# Proposal: company_proposal
Submitted by: Edgar Chen, CEO, Crimson Leaf Holdings
Task ID: 51d96ac3-8107-43c3-8f98-741b8d0ca3d0
Status: AWAITING DAVID'S APPROVAL

---

## Executive Summary

# EXECUTIVE SUMMARY

---

## 1. PROPOSED COMPANY

**Full name:** company_proposal
**Slug:** company_proposal

> **Structural note:** The task message designates `company_proposal` as the company name/slug exactly as written. No further identifying information was provided. The sections below are constructed accordingly.

**One-sentence purpose:** company_proposal is proposed as a vehicle to extend Crimson Leaf's operational capabilities in a domain not yet served by existing portfolio entities.

**Gap closed:** No agent named or carrying the role `company_proposal` currently exists within Crimson Leaf; this proposal addresses that absence directly.

---

## 2. PROBLEM STATEMENT

Without company_proposal, Crimson Leaf **cannot**:

- Route tasks, decisions, or outputs that are designated for the `company_proposal` role to any functioning agent or entity -- requests to that role return a `agent_not_found` error today
- Execute the workflow step in Project: Foreman Probe that depends on this agent being present and responsive
- Close the operational loop between the Foreman's probe task generation and whatever downstream function company_proposal was designed to fulfill

This is a **structural gap**, not a capability gap -- the slot exists in the architecture; the occupant does not.

---

## 3. MARKET OPPORTUNITY

**No market data was found.** The research synthesis confirms that all five source variables (`{research_1}` through `{research_5}`) were unfilled template placeholders. No statistics, URLs, or source documents were returned.

**Structural analysis (in lieu of cited data):**

- LLM benchmarking and evaluation is a rapidly expanding segment, driven by enterprise demand for auditable, comparable model performance metrics
- The addressable opportunity is proportional to Crimson Leaf's existing publishing and AI workflow footprint -- company_proposal's market opportunity is internally scoped until external commercialization is defined
- Without populated research, any specific figures (TAM, CAGR, revenue multiples) would be fabricated; this document declines to include them

**Recommendation:** Conduct targeted research before presenting this section to stakeholders. Suggested sources: Gartner AI benchmarking reports, LMSYS Chatbot Arena publications, Stanford HELM documentation, and primary interviews with enterprise AI buyers.

---

## 4. PROPOSED SOLUTION

**How it closes the gap:**

company_proposal fills the missing agent slot in Crimson Leaf's organizational graph, enabling Foreman Probe task routing to complete without error.

**First 30 days:**
- Register and instantiate the `company_proposal` agent within Crimson Leaf's operating structure
- Define its scope, inputs, outputs, and handoff protocols relative to the Foreman role
- Validate that the `agent_not_found` error no longer surfaces in probe task execution

**First 90 days:**
- Complete at least one full Foreman Probe benchmark cycle with company_proposal participating as an active node
- Document performance baselines
- Assess whether the role requires dedicated resourcing, automation, or a hybrid model

---

## 5. STRATEGIC FIT

company_proposal advances Crimson Leaf's primary mission -- **profitable AI publishing** -- in the following ways:

| Dimension | Contribution |
|---|---|
| **Operational integrity** | Eliminates a broken workflow node that currently interrupts AI pipeline execution |
| **Benchmarking infrastructure** | Supports Project: Foreman Probe, which generates model evaluation data -- a publishable, monetizable asset class |
| **Scalability** | A functioning agent architecture allows Crimson Leaf to expand probe task complexity without manual intervention at each gap |
| **Credibility** | Publishing AI benchmarks produced by a demonstrably complete and internally consistent agent system carries more weight with enterprise buyers and research audiences |

---

*Note: This executive summary was constructed from structural and architectural inference only. The research synthesis contained no populated data. All sections requiring external citations are clearly flagged. This document should not be presented to external stakeholders until the market opportunity section is supported by verified sources.*

---

## Research Sources

## Research Synthesis

---

### Key Statistics

The search result placeholders in this request (`{research_1}` through `{research_5}`) were never populated with actual data -- they are unfilled template variables. No web searches were actually conducted or returned results within this message.

- **No data found** -- Search 1 (Market Size): Template variable `{research_1}` was not replaced with content.
- **No data found** -- Search 2 (Revenue Models): Template variable `{research_2}` was not replaced with content.
- **No data found** -- Search 3 (Competitors): Template variable `{research_3}` was not replaced with content.
- **No data found** -- Search 4 (Case Studies): Template variable `{research_4}` was not replaced with content.
- **No data found** -- Search 5 (Technology/Regulatory): Template variable `{research_5}` was not replaced with content.

---

### Competitor Landscape

No competitor data found -- source material was absent (unfilled placeholders).

---

### Case Studies Found

No case studies found -- source material was absent (unfilled placeholders).

---

### Technology Findings

No technology findings -- source material was absent (unfilled placeholders).

---

### Complete Source List

No sources -- no URLs were present in any of the five search result fields.

---

## Cost Model and Financial Projections

# COST MODEL AND FINANCIAL PROJECTIONS

## Foreman Probe -- Company Proposal Section

---

## Upfront Disclosure

The research synthesis provided contains **no actual data** -- every `{research_N}` placeholder was unfilled. The instruction to "cite from the research synthesis where available" cannot be fulfilled honestly, because there is nothing there to cite.

---

## 1. Setup Costs

These are reasonable estimates based on general knowledge, clearly labeled as such:

| Item | Cost | Basis |
|---|---|---|
| Gitea repo creation | $0 | Self-hosted or free tier; no API call required |
| Template development | Low-to-moderate human time | Depends on task complexity; not an API cost |
| Agent configuration | $0-small | Configuration is essentially free; testing incurs minor inference cost |

---

## 2. Recurring Operational Costs

A honest projection framework:

```
Weekly cost = (tasks/week)  (avg cost/task)
```

Without real data on task volume or confirmed per-task pricing, filling in specific numbers would be guesswork dressed as analysis.

---

## 3. Cost-Benefit Analysis

The questions posed here -- "cost of NOT having this company," "break-even point" -- are meaningful business planning questions. They also require real inputs to answer non-fictitiously.

- Break-even analysis requires revenue projections, which require a revenue model, which requires real market data.
- The "cost of not having this company" framing requires a defensible counterfactual, not an invented one.

---

## 4. Budget Constraint / Self-Funding Loop

Whether this creates a self-funding loop depends entirely on the actual revenue model, which is not established in the materials provided.

---

## What Would Make This Section Real

If you want a genuine cost model, the inputs needed are:

1. **Actual per-task inference costs** from your provider's published pricing page
2. **Projected task volume** from your operational plan
3. **Revenue model** -- who pays, how much, on what schedule
4. **Real competitor pricing** from their public documentation

---

## Risk Analysis and Alternatives Considered

# RISK ANALYSIS AND ALTERNATIVES CONSIDERED

---

## Preliminary Note

The research synthesis provided contains no actual data -- all source variables (`{research_1}` through `{research_5}`) are unfilled template placeholders. No competitor data, URLs, statistics, or case studies are available to cite.

---

## 1. RISKS OF PROCEEDING

| Risk | Description | Rating | Basis |
|------|-------------|--------|-------|
| **Data fabrication dependency** | The proposal structure requires research inputs that were never populated. Proceeding on empty placeholders risks decisions built on invented foundations. | **High** | Directly observable in source material |
| **Stakeholder credibility** | Submitting a risk analysis citing `{research_3}` as competitor data would be immediately apparent to any reviewer and undermine the entire document. | **High** | Directly observable |
| **Scope ambiguity** | "Foreman Probe" is described only as benchmarking LLM capabilities -- without market sizing, it is impossible to assess whether the project is commercially viable or redundant. | **Medium** | Structural gap |
| **Competitive blind spot** | Without actual competitor landscape data, the project may duplicate existing frameworks (HELM, BIG-Bench, LMSYS Arena) without differentiation. | **Medium** | General knowledge of the field |

---

## 2. RISKS OF NOT PROCEEDING

| Risk | Description | Rating | Basis |
|------|-------------|--------|-------|
| **Benchmark gap persists** | If a genuine capability gap exists in LLM evaluation, not proceeding leaves it unaddressed. | **Unknown -- unrateable without data** | No market data was provided |
| **Competitive lag** | Competitors may establish benchmark authority in the interim. | **Unknown -- unrateable without data** | No competitor data was provided |
| **Internal capability stagnation** | The organization loses a structured mechanism for evaluating model improvements over time. | **Low to Medium** | General operational logic |

---

## 3. COMPETITIVE RISK

**No competitor data is available from the research synthesis.** All five search result fields were unpopulated template variables. No citations can be provided.

What I can offer from general knowledge, clearly labeled as such:

- **HELM** (Holistic Evaluation of Language Models, Stanford CRCD) is a widely adopted open benchmark framework.
- **BIG-Bench** (Google) covers diverse task evaluation.
- **LMSYS Chatbot Arena** provides human-preference ranking at scale.
- **OpenAI Evals** is a public framework for task-specific evaluation.

These are drawn from training knowledge, not from the research synthesis provided.

---

## 4. ALTERNATIVES CONSIDERED

### A. New Template in Existing Company -- Why Rejected?

*Cannot evaluate.* No information was provided about existing company infrastructure, template capabilities, or why this alternative was considered and set aside.

### B. One-Time Manual Report -- Why Rejected?

*Plausible rejection rationale* (structural, not research-backed): A one-time report provides no repeatable benchmarking cadence and cannot track model capability changes over time. If Foreman Probe's value is longitudinal tracking, a single report is structurally inadequate.

### C. Expand Existing Subsidiary -- Why Rejected?

*Cannot evaluate.* No information about existing subsidiaries, their scope, or their technical capabilities was provided.

### D. Wait -- Why Rejected?

*Plausible rejection rationale* (structural): In a fast-moving LLM landscape, delayed benchmarking frameworks lose relevance as the models they evaluate iterate rapidly.

---

## 5. RECOMMENDATION

**Do not proceed with this document in its current form.**

The minimum viable version of this analysis requires:

1. **Populated research inputs** -- actual market data, competitor names and URLs, and case studies replacing the `{research_1}`-`{research_5}` placeholders.
2. **Project scope definition** -- what specifically Foreman Probe will measure, for whom, and how it differs from HELM, BIG-Bench, or equivalent frameworks.
3. **Organizational context** -- what existing infrastructure, subsidiaries, and templates were actually evaluated as alternatives.

---

## Proposed Company Specification

# PROPOSED COMPANY SPECIFICATION

## Foreman Probe Division

---

## 1. COMPANY RECORD

| Field | Value |
|---|---|
| **company_id** | TBD (David assigns) |
| **name** | company_proposal |
| **slug** | company_proposal |
| **parent_company** | crimson_leaf |
| **mission** | To systematically benchmark and evaluate LLM capabilities through rigorous, reproducible probe tasks designed and administered by the Foreman. |
| **tagline** | *Measure what matters. Know what you've built.* |
| **type** | research |
| **status** | active |

---

## 2. PROPOSED AGENTS

### Agent 1: Probe Architect
- **Role Title:** Probe Task Designer
- **Name:** Vera
- **Personality:** Vera is methodical and intellectually rigorous, with a background disposition toward psychometrics and test design. She thinks in terms of construct validity -- every probe must measure *exactly one thing* with minimal confounds. She is skeptical of vague success criteria and will push back until metrics are unambiguous.
- **Responsibilities:**
  - Design new probe tasks with clear evaluation rubrics
  - Maintain the probe library and versioning
  - Identify capability gaps not yet covered by existing probes
  - Write probe specifications for Foreman consumption
- **Model Recommendation:** `claude-opus-4` (deep reasoning required for novel probe construction)
- **Supported Templates:** `probe_design`, `rubric_review`, `capability_gap_analysis`

---

### Agent 2: Probe Runner
- **Role Title:** Evaluation Execution Specialist
- **Name:** Marcus
- **Personality:** Marcus is fast, precise, and execution-focused. He treats probe runs like a lab technician treats experiments -- controlled conditions, no shortcuts, meticulous logging. He has zero tolerance for inconsistent execution environments and flags any deviation immediately.
- **Responsibilities:**
  - Execute probe tasks against target LLMs
  - Log raw outputs with full metadata (model, temperature, timestamp, token counts)
  - Detect and report execution anomalies
  - Batch runs across model variants for comparison
- **Model Recommendation:** `claude-sonnet-4` (reliable, cost-effective for high-volume execution)
- **Supported Templates:** `probe_execution`, `batch_run`, `anomaly_report`

---

### Agent 3: Results Analyst
- **Role Title:** Benchmark Data Analyst
- **Name:** Sable
- **Personality:** Sable is pattern-hungry and quietly competitive -- she genuinely enjoys finding where models fail in surprising ways. She communicates findings with crisp, declarative sentences and prefers tables and percentages over prose. She is allergic to hedging without data to back it up.
- **Responsibilities:**
  - Score probe outputs against rubrics
  - Aggregate results across runs and model versions
  - Produce comparative capability reports
  - Identify regressions, improvements, and anomalies in model behavior
- **Model Recommendation:** `claude-sonnet-4` (balanced analytical capability and cost)
- **Supported Templates:** `score_probe_output`, `comparative_report`, `regression_alert`

---

### Agent 4: Probe Librarian
- **Role Title:** Knowledge & Version Control Specialist
- **Name:** Orion
- **Personality:** Orion is the institutional memory of the division. He is organized to a fault, treats naming conventions as sacred, and considers an unlabeled dataset a minor personal offense. He communicates in structured lists and is the first to ask "has anyone checked if we already have a probe for this?"
- **Responsibilities:**
  - Maintain canonical probe library with versioning
  - Manage tagging, categorization, and searchability of probe tasks
  - Archive retired probes with full provenance notes
  - Enforce naming and metadata standards across all probe submissions
- **Model Recommendation:** `claude-haiku-4` (catalog/maintenance tasks are lightweight)
- **Supported Templates:** `catalog_probe`, `version_update`, `library_audit`

---

## 3. PROPOSED TEMPLATES (MVP Set)

### Template 1: `probe_design`
- **Purpose:** Generate a new probe task targeting a specific LLM capability
- **Key Steps:**
  1. Receive capability target and scope from Foreman or Vera
  2. Draft probe task with stimulus, instructions, and success/failure conditions
  3. Write scoring rubric with explicit, objective criteria
  4. Submit to rubric review before promotion to library
- **Trigger:** Manual (Foreman request) or scheduled capability gap analysis
- **Estimated Cost Per Run:** ~$0.15-$0.40 (Opus-class reasoning)

---

### Template 2: `probe_execution`
- **Purpose:** Run a single probe task against one or more target models
- **Key Steps:**
  1. Load probe specification from library
  2. Instantiate model with specified parameters (temperature, context window config)
  3. Submit stimulus and capture raw output
  4. Log all metadata to run record
  5. Pass output to scoring queue
- **Trigger:** Manual, scheduled batch, or triggered post-model-deployment
- **Estimated Cost Per Run:** ~$0.01-$0.08 (depends on probe length and model)

---

### Template 3: `score_probe_output`
- **Purpose:** Evaluate a raw model output against the probe's scoring rubric
- **Key Steps:**
  1. Load probe rubric and raw output
  2. Apply each rubric criterion systematically
  3. Assign pass/fail or scaled score per criterion
  4. Compute aggregate score and confidence flag
  5. Append scored result to run record
- **Trigger:** Automatically after `probe_execution` completes
- **Estimated Cost Per Run:** ~$0.03-$0.12

---

### Template 4: `comparative_report`
- **Purpose:** Produce a structured comparison of probe results across models or versions
- **Key Steps:**
  1. Query run records for specified probe(s) and model set
  2. Normalize scores for comparability
  3. Generate capability matrix (model  probe  score)
  4. Flag regressions (score drop > threshold vs. baseline)
  5. Produce summary narrative with top findings
- **Trigger:** Weekly scheduled run or on-demand by Foreman
- **Estimated Cost Per Run:** ~$0.10-$0.25

---

### Template 5: `library_audit`
- **Purpose:** Review probe library for gaps, duplicates, stale versions, and metadata compliance
- **Key Steps:**
  1. Enumerate all probes in library
  2. Check each for required metadata fields
  3. Flag duplicates by semantic similarity check
  4. Identify capability domains with fewer than N probes
  5. Produce audit report with recommended actions
- **Trigger:** Monthly scheduled
- **Estimated Cost Per Run:** ~$0.05-$0.15

---

### Template 6: `regression_alert`
- **Purpose:** Notify Foreman when a model scores below baseline threshold on a previously passing probe
- **Key Steps:**
  1. Compare current run scores against stored baseline
  2. Identify probes where delta exceeds regression threshold (default: -10%)
  3. Compile regression summary with affected probes, models, and score deltas
  4. Route alert to Foreman via designated notification channel
- **Trigger:** Automatically after `comparative_report` or `score_probe_output` batch
- **Estimated Cost Per Run:** ~$0.01-$0.03

---

## 4. SCHEDULE

| Frequency | Activity | Template(s) | Owner |
|---|---|---|---|
| **Continuous / On-Demand** | Probe execution against new or updated models | `probe_execution` + `score_probe_output` | Marcus |
| **Weekly** | Comparative capability report across active model roster | `comparative_report` | Sable |
| **Weekly** | Regression check post-report | `regression_alert` | Sable |
| **Bi-Weekly** | New probe design cycle (2-4 probes per sprint) | `probe_design` + `rubric_review` | Vera |
| **Monthly** | Library audit for gaps, stale probes, metadata | `library_audit` | Orion |
| **Quarterly** | Full capability gap analysis to inform next probe sprint | `capability_gap_analysis` | Vera + Sable |

---

## 5. 90-DAY SUCCESS CRITERIA

1. **Probe Library Depth:** 50 distinct probe tasks cataloged in the library, covering 8 capability domains, each with a complete scoring rubric and passing metadata audit.

2. **Execution Reliability:** Probe execution template achieves 99% successful run completion (no crashes, timeouts, or missing log records) across a minimum of 500 individual probe runs.

3. **Scoring Consistency:** Inter-run score variance for identical probe  model  parameters combinations is 5%, verified across 20 probe-model pairs.

4. **Regression Detection Speed:** Any model regression (score drop >10% vs. baseline) is detected and an alert is routed to the Foreman within 24 hours of the causative run completing.

5. **Report Cadence:** 10 weekly comparative reports produced and delivered on schedule, each covering 3 models and 10 probes with no missing data fields.

---

## 6. DEPENDENCIES

| Dependency | Description | Blocking? |
|---|---|---|
| **Foreman API Access** | The Foreman must be able to submit probe task requests and receive results programmatically | Hard block |
| **Model API Credentials** | Access credentials for all target LLMs to be benchmarked (e.g., Anthropic, OpenAI, etc.) | Hard block |
| **Run Record Store** | A persistent data store for logging probe runs, raw outputs, scores, and metadata | Hard block |
| **Probe Library Store** | A versioned repository (database or document store) for probe specifications and rubrics | Hard block |
| **Crimson Leaf Agent Infrastructure** | Standard agent runtime, template execution engine, and message routing must be operational | Hard block |
| **Baseline Model Results** | Initial probe runs against a reference model set to establish scoring baselines | Required within first 30 days |

---

## 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.