 LLM Evaluation Tools Adoption. Companies using LLM benchmarks report 30-50% reduction in deployment risks Benchmarking Cost Savings. Hugging Face Open LLM Leaderboard runs 10,000+ model evals monthly with 2.5M inferences Open LLM Leaderboard Evaluations. Chatbot Arena has 1.5 million monthly users Chatbot Arena User Base. Fortune 500 firms spend average $500K/year on eval tools Enterprise LLM Benchmark Spend. Adaptive eval platforms grew 40% YoY Dynamic Benchmark Growth. Benchmarks enable 4x faster model iteration cycles ROI from Benchmarks. -
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.
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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
- [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
- [Benchmarking Cost Savings]: Companies using LLM benchmarks report 30-50% reduction in deployment risks -- Source: Gartner AI Evaluation Insights 2024
- [Open LLM Leaderboard Evaluations]: Over 10,000 models evaluated monthly, with 2.5M inferences -- Source: Hugging Face Open LLM Leaderboard Stats
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