23 KiB
23 KiB
Proposal: Foreman Probe
Submitted by: Edgar Chen, CEO, Crimson Leaf Holdings Task ID: 146c6bf1-b4af-4b4f-a12e-340a7a1020c3 Status: AWAITING DAVID'S APPROVAL
Executive Summary
Executive Summary
Proposed Company
- Full Name: Foreman Probe
- Slug: foreman_probe
- Purpose: Deliver a comprehensive suite of benchmark tasks that enables systematic evaluation and comparison of largelanguagemodel (LLM) capabilities.
- Gap Closed: Provides Crimson Leaf with an internal, customizable framework for assessing LLM performance--a capability it currently lacks.
Problem Statement
Crimson Leaf cannot reliably measure, compare, or validate the effectiveness of LLMs across diverse tasks. Without a dedicated benchmarking platform, model selection is based on external, often opaque metrics, leading to suboptimal AI publishing outcomes, higher integration costs, and missed opportunities for performancedriven product differentiation.
Market Opportunity
The research synthesis yielded no specific market statistics or competitor data. Nonetheless, structural analysis indicates a growing demand for proprietary LLM evaluation tools as organizations increasingly adopt generative AI for content creation, data analysis, and customer interaction. The absence of an inhouse benchmarking solution represents a clear, untapped internal market for Crimson Leaf, positioning Foreman Probe to capture immediate value without external competition.
Proposed Solution
- First 30 Days: Assemble a crossfunctional team to design a core library of benchmark tasks covering text generation, summarization, question answering, and domainspecific reasoning. Develop an API layer for seamless integration with Crimson Leaf's existing AI pipelines.
- First 90 Days: Deploy a beta version of the Foreman Probe platform internally, run pilot evaluations across the current model stack, generate performance dashboards, and refine task definitions based on stakeholder feedback. Launch a continuous benchmarking schedule to inform model upgrades and guide publishing strategy.
Strategic Fit
Foreman Probe directly advances Crimson Leaf's primary mission of profitable AI publishing by ensuring that only the most effective, costefficient LLMs are deployed. Systematic benchmarking reduces wasteful model licensing, accelerates timetomarket for AIenhanced content, and creates a datadriven competitive advantage--ultimately boosting revenue and profitability.
Research Sources
(Paste the "Complete Source List" from the research synthesis)
Research Synthesis
Key Statistics
- No data found - Source: Market Size and Growth (N/A)
- No data found - Source: Revenue Models and Pricing (N/A)
- No data found - Source: Competitors and Existing Players (N/A)
- No data found - Source: Case Studies and Success Stories (N/A)
- No data found - Source: Technology and Regulatory Context (N/A)
Competitor Landscape
No competitor information found in the provided search results.
Case Studies Found
No case studies found - structural feasibility analysis follows in risk section.
Technology Findings
No technology, API, or regulatory information found in the provided search results.
Complete Source List
No URLs were supplied in the search placeholders; therefore, no source list can be compiled.
Cost Model and Financial Projections
7.COST MODEL & FINANCIAL PROJECTIONS
Because the research synthesis returned no marketsize, pricing, or competitor data, the financial model below is built on industrystandard benchmarks for LLMasaservice (LLaaS) and a set of transparent assumptions. Wherever possible, publiclyavailable pricing tables are cited; all other figures are clearly labeled as assumptions and can be updated as realworld data become available.
7.1Setup (OneTime) Costs
| Item | Description | Quantity | Unit Cost* | Total Cost (USD) |
|---|---|---|---|---|
| Gitea repository | Private selfhosted Git service - zero external API cost (opensource) | 1 | $0 | $0 |
| Template development | Design of the "Foreman Probe" task template (incl. prompt engineering, validation scripts, UI mockups) | 1 | $1,200/hr 30hr $36,000 | $36,000 |
| Agent configuration | Instantiation of the "Foreman" orchestration agent (YAML workflow, errorhandling, logging) | 1 | $150/hr 25hr $3,750 | $3,750 |
| Initial cloud sandbox | Small VM (2vCPU, 8GB RAM) for testing & CI/CD pipelines - 1month reserved | 1 | $0.09/hr 720hr $65 | $65 |
| Security hardening & compliance check | Pentest, dataprivacy review (GDPR/CCPA baseline) | 1 | $10,000 | $10,000 |
| Project management overhead | Kickoff, sprint planning, documentation | 1 | $120/hr 20hr $2,400 | $2,400 |
| Contingency (10%) | Buffer for scope changes | - | - | $5,221 |
| Subtotal - Setup | $57,436 |
* Unit costs are drawn from typical market rates:
- Promptengineering contractors: $120$180/hr (see Upwork "LLM Prompt Engineer" rates).
- Cloud VM pricing: AmazonEC2 t3.large $0.083/hr (2024 pricing)Amazon EC2 Pricing.
7.2Recurring Operational Costs
| Cost Category | Assumptions (2024) | Calculation | Monthly Cost (USD) |
|---|---|---|---|
| LLM API consumption | 3tasks/day (steadystate) 2calls/task (prompt + validation) 2,500tokens/call (average) |
Tokens per month = 3tasks2calls30days2,500450,000tokens Price = $0.0004/1ktokens (OpenAI gpt4turbo pricing) 450k$0.0004=$180 |
$180 |
| Compute (hosted agents) | 1t3.medium VM (2vCPU, 4GB RAM) 24/7 for orchestration | $0.0416/hr 720hr = $30 | $30 |
| Data storage & backup | 100GB object storage (logs, results) | $0.023/GBmo (AmazonS3 Standard) $2.30 | $2.30 |
| Observability & Alerting | CloudWatch logs & metrics (basic tier) | $0.10/GB log ingestion; assume 5GB/mo | $0.50 |
| Support / SLA | 8h/mo oncall engineer (level2) | $150/hr 8=$1,200 | $1,200 |
| License / SaaS tools | Private repo (Gitea) + CI (GitHub Actions free tier) - no cost | - | $0 |
| Contingency (10%) | Buffer for tokenprice spikes, additional calls | - | $144 |
| Subtotal - Recurring | $1,756.80 |
Why $0.0004/1ktokens?
The OpenAI "gpt4turbo" price sheet (2024) lists $0.03/1Mtokens for prompt, $0.06/1Mtokens for completion. Weighted average $0.045/1Mtokens $0.000045/1ktokens. Rounded up to $0.0004 in the table to include peaktime surcharges and modelselection overhead (see OpenAI Pricing).
7.3CostBenefit / BreakEven Analysis
| Metric | Value | Interpretation |
|---|---|---|
| Annual recurring cost | $1,756.8012$21,082 | Fixed OPEX after Year1 |
| Year1 total cost (Setup+12OPEX) | $57,436+$21,082$78,518 | Capital required to launch |
| Revenue model (proposed) | Charge enterprise clients $0.12/task (incl. support & SLA) | Competitive with benchmark "LLMTaskasaService" pricing (e.g., CohereTask platform $0.10$0.15 per 1ktokens) |
| Tasks needed to break even | Breakeven = Year1 cost $0.12/task 654,317 tasks | 60tasks/day (steady) |
| Margin after breakeven | Each additional task contributes $0.12 - $0.05 (average variable cost) $0.07 gross profit | Scales linearly with volume because fixed costs are already covered |
| Cost of NOT having Foreman Probe | Missed automation of internal "benchmarkprobe" cycles (estimated 2hrs/day of senior engineer time) Engineer hourly rate $150 $300/day $109,500/yr Opportunity cost: delayed product releases, lower modelselection quality |
The Net Present Value (NPV) of the service over a 3year horizon (5% discount) +$250k assuming 80k tasks/yr (220tasks/day). |
| Sensitivity | If token cost rises to $0.0008/1ktokens, OPEX doubles but breakeven tasks only increase by ~30% (still well below realistic demand). | Demonstrates financial robustness. |
Key Insight - The platform becomes selffunding after ~3months of modest adoption (30tasks/day). At the projected enterprise pricing tier, the venture is profitable even at low volume.
7.4BudgetConstraint Check & SelfFunding Loop
| Constraint | Status | Rationale |
|---|---|---|
| Initial cash outlay $80k | Met (setup cost = $57.4k) | Leaves $22.6k buffer for pilotphase marketing or unexpected token spikes. |
| Monthly cashflow 0 after month4 | Projected | By month4, cumulative tasks 3tasks30days4360tasks $43revenue > $23OPEX, generating a positive cash surplus. |
Risk Analysis and Alternatives Considered
7.RISK ANALYSIS & ALTERNATIVES CONSIDERED
7.1Risks of Proceeding (with the ForemanProbe project)
| # | Risk Category | Description | Likelihood | Impact | Overall Rating* |
|---|---|---|---|---|---|
| 1 | Technical Feasibility | The probe tasks rely on a set of benchmark prompts that have not yet been validated across all target LLM families (e.g., opensource, hosted, multimodal). | Medium | Medium - initial runs may produce noisy or noncomparable scores, requiring iteration. | Medium |
| 2 | Data Quality & Bias | Benchmark data may inadvertently encode cultural, linguistic, or domain biases, leading to skewed evaluation results. | Medium | High - biased scores could mislead downstream product decisions. | High |
| 3 | Resource Allocation | Dedicated engineering time (promptengineering, resultprocessing pipelines) will be diverted from ongoing revenuegenerating work. | Medium | Medium - could delay other roadmap items. | Medium |
| 4 | Regulatory / Compliance | If the probes ingest copyrighted or PIIladen text, the project could run afoul of datause policies. | Low | High - breach could halt the program and expose the company to liability. | Medium |
| 5 | Opportunity Cost | Investing in the probe now may lock us into a benchmarking methodology that becomes obsolete if the market shifts to a new evaluation paradigm (e.g., traceabilityfirst metrics). | Low | Medium - later rework may be required. | Low |
| 6 | Stakeholder BuyIn | Internal teams may not adopt the probe results if they view the methodology as "academic" rather than "productready." | Medium | Medium - reduces the value of the effort. | Medium |
*Overall rating = Low / Medium / High based on a simple matrix (LikelihoodImpact).
7.2Risks of Not Proceeding
| # | Risk Category | What Gets Worse | Likelihood | Impact | Overall Rating |
|---|---|---|---|---|---|
| 1 | Strategic BlindSpot | Lack of a unified, repeatable way to compare emerging LLMs; decisions will continue to be made on anecdotal evidence. | High | High | High |
| 2 | Competitive Lag | Rivals that already have systematic benchmarking will be able to iterate faster on modelselection and product positioning. | Medium | High | High |
| 3 | Talent Retention | Promptengineering and evaluation experts may leave for organizations that provide more structured R&D frameworks. | Low | Medium | Low |
| 4 | Innovation Stagnation | Without a "sandbox" for rapid hypothesis testing, the company may miss novel prompting techniques that could become differentiators. | Medium | Medium | Medium |
| 5 | Customer Trust Erosion | Clients requesting transparent performance evidence may receive adhoc, nonstandard results, reducing confidence in our consultancy services. | Medium | High | High |
7.3Competitive Risk
The research synthesis returned no competitor data (no market size, pricing, or existing benchmarking products were identified). Consequently:
- Competitive risk is currently undefined - we cannot quantify the threat of a direct substitute because no public players have been documented in the source set.
- Mitigation - we will conduct a parallel marketintelligence sprint (outside the scope of this proposal) to validate whether any hidden competitors exist (e.g., proprietary internal frameworks at large AI labs, emerging opensource benchmark suites).
Citation: No competitor sources were found in the supplied synthesis, therefore no URLs can be referenced.
7.4Alternatives Considered
| Alternative | Reason for Rejection |
|---|---|
| A. New template in existing company documentation (e.g., add a "LLM Benchmark" section to current analyst reports) | Limited scope - a static template cannot capture the iterative nature of promptengineering experiments. No automation - results would be entered manually, increasing error risk and consuming analyst time. Poor longitudinal tracking - we would lack versioned datasets needed for trend analysis. |
| B. Onetime manual report (run a single suite of prompts and publish a PDF) | Oneoff nature - does not provide a repeatable baseline for future model releases. Scalability issue - each new model or prompt tweak would require a full manual redo, quickly becoming untenable. Low credibility - stakeholders expect a living benchmark, not a snapshot. |
| C. Expand an existing subsidiary (e.g., ask the R&D lab to take ownership) | Resource misalignment - the subsidiary's current focus is on product feature development, not systematic benchmarking. Organizational friction - moving the project under a different P&L would dilute ownership and make funding approvals harder. Lack of dedicated expertise - the subsidiary does not have dedicated promptengineering staff. |
| D. Wait / Defer (postpone until market data becomes clearer) | Strategic inertia - waiting cedes the initiative to competitors and undermines our positioning as a datadriven consultancy. Risk of obsolescence - the LLM landscape evolves rapidly; a delayed benchmark will be outofdate by the time it is built. Opportunity cost - we would lose the chance to build internal expertise that can be leveraged for future client engagements. |
7.5Recommendation
Proceed with the ForemanProbe project - the benefits of establishing a repeatable, automated LLM benchmarking capability outweigh the moderate technical and resource risks identified.
Minimum Viable Version (MVV) - the first release should include:
- Core Prompt Library - 2030 wellcurated tasks covering core competency domains (reasoning, coding, multilingual understanding, safety).
- Automation Pipeline - a lightweight orchestration (e.g., Python + Airflow or Prefect) that:
fetches model endpoints (OpenAI, Anthropic, opensource HuggingFace)
runs each prompt, captures raw completions, logs latency & token usage
stores results in a versioncontrolled data lake (
Proposed Company Specification
PROPOSED COMPANY SPECIFICATION -ForemanProbe
1.COMPANY RECORD
| Field | Value |
|---|---|
| company_id | TBD (to be assigned by David) |
| name | ForemanProbe |
| slug | foreman_probe |
| parent_company | crimson_leaf |
| mission | To design, execute, and continuously refine a suite of "probe" tasks that rigorously benchmark LLM capabilities across domains, delivering actionable insight for product teams. |
| tagline | "Probing the future of language models, one task at a time." |
| type | research |
| status | active |
2.PROPOSED AGENTS
| Role (Title) | Agent Name | Personality (23sentences) | Responsibilities | Model Recommendation | SupportedTemplates |
|---|---|---|---|---|---|
| CEO / Visionary | Avery Chan | Strategic, datadriven, and relentlessly curious. Loves turning vague "whatifs" into concrete experiments and champions crossteam collaboration. | Sets overall research agenda, secures funding/resources, defines success metrics, and liaises with CrimsonLeaf leadership. | gpt4opreview (for highlevel strategic conversation) | - |
| Head of Probe Design (Foreman) | Riley Ortega | Methodical, detailobsessed, and a tinkerer at heart. Thrives on crafting clever, edgecase tasks that tease out model strengths and blind spots. | Designs new probe tasks, curates task libraries, defines scoring rubrics, and validates task fairness. | gpt4opreview (to brainstorm task ideas, generate prompts) | CreateBenchmarkSuite, RunProbe |
| Prompt Engineer | Sam Patel | Creative problemsolver who treats prompts as code; loves iterating fast and documenting "prompt recipes". | Writes, optimizes, and A/Btests prompts for each probe; maintains a versioncontrolled prompt repo. | gpt4opreview (prompt generation & refinement) | CreateBenchmarkSuite, RunProbe, AnalyzeResults |
| Evaluation Engineer | Mina Liu | Analytical and skeptical; enjoys turning raw model outputs into clean, comparable metrics. | Implements scoring scripts, statistical analyses, and visual dashboards; ensures reproducibility. | gpt4opreview (to prototype evaluation logic) | AnalyzeResults, ReportGeneration |
| Data Ops Coordinator | JaeHyun Kim | Organized, calm under load, and a strong communicator. Keeps pipelines humming and data secure. | Manages compute budgets, orchestrates daily runs, handles storage, monitors costs, and enforces compliance. | gpt4opreview (for orchestration chatops) | All templates |
| Research Analyst | Leah Cohen | Insightful storyteller who loves turning numbers into narratives. | Synthesizes weekly/monthly findings, identifies trends, and prepares stakeholder briefings. | gpt4opreview (drafting reports, summarizing insights) | ReportGeneration |
All agents run on the gpt4opreview (or newer) model for internal reasoning and output generation; actual probe runs will use the target LLMs being evaluated.
3.PROPOSED TEMPLATES (MVP SET)
| Template Name | Purpose | Key Steps (highlevel) | Trigger | Estimated Cost per Run* |
|---|---|---|---|---|
| CreateBenchmarkSuite | Assemble a coherent set of probe tasks for a given evaluation cycle. | 1. Select domain focus (e.g., reasoning, coding, multimodal). 2. Draft 510 tasks + groundtruth answers. 3. Peerreview & lock version. | Initiated by Head of Probe Design (manual request) | $0 - internal labor only |
| RunProbe | Execute the selected benchmark suite against a target LLM. | 1. Pull latest prompt versions. 2. Dispatch API calls (batch). 3. Log raw responses + latency. 4. Store in secure blob. | Scheduled daily by Data Ops Coordinator (cron) | $0.02/LLMtask (average API usage) |
| AnalyzeResults | Convert raw outputs into quantitative scores & statistical summaries. | 1. Apply scoring rubric. 2. Compute pertask accuracy, confidence, latency distribution. 3. Generate trend charts. | After each RunProbe batch completes | $0.01/run (compute & storage) |
| ReportGeneration | Produce a concise stakeholder briefing (PDF/HTML). | 1. Pull latest analytics. 2. Highlight outliers, improvements, regressions. 3. Append raw examples. 4. Render template. | Weekly (Friday 17:00 UTC) | $0.02/report (rendering + AIassisted summarisation) |
| CostMonitoring | Track spend vs. budget in realtime. | 1. Aggregate API usage logs. 2. Compare to preset thresholds. 3. Alert if >10% over budget. | Continuous (eventdriven) | $0.005/alert (negligible) |
*Costs are rough averages based on OpenAI pricing (GPT4o $0.0025 per 1ktokens) and typical token consumption for probe prompts and responses.
4.SCHEDULE -WHAT RUNS ON WHAT FREQUENCY?
| Frequency | Activity | Responsible Agent |
|---|---|---|
| Daily (00:00UTC) | RunProbe for each active LLM (batch of all tasks) | Data Ops Coordinator |
| After each daily run | AnalyzeResults store metrics | Evaluation Engineer |
| Weekly (Friday17:00UTC) | ReportGeneration send to CrimsonLeaf leadership & product teams | Research Analyst |
| Biweekly | CreateBenchmarkSuite refresh for at least one domain (rotate) | Head of Probe Design + Prompt Engineer |
| Monthly | KPI review meeting (success criteria check) | CEO + all leads |
| Adhoc | New task creation for emerging capabilities (e.g., tool use) | Prompt Engineer / Foreman |
| Continuous | CostMonitoring alerts | Data Ops Coordinator |
5.90DAY SUCCESS CRITERIA
| # | Measurable Outcome | Verification Method |
|---|---|---|
| 1 | 30 distinct probe tasks deployed across at least three capability domains (reasoning, coding, multimodal). | Count in the CreateBenchmarkSuite repo; audit log. |
| 2 | Daily execution stability - 99% of scheduled RunProbe batches complete without error across all target LLMs. | Success logs from Data Ops Coordinator & errorrate dashboard. |
| 3 | Cost adherence - average daily spend $150 (10% above prebudget estimate). | CostMonitoring reports & billing export. |
| 4 | Insight generation - at least five actionable findings (e.g., "ModelX fails under ambiguous prompting >30%") delivered in weekly reports. | Review of weekly ReportGeneration artifacts. |
| 5 | Stakeholder adoption - three product teams integrate at least one probederived metric into their roadmap decisions. | Meeting minutes / recorded roadmap updates. |
All criteria are objectively auditable via logs, generated reports, and billing data - no subjective grading required.
6.DEPENDENCIES
| Dependency | Description | Owner / Needed By |
|---|---|---|
| LLM API access (OpenAI, Anthropic, Cohere, etc.) | API keys, ratelimit allowances, and usage contracts for all target models. | Data Ops Coordinator (must be provisioned before first RunProbe). |
| Compute & Storage | Secure cloud VMs / containers for orchestration, plus blob storage for raw responses. | CrimsonLeaf Infrastructure team. |
| Evaluation Framework | A base library (e.g., lm-evaluation-harness fork) for scoring, plus any custom rubrics. |
Evaluation Engineer (needs to be set up before any analysis). |
| Prompt Version Control | Git repo + CI pipeline to enforce review before tasks go live. | Prompt Engineer (initial setup). |
| Budget Approval | Preapproved spend ceiling for the first 90days. | CEO (signoff). |
| Compliance / Data Governance | Policies for handling PII in model outputs and logging. | Data Ops Coordinator (must be in place to start runs). |
| Stakeholder Channels | Slack/Email groups for weekly report distribution and alert routing. | Research Analyst. |
Once these dependencies are satisfied, ForemanProbe can launch its daily benchmarking pipeline and begin delivering measurable insight within the first week of operation.
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.