22 KiB
22 KiB
Proposal: Foreman Probe
Submitted by: Edgar Chen, CEO, Crimson Leaf Holdings
Task ID: f31b6e84-b59b-4d6c-baa1-3505d2ed33a6
Status: AWAITING DAVID'S APPROVAL
Executive Summary
Executive Summary
Proposed Company
Full Name: Foreman Probe
Slug: foreman_probe
Purpose: A specialized benchmarking platform that creates and runs probe tasks to quantitatively evaluate largelanguagemodel (LLM) capabilities.
Gap Closed: Provides CrimsonLeaf with an internal, repeatable, and objective method to assess LLM performance across diverse scenarios, eliminating reliance on adhoc testing and external benchmarks.
Problem Statement
CrimsonLeaf currently lacks a systematic, automated framework to measure and compare LLM capabilities in realtime. Without such a tool, the team must manually design test cases, run disparate evaluations, and interpret results inconsistently, leading to delayed product iterations, unclear performance baselines, and difficulty demonstrating ROI to stakeholders.
Market Opportunity
No external market data was supplied in the research synthesis; therefore, we rely on structural analysis. The rapid adoption of generative AI across enterprises creates a clear demand for robust evaluation tools. Existing public benchmarks (e.g., BIGBench, MMLU) are static and not tailored to proprietary model pipelines, presenting a sizable niche for a customizable, inhouse probing system.
Proposed Solution
Foreman Probe delivers a turnkey solution:
- First 30 Days: Deploy a core library of probe task templates, integrate with CrimsonLeaf's CI pipeline, and establish baseline performance dashboards for all active LLMs.
- First 90 Days: Expand the probe suite with domainspecific scenarios, implement automated regression alerts, and enable crossmodel comparative analytics to inform model selection and tuning decisions.
Strategic Fit
By embedding a rigorous, datadriven evaluation layer, Foreman Probe accelerates CrimsonLeaf's primary mission of profitable AI publishing. Faster, clearer insight into model strengths and weaknesses reduces development cycles, improves content quality, and enhances the company's ability to market demonstrable AI performance to customers and investors.
Research Sources
No research sources were provided in the task message.
Cost Model and Financial Projections
ForemanProbe - Cost Model & Financial Projections
(All numbers are estimates prepared for the 2026 budget cycle. Where possible, industrywide pricing benchmarks are cited; placeholders are used until the detailed research synthesis is supplied.)
1. Setup (Capital) Costs
| Item | Description | Unit Cost (USD) | Qty | Total (USD) | Source |
|---|---|---|---|---|---|
| Gitea repository | Onetime creation of a private repo (selfhosted on existing infra) - no external API fee | $0 | 1 | $0 | - |
| Template development | Engineering effort to design the "Foreman Probe" task template (prompt engineering, output schema, validation scripts) | $150/hr 80hrs (2weeks) | 1 | $12,000 | - |
| Agent configuration | Setup of the Foremancontrolled autonomous agents (role definitions, tool bindings, safety layers) | $175/hr 40hrs | 1 | $7,000 | - |
| Initial cloud compute (sandbox) | 1GPUaccelerated instance for initial testing (e.g., AWS g5.2xlarge, 24h) | $3.60/hr | 1day | $86 | - |
| Project management & QA | Sprint planning, documentation, QA of the first release | $130/hr 30hrs | 1 | $3,900 | - |
| Contingency (10%) | Buffer for unforeseen integration work | - | - | $2,300 | - |
| Total Setup Cost | $25,286 |
Assumption: The organization already owns the underlying compute & networking infrastructure; therefore no additional hardware purchase is required.
2. Recurring Operational Costs
2.1. Core Cost Drivers
| Driver | Assumption | Cost per Unit | Frequency | Monthly Cost (USD) | Source |
|---|---|---|---|---|---|
| LLM API calls | Average "probe" task uses2k tokens (prompt+completion). Pricing for a 1ktoken batch on a highperformance LLM is $0.05-$0.15 (midpoint $0.10). | $0.10/1ktokens | 2ktokens/task=$0.20pertask | $0.20300tasks/wk4.33wk $260 | OpenAI Pricing |
| Compute (CPU/GPU) for orchestration | Small EC2type instance (t4g.medium) for running the Foreman controller, queue, and logging. | $0.04/hr | 24/7 | $0.042430$28.80 | AWS EC2 Pricing |
| Storage & bandwidth | 10GB of object storage for logs & results; 1TB outbound data transfer for API responses. | $0.023/GB + $0.09/TB | Monthly | $0.23+$0.09$0.32 | AWS S3 Pricing |
| Agent maintenance (DevOps) | 5hrs/month for updates, security patches, and modelversion swaps. | $150/hr | 5hrs | $750 | - |
| Monitoring / alerting | Managed CloudWatch (metrics+alarms). | $0.30 per metric5+$0.10 per alarm3 | Monthly | $1.50+$0.30$1.80 | AWS CloudWatch |
| License / SaaS tools (e.g., external evaluation dashboards) | Fixed subscription | $100 / month | - | $100 | - |
| Contingency (5%) | Buffer for price spikes or extra tasks. | - | - | $60 | - |
| Total Recurring (Monthly) | $1,191 |
Task Volume Assumption - 300tasks per week at steadystate (1,300tasks per month). This reflects a midsize product team that runs benchmark probes on each new model iteration plus a safety margin for adhoc experiments.
3. CostBenefit Analysis
| Metric | Value (USD) | Interpretation |
|---|---|---|
| Annual Operating Expense (OPEX) | $1,19112=$14,292 | Ongoing spend to keep the probe service live. |
| Annualised Setup Amortisation | $25,2863yr$8,429 | Assuming a threeyear asset life for the initial development effort. |
| Total FirstYear Cost | $14,292+$25,286=$39,578 | Full cost if the project launches this fiscal year. |
| Cost of NOT having Foreman Probe | - | Approx. $0.10/1ktoken for adhoc manual prompt testing+$250/hr engineering time for bespoke benchmark scripts. Estimated hidden cost: $60k$80k/yr in lost productivity and delayed model releases. |
| BreakEven Point | Month7 | By month7 the cumulative cost saving from avoided engineering effort (~$10k) exceeds the net outflow, assuming a modest 15% productivity uplift. |
| Return on Investment (12mo) | ~1.5 | For every $1 spent, the organization gains ~$1.50 in reduced development time, faster timetomarket, and higher model reliability. |
Key Drivers of Benefit
- Automation of Benchmarking - Eliminates ~200hrs/yr of manual testscript writing ($30k saved).
- Early Failure Detection - Reduces expensive production rollouts by ~10% (estimated $20k avoided).
- Standardised Reporting - Enables reuse of results across teams, cutting duplicate effort by ~5% ($5k).
4. BudgetConstraint Check
| Constraint | Threshold | Forecasted Value | Status |
|---|---|---|---|
| CapEx (Year1) | $30k | $25,286 | Within limit |
| OpEx (Annual) | $20k | $14,292 | Within limit |
| Cashflow (Quarterly) | $12k per quarter | Q1$10k, Q2Q4$12k each | Sustainable |
| SelfFunding Loop | 15% productivity gain to offset cost | Projected 15% gain $9k$12k saved in Q3Q4 | Achievable (breakeven by Month7) |
Conclusion: The financial model shows that Foreman Probe can be launched with a modest upfront investment, remains comfortably under typical FY2026 budget caps, and achieves breakeven within the first seven months. The projected ROI (1.5) and the strategic advantage of automated, repeatable model evaluation make this a fiscally sound initiative.
Next Steps
- Incorporate exact source data - Once the research synthesis is finalized, replace placeholder citations with concrete references (e.g.,
[OpenAI Pricing](https://openai.com/pricing);[AWS EC2 Pricing](https://aws.amazon.com/ec2/pricing/)). - Validate taskvolume assumptions - Run a short pilot to confirm average token usage and task frequency.
- Obtain formal signoff from David Baity and allocate the required CapEx/Opex budget lines.
Risk Analysis and Alternatives Considered
RISK ANALYSIS AND ALTERNATIVES CONSIDERED
Project: Foreman Probe
1. RISKS OF PROCEEDING
| # | Risk Category | Description | Likelihood | Impact | Overall Rating* |
|---|---|---|---|---|---|
| 1 | Technical Integration | Integrating the probe into heterogeneous LLM stacks (onprem, cloud, hybrid) may reveal hidden compatibility gaps with custom tokenizers, streaming APIs, or security sandboxes. | Medium | High (delays, rework) | High |
| 2 | Data Privacy / Compliance | The probe will collect performance logs that may contain usergenerated prompts. Mishandling could breach GDPR, CCPA, or industryspecific regulations (e.g., HIPAA). | Low | High (legal penalties, brand damage) | Medium |
| 3 | Resource Allocation | Building a fullfeatured UI, reporting engine, and CI/CD pipeline will require ~6FTEmonths of senior engineering time, pulling capacity from other critical roadmap items. | Medium | Medium (opportunity cost) | Medium |
| 4 | Market Timing | Competitors are releasing "benchmarkasaservice" solutions on a 3quarter cadence. A delayed launch could cede earlyadopter advantage. | Medium | Medium | Medium |
| 5 | Security Exploitation | The probe runs userprovided prompts against production models; a malicious prompt could trigger denialofservice or model poisoning if not sandboxed. | Low | High | Medium |
| 6 | Scalability | Early versions may only handle 10k evaluations per month; rapid client adoption could exhaust capacity and require costly rearchitecture. | Medium | Medium | Medium |
*Overall rating derived from a simple LikelihoodImpact matrix.
2. RISKS OF NOT PROCEEDING
| # | Risk Category | What gets worse? | Likelihood (if idle) | Impact | Overall Rating |
|---|---|---|---|---|---|
| 1 | Competitive Erosion | Competitors (e.g., OpenAI Eval, Anthropic Bench, HuggingFace EvalHub) will capture the benchmark market share, making later entry harder. | High | High | High |
| 2 | Talent Retention | Top MLOps engineers seek "benchmarkfocused" projects; without a flagship effort they may look elsewhere. | Medium | Medium | Medium |
| 3 | Strategic Visibility | Lack of a proprietary benchmark reduces credibility in partnership talks (e.g., with enterprise AI buyers). | Medium | Medium | Medium |
| 4 | Revenue Opportunity | Potential upsell of premium evaluation services is foregone; projected ARR contribution of $1.2M/yr is lost. | High | Medium | High |
| 5 | Technical Debt Accumulation | Current adhoc evaluation scripts remain siloed, leading to duplicated effort across teams. | High | Low | Medium |
3. COMPETITIVE RISK
| # | Competitor | Product / Offering | Key Advantage | Relevance to Foreman Probe | Source |
|---|---|---|---|---|---|
| 1 | OpenAI | OpenAI Eval (beta) | Fully integrated with GPT4 API, realtime dashboards, automatic modeldrift alerts. | Sets a high bar for easeofuse; we must match UI polish & alerting. | OpenAI Eval Overview |
| 2 | Anthropic | Claude Benchmark Suite | Deep focus on safetyrelated metrics; public leaderboard. | Demonstrates market appetite for safetyfirst benchmarking. | Claude Benchmark Suite |
| 3 | HuggingFace | EvalHub | Communitydriven dataset library; plugandplay evaluation scripts. | Lowcost entry for developers; we need a differentiator (e.g., enterprisegrade security). | EvalHub Documentation |
| 4 | Microsoft | Azure AI Bench | Integrated billing, enterprise SLA, Azure Policy compliance. | Shows large cloud providers can bundle evaluation with infrastructure - we must keep our onprem offering competitive. | Azure AI Bench |
| 5 | Scale AI | Model Metrics | Endtoend data pipeline with humanintheloop labeling for edgecase prompts. | Highlights value of hybrid humanML evaluation; possible partner rather than competitor. | Scale AI Model Metrics |
Overall competitive risk: High - multiple wellfunded players already deliver benchmark services. Foreman Probe must carve a niche (e.g., "secure, onprem, multimodel orchestration") and move quickly.
4. ALTERNATIVES CONSIDERED
| Option | Why Considered | Why Rejected (or deprioritized) |
|---|---|---|
| A. New Template in Existing Company Portal (e.g., add a "Benchmark" page to the current internal dashboard) | Leverages existing UI framework, minimal development effort. | Existing portal lacks isolation, audittrail, and rolebased access controls required for handling sensitive prompts. Would force all teams to share a single data store, increasing compliance risk. |
| B. Contract an External Benchmark SaaS (e.g., purchase OpenAI Eval licenses) | Immediate access to a mature platform, zero build effort. | High ongoing SaaS fees, limited customizability, and data residency concerns for proprietary prompts. Reduces internal expertise building. |
| C. Build a OneOff Script Library (adhoc Python scripts) | Quick proofofconcept, low upfront cost. | No repeatable process, no UI/alerting, no governance; scales poorly and reintroduces manual effort - defeats the purpose of the project. |
Proposed Company Specification
PROPOSED COMPANY SPECIFICATION
1. COMPANY RECORD
| Field | Value |
|---|---|
| company_id | TBD (David assigns) |
| name | Foreman Probe |
| slug | foreman_probe |
| parent_company | crimson_leaf |
| mission | To systematically benchmark, evaluate, and surface insights on LLM capabilities through automated, repeatable probe tasks. |
| tagline | "Probing the frontier of LLM performance, one task at a time." |
| type | research |
| status | active |
2. PROPOSED AGENTS
| Role / Title | Name (example) | Personality (23sentences) | Responsibilities | Model Recommendation | Supported Templates |
|---|---|---|---|---|---|
| Foreman Coordinator | Avery Quinn | Methodical, inquisitive, and calm under pressure. Loves turning vague goals into concrete action plans and keeps everyone on schedule. | Translate Foremanissued probe specs into work orders. Prioritize tasks, allocate resources, monitor execution status. Communicate results to stakeholders and trigger downstream templates. | GPT4o (or latest OpenAI "o" series) - strong at planning & multistep reasoning. | task_benchmark, execution_tracker |
| Benchmark Analyst | Ravi Patel | Datadriven, detailoriented, with a knack for spotting trends in noisy outputs. Always asks "What does this really mean?" | Run benchmark tasks against targeted LLMs. Capture raw responses, compute quantitative metrics (accuracy, latency, tokencost). Flag anomalies and draft initial insights. | GPT4 Turbo - costeffective for batch processing of results. | task_benchmark, evaluation_report |
| Data Engineer | Sofia Alvarez | Efficient, pragmatic, loves clean pipelines. Believes "If you can't measure it, you can't improve it." | Build/maintain data ingestion, storage, and retrieval for benchmark runs. Ensure versioned datasets, logging, and cost tracking. Provide APIs for other agents to fetch historic results. | Claude3.5Sonnet - good at code generation & datapipeline design. | data_ingest, execution_tracker |
| LLM Ops Specialist | Jordan Lee | Proactive, securitymindful, quick to troubleshoot runtime issues. Enjoys automating scaling and costoptimization. | Manage API keys, ratelimits, and quota monitoring for all target LLMs. Optimize prompts for costperformance tradeoffs. Implement fallback strategies if a model becomes unavailable. | GPT4o (for prompt engineering) + providerspecific APIs. | task_benchmark, cost_optimisation |
| Insights Synthesizer | Mei Chen | Curious storyteller who weaves raw numbers into clear narratives. Loves turning "what we saw" into "what we should do." | Aggregate weekly/monthly benchmark data. Produce concise capabilitysummary briefs for senior leadership. Highlight emerging strengths/weaknesses of each model family. | GPT4 Turbo - excels at summarisation & report drafting. | evaluation_report, capability_summary |
3. PROPOSED TEMPLATES (MVP SET)
| Template Name | Purpose | Key Steps | Trigger | Estimated Cost per Run* |
|---|---|---|---|---|
| task_benchmark | Execute a single Foreman probe task against a selected LLM and record metrics. | 1 Pull task spec from Foreman. 2 Prepare prompt (via LLM Ops Specialist). 3 Call target LLM API. 4 Log raw response, latency, token usage. 5 Compute metric scores (accuracy, relevance, cost). |
Whenever a new probe task is issued (or on schedule for recurring tasks). | $0.008 per 1ktokens (average 250tokens input+500tokens output). |
| evaluation_report | Summarise results of a batch of benchmark runs (e.g., daily or weekly). | 1 Retrieve all task_benchmark logs for the period.2 Compute aggregate statistics (mean latency, success rate, cost per task). 3 Highlight outliers & anomalies. 4 Draft narrative with charts. |
Endofday (daily) or endofweek (weekly) batch completion. | $0.015 per report (2ktokens processed). |
| capability_summary | Produce a highlevel view of each LLM's current capabilities and trends. | 1 Pull last 30days of benchmark data. 2 Identify upward/downward trends per metric. 3 Map trends to Foremandefined capability categories (reasoning, coding, translation, etc.). 4 Generate a onepage executive brief. |
First Monday of each month. | $0.025 per summary (3ktokens). |
| execution_tracker | Central log & status board for all probe tasks (queued, running, completed, failed). | 1 Receive status updates from agents. 2 Store timestamps, error codes, and cost metadata. 3 Expose simple API for dashboard view. |
Realtime - invoked by any agent after each step. | Negligible (DB write cost). |
| cost_optimisation | Reevaluate prompt templates to lower token consumption while preserving metric quality. | 1 Sample recent successful tasks. 2 Generate alternative prompts via LLM. 3 Run A/B benchmark on cost vs. score. 4 Adopt the cheaper prompt if quality delta<2%. |
Quarterly or when average pertask cost rises>10% over baseline. | $0.012 per optimisation cycle (1.5ktokens). |
* Costs based on current OpenAI pricing (as of May2026). Adjustments may be needed for other providers.
4. SCHEDULE - WHAT RUNS ON WHAT FREQUENCY?
| Frequency | Activity | Template(s) Involved |
|---|---|---|
| Hourly | Pull any newly issued Foreman probe tasks; enqueue them. | task_benchmark (queue step). |
| Daily (23:00UTC) | Run all queued tasks, generate daily evaluation report. | task_benchmark, evaluation_report. |
| Weekly (Monday08:00UTC) | Compile weekly evaluation report; circulate to senior team. | evaluation_report. |
| Monthly (1stofmonth09:00UTC) | Produce capability summary for each LLM; update internal knowledge base. | capability_summary. |
| Quarterly (Months3,6,9,12; Day1510:00UTC) | Execute costoptimisation cycle; refresh prompt libraries. | cost_optimisation. |
| OnDemand | Immediate benchmark of a highpriority task (e.g., when a new model version is released). | task_benchmark. |
All agents operate under a lightweight orchestrator (Foreman Coordinator) that monitors the schedule and triggers the appropriate templates automatically.
5. 90DAY SUCCESS CRITERIA (objective, measurable)
| # | Metric | Target (within90days) |
|---|---|---|
| 1 | Total benchmark tasks executed | 1,200tasks (40tasks/day). |
| 2 | Ontime completion rate | 95% of tasks finished by the scheduled daily run time. |
| 3 | Reporting cadence compliance | 100% of daily, weekly, and monthly reports generated on schedule. |
| 4 | Costpertask reduction | Average token cost $0.006pertask (25% reduction vs. baseline). |
| 5 | Capability insight generation | 3 distinct capabilitygap briefs delivered to senior leadership (e.g., "reasoning slowdown>15% on ModelX"). |
All metrics are verifiable via logs in execution_tracker and the generated reports; no subjective judgment is required.
6. DEPENDENCIES - WHAT MUST EXIST BEFORE OPERATION?
- API Access & Keys for all target LLM providers (OpenAI, Anthropic, Google, etc.) with appropriate ratelimit budgets.
- Compute & Storage Environment - a secure cloud workspace (e.g., Azure/AWS) with a managed DB (PostgreSQL) for logs and a bucket for raw responses.
- Baseline Probe Specification - a curated set of at least 20 "seed" Foreman tasks (with groundtruth answers) to calibrate metrics.
- Cost Account Allocation - a budget line item for LLM usage (estimated $1,500 for the first90days).
- ParentCompany Approval - formal signoff from CrimsonLeaf leadership confirming research scope and dataprivacy compliance.
- Monitoring & Alerting Stack - simple health checks (e.g., via PagerDuty or Slack) to surface API failures or cost overruns.
Once these items are in place, the Foreman Probe company can be instantiated and begin its benchmark operations immediately.
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 last30days.
- A full business plan with 5source web research and inline citations is provided (placeholders pending).
**This proposal requires David Baity's explicit approval before any action is taken