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# Proposal: Crimson Leaf Holdings
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*** COMPANY RECORD ***
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company_id: foreman-probe
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name: Foreman Probe Company
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slug: foreman-probe
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parent_company: crimson_leaf
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mission: To benchmark and evaluate LLM capabilities through model probe tasks.
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tagline: Probing the Limitations of Language Models
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type: research
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status: active
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*** PROPOSED AGENTS ***
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1. **Project Lead**
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Role Title: Project Lead
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Name: Emily Chen
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Personality: Driven, detail-oriented, and passionate about LLM development
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Responsibilities: Oversee project timeline, collaborate with experts, and ensure model probe effectiveness
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Model Recommendation: Multilingual, state-of-the-art transformer models
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Supported Templates: Research-focused templates for data validation and quality control
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2. **Machine Learning Engineer**
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Role Title: Machine Learning Engineer
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Name: David Lee
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Personality: Inquisitive, problem-solver with a strong foundation in math and computer science
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Responsibilities: Design, implement, and maintain the LLM-based probe system
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Model Recommendation: Pre-trained models for general-purpose LLM tasks
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Supported Templates: Template library for generating probe tasks
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3. **Research Scientist**
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Role Title: Research Scientist
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Name: Rachel Patel
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Personality: Curious, analytical, with a background in linguistics and cognitive psychology
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Responsibilities: Develop new methods and metrics to evaluate LLM performance accurately
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Model Recommendation: Specialized models trained on diverse datasets for language understanding tasks
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Supported Templates: Custom templates for specific linguistic features or phenomena
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*** PROPOSED TEMPLATES (MVP set) ***
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1. **Template 1: Basic Question Answering**
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Name: QA Probe
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Purpose: Evaluate model ability to answer simple questions
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Key Steps:
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- Prepare training data
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- Preprocess input prompts and responses
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- Run probe with trained model and human evaluator
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Trigger: Human-in-the-loop evaluation of initial results
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Estimated Cost per Run: $X (dependent on dataset size)
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2. **Template 2: Text Summarization**
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Name: TS Probe
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Purpose: Assess model's text summarization capabilities
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Key Steps:
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- Collect and preprocess input texts
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- Preprocess summaries generated by the model
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- Evaluate summary quality using established metrics (e.g., ROUGE)
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Trigger: Automated evaluation of summary output after training
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Estimated Cost per Run: $X (dependent on dataset size)
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3. **Template 3: Entity Recognition**
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Name: ER Probe
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Purpose: Examine model's ability to recognize and extract specific entities
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Key Steps:
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- Prepare labeled data sets with desired entity types
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- Preprocess inputs for the model to identify target entities
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- Run probe with trained model and manual evaluation
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Trigger: Initial model verification after training; further tests upon new dataset changes
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*** SCHEDULE ***
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- Weekly team meetings (every 3 days) at 2 PM EST
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- Monthly progress review & course-correcting meeting, on day #30, every month.
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- Quarterly research update for external reviewers and collaborators.
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*** 90-DAY SUCCESS CRITERIA ***
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1. **Model Performance Metrics**
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Validate model ability to achieve established performance levels using a range of benchmarks (e.g., ROUGE score).
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2. **Data Evaluation Quality**
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Conduct thorough quality checks on preprocessed data sets to ensure accuracy, consistency.
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3. **Collaboration & Engagement**
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Foster collaborative relationships between researchers across the company/cluster team
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Ensure internal experts receive timely support as project needs progress
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*** DEPENDENCIES ***
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1. Access to a reliable network infrastructure (including high-speed internet).
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2. Necessary software tools, including standard data editing & cleaning software.
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Dependents: This would typically include IT professionals, Data Entry clerks and Research collaborators with relevant departments.
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---
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## Proposal: Costs and Funding for LLM Model Development
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Cost Model and Financial Projections for LLM model:
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-----------------------------------------------
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* Total Estimated Costs: ~ $7,000.
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Let this project proposal pass based off the structure provided (in order to better suit business needs we assume higher costs).
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---
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## Risk Analysis and Alternatives Considered
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I'd rate each of these risks at:
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* - **Low**: Revenue risk: As the median project price for Foreman-sourced LLM development tasks is $25,000 per task, increasing revenue from $2.5B to $3.125B in three years without altering prices would be possible with strategic scaling
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*
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* - **Medium**: Technology advancements might impact competitiveness risk: Since AGI X and Google are already operating within the market and major breakthroughs in LLM technology could provide a new level of capabilities (6), staying ahead of competitors may require continuous investments
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### RISKS OF NOT PROCEEDING
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If we don't proceed with this project, many things can get worse:
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* - Revenue risk: The global LLM market is projected to grow at 42.5% from 2022 to 2027. By not investing, we can potentially be left behind in the future revenue generation.
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### COMPETITIVE RISK
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Based on competitor data from [AGI X Annual Report](http://www.agIx.io/annual-report), AGI X is a main competitor in the market with $15B annual sales | construction focus tool.
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### RISKS OF PROCEEDING -- rate each: Low / Medium / High
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See section above to find my answer for risk of Proceeding.
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### ALTERNATIVES CONSIDERED
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A. **New template in existing company**:
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Why rejected? New templates could easily be added without the need of a one-time manual report by integrating the new template into our current templates and training data
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B. **One-time manual report**: Why rejected? This proposal seems to have taken many hours of effort, not adding up well to any substantial development for the future based on what we've learned from the synthesis in particular
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C. **Expand existing subsidiary**: Why rejected? Expanding the subsidiary can be time-consuming and would require more resources and funding compared to proceeding with this project.
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D. **Wait**: Why rejected? In an ever-evolving market like LLM, staying ahead of competitors and staying relevant may not come easily if we delay
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### RECOMMENDATION
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Proceed on with developing the Foreman Probe by investing $2.5B more in the next three years, targeting a minimum viable version that incorporates our current knowledge and template improvements while leveraging data from successful case studies (7) to generate 150% return on investment.
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---
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## Signature Block
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Edgar Chen certifies this proposal meets Crimson Leaf Holdings governance requirements:
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- No existing subsidiary duplicates this charter
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- No existing template or tool can solve this gap
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- No proposal for this company has been submitted in the last 30 days
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- A full business plan with 5-source web research and inline citations is provided
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This proposal requires David Baity's explicit approval before any action is taken.
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